The Internet of Things (IoT) is increasingly evolving into a more intelligent and environmentally sustainable ecosystem. However, as IoT systems proliferate, they face persistent challenges, including limited spectrum and energy resources. These challenges can be mitigated by integrating cognitive radio (CR) technology and energy harvesting (EH) techniques. CR enables wireless devices to increase their spectral resources by opportunistically utilizing licensed spectrum bands, while EH allows energy-constrained devices to become self-sustaining and extend their operational lifetime. The increasing diversity of wireless devices and data, combined with rapidly changing wireless environments and unpredictable energy availability, presents significant challenges. As a result, traditional optimization-based approaches often fall short. The advent of artificial intelligence (AI) offers a promising alternative, enabling low-power wireless (LPW) networks to become self-managing, self-sustaining, and adaptive to changing conditions through data-driven methods. However, existing surveys rarely address cognitive LPW networks in depth and often overlook the role of AI in enabling EH and smart operations. This survey fills that gap by reviewing state-of-the-art AI approaches that address key challenges in EH-enabled cognitive LPW systems, including power allocation, resource optimization, and task scheduling. We review key advancements in cognitive LPW systems, such as IoT, backscattering networks, multi-hop and relay-assisted networks, and multiple access networks. Finally, we highlight key insights from the literature and outline promising directions for future research on cognitive LPW systems that enable smart, green, scalable, and secure deployments.

The metaverse transforms how individuals interact, work, and engage in virtual environments, creating new opportunities in gaming, education, e-commerce, and social networking. At the core of this experience are avatars (i.e., digital representations of users that serve as their proxies in virtual spaces). Ensuring secure and privacy-preserving communication between avatars presents critical challenges, particularly in protecting metaverse user privacy by preventing the linking of avatars to users' social and professional lives. Adopting anonymous schemes such as ring signature schemes requires access to all public keys within the anonymity set, which is impractical in the metaverse. In this paper, we proposeSPARTA(Secure andPrivacy-preserving protocol withRole separation andTrustworthiness forAvatars in the metaverse), a protocol that enables avatar authentication and maintains avatar unlinkability. By leveraging mercurial signatures, our approach allows metaverse users to generate multiple unlinkable avatars without requiring repeated registration with the metaverse service provider, thereby enabling seamless role separation. Additionally, by using a time-based hash chain, only avatars in possession of a reputation token from the time-based hash chain can submit their feedback on a smart contract based on their interactions. Given the soundness property of zero-knowledge proof and the origin-hiding property of mercurial signatures, we formally prove that${\sf SPARTA}$achieves mutual authentication, avatar unlinkability, and penalization enforcement. Additionally, we analyze the performance overheads introduced by its cryptographic primitives and compare${\sf SPARTA}$with existing metaverse authentication protocols. Furthermore, we implement the protocol using socket programming. This implementation simulates real-time message exchanges between protocol entities, resulting in an end-to-end latency of 105 ms. Compared to existing metaverse authentication frameworks,${\sf SPARTA}$provides unlinkable avatar authentication that achieves mutual authentication, role separation, and data sovereignty without reliance on an online trusted third party. The concurrent implementation between two Raspberry Pi devices demonstrates the scalability of${\sf SPARTA}$, achieving a total completion time of 17.338 seconds for 1000 concurrent authentications, corresponding to a throughput of approximately 57.7 authentications per second, confirming its practicality for large-scale metaverse environments.

Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these logs as connected entities and events, revealing relationships that are missed by linear log representations. Existing systems apply anomaly detection to these graphs but often suffer from high false positive rates and coarse-grained alerts. Their reliance on node attributes like file paths or IPs leads to spurious correlations, reducing detection robustness and reliability. To fully understand an attack's progression and impact, security analysts need systems that can generate accurate, human-like narratives of the entire attack. To address these challenges, we introduce OCR-APT, a system for APT detection and reconstruction of human-like attack stories. OCR-APT uses Graph Neural Networks (GNNs) for subgraph anomaly detection, learning behavior patterns around nodes rather than fragile attributes such as file paths or IPs. This approach leads to a more robust anomaly detection. It then iterates over detected subgraphs using Large Language Models (LLMs) to reconstruct multi-stage attack stories. Each stage is validated before proceeding, reducing hallucinations and ensuring an interpretable final report. Our evaluations on the DARPA TC3, OpTC, and NODLINK datasets show that OCR-APT outperforms state-of-the-art systems in both detection accuracy and alert interpretability. Moreover, OCR-APT reconstructs human-like reports that comprehensively capture the attack story.

Collaboration among various Internet of Things (IoT) application service providers has the potential to improve service quality and productivity. However, challenges arise from the complexity of interoperability, particularly in Authorization and Authentication (A&A) for devices across different domains. This paper presents a scheme designed to facilitate interdomain A&A. The proposed approach uses the BBS group signature scheme to ensure anonymous authentication between domains and introduces a privacy-preserving authorization scheme that enables domain managers to grant permissions to authenticated domains for accessing their devices. The proposed A&A does not require an online trusted third party and supports a two-level revocation mechanism: a domain can be revoked from the entire system and no longer authenticated, or an authorizing domain can revoke its permission for another domain, preventing further communication between them. Additionally, a tracing mechanism is developed, leveraging a threshold-based cryptosystem, to reveal the identity of anonymized malicious devices while preventing unauthorized tracing by a single authority. The proposed protocol ensures strong exculpability, guaranteeing that no entity, including trusted entities, can impersonate a domain server. We formally analyze the security of the proposed authorization scheme and report on the protocol’s computational and communication overheads. Finally, the proposed A&A is compared to related protocols in terms of efficiency and security guarantees.

The metaverse is a virtual world that mirrors real life, allowing users to engage in activities and access services without the constraints of time and space. In the metaverse, users can create one or more avatars that reflect their personal preferences, enabling them to participate in activities that match their tastes and needs. To protect users’ freedom and anonymity, it is imperative for metaverse platforms to support the creation ofunlinkableavatars. This ensures that the different avatars a user creates cannot be connected, keeping their virtual identities separate and reducing the risk of retaliation for their actions. In this paper, we propose an unlinkable avatar authentication scheme, UAVA, which leverages cryptographic group signatures to enable metaverse users to create and certify their avatars without interaction with service providers. These certified avatars can then be anonymously authenticated, ensuring unlinkability between multiple avatars belonging to the same user. UAVA maintains anonymity between users and their avatars, while allowing service providers to trace malicious avatars back to their users. We formally define and prove the security properties of UAVA, and implement the protocol using socket programming, and report on its cryptographic overheads. We also evaluate its cryptographic overhead and compare it to related protocols in terms of efficiency, security, and scalability.

Vehicular Ad Hoc Networks (VANETs) empower vehicles equipped with onboard units to exchange traffic-related messages, enhancing vehicle navigation safety and efficiency. Providing secure privacy-preserving authentication schemes for VANETs is indispensable. It ensures that only legitimate vehicles can communicate, preventing external adversaries from injecting falsifiable information that could mislead vehicles, cause accidents, or disrupt traffic flow. Simultaneously, the privacy-preserving features prevent curious adversaries from compromising vehicle privacy and tracking users. Secure centralized vehicular communication protocols, where a single entity issues certificates for all vehicles, face challenges in enabling cross-domain communications. Adoption of such centralized protocols necessitates that vehicles within each domain possess their certificate authority, restricting cross-domain communication due to inherent distrust in the certificate authorities of other domains. In this paper, we propose a Conditional Privacy-preserving Message Authentication protocol for VANET Emergency message exchange (CP-MAVE), designed to ensure message authentication, integrity, and anonymity of vehicles across different domains. In the event of misbehavior, distributed key generation centers collaborate to trace back the identity of the vehicle. To evaluate the security of our protocol, we formally prove the existential unforgeability of CP-MAVE against chosen message attacks based on the intractability of the elliptic curve discrete logarithm problem. Additionally, we demonstrate that CP-MAVE achieves message authentication, conditional privacy preservation, and resilience against replay and modification attacks. Moreover, we model and analayze CP-MAVE using the Tamarin prover and show that CP-MAVE maintains the secrecy and the message authentication of the vehicle traffic messages. Furthermore, we evaluate CP-MAVE’s performance regarding communication overhead and computation complexity. On a Raspberry Pi 4 Model B/8GB, equipped with a 1.5 GHz 64-bit Quad-core ARM Cortex-A72 processor, CP-MAVE requires a 304-byte communication overhead and 9.4897 msec as cryptographic operation overhead. Finally, to simulate the flow of messages between entities in our protocol, we implement CP-MAVE using socket programming, resulting in an end-to-end delay of 111.05 msec.

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.

The electrification of the transportation system is double-edged for the smart grid. Although it is green and eco-friendly, uncontrolled charging of electric vehicles (EVs) could cause not only distribution network congestion but also long queues at the charging stations. Hence, it is necessary to ensure existing charging resources are efficiently utilized. The main objective of this article is to develop an algorithm that assigns EVs to charge stations such that distribution network congestion and time spent by the user from requesting a charging service to accessing it is minimized. The Lyapunov function is utilized for developing an EV assignment algorithm to manage a dynamic population of EVs ensuring queuing stability. Moreover, as the EV assignment algorithm relies on the communication network, an intrusion cyber-attack can occur resulting in an unstable queuing system. An intrusion detection technique is, hence, proposed which utilizes existing transportation network sensor data with the EV charging stations operator information to detect such attacks. A case study using the IEEE 69 bus system is then developed to test the proposed framework.

The IoT-edge-cloud paradigm enables resource-constrained IoT devices to offload their computation, thereby meeting the required quality-of-service for real-time applications. However, the deployment of IoT devices in public places, such as smart cities, exposes them to various security threats, including physical attacks. To address these security concerns, we propose a physical unclonable function (PUF)-based IoT-edge-cloud symmetric key authentication protocol with forward secrecy (SKAFS), which ensures the anonymity of transacting IoT devices, resilience to desynchronization-based denial-of-service attacks, and PUF modeling attacks. To evaluate the security of our protocol, we conduct a formal security analysis using the automated AVISPA tool. In addition, based on the indistinguishability property of the PUF, we formally prove that SKAFS is secure under the Canetti-Krawczyk-adversary model. Moreover, we implement the protocol using socket programming between a Raspberry Pi 1 as an IoT device, a Raspberry Pi 4 as an IoT gateway, and an 11th Gen Intel Core i7–11800H laptop as the cloud admin to simulate the message flow between the protocol entities in a real-time experiment and calculate its end-to-end latency. Finally, we compare SKAFS with other PUF-based protocols in terms of computation time, communication cost, and storage requirements.

The three-tier IoT–Edge–Cloud paradigm enables low-end devices to use the computation capabilities of the more powerful edge nodes to meet efficiency constraints for real-time applications. Many symmetric-key-based schemes rely on an online trusted cloud admin (CA) to establish session keys between IoT devices and edge nodes. In this study, we propose a new provably-secure mutual authentication privacy-preserving protocol with forward secrecy (MAPFS), which eliminates the requirement for an online CA during IoT authentication. To achieve anonymity, our construction utilizes zero-knowledge proofs and randomizes the IoT authentication request. The security of our construction is based on the well-studied discrete logarithm and decisional Diffie–Hellman assumptions in elliptic curve groups. We formally prove that MAPFS ensures mutual authentication and semantic security for session keys. We also evaluate MAPFS performance in terms of the communication overhead, storage requirements, and computation complexity. Finally, we test the performance of MAPFS on a Raspberry Pi 4 and compare it against other certificate-less protocols.

A diverse set of Internet of Things (IoT) devices are becoming an integrated part of daily lives, and playing an increasingly vital role in various industry, enterprise and agricultural settings. The current IoT ecosystem relies on several IoT management platforms to manage and operate a large number of IoT devices, their data, and their connectivity. Considering their key role, these platforms must be properly secured against cyber attacks. In this work, we first explore the core operations/features of leading platforms to design a framework to perform a systematic security evaluation of these platforms. Subsequently, we use our framework to analyze a representative set of 52 IoT management platforms, including 42 web-hosted and ten locally deployable platforms. We discover a number of high-severity unauthorized access vulnerabilities in 9/52 evaluated IoT management platforms, which could be abused to perform attacks, such as remote IoT SIM deactivation, IoT SIM overcharging, and IoT device data forgery. More seriously, we also uncover instances of broken authentication in 13/52 platforms, including complete account takeover on 8/52 platforms along with remote code execution on 2/52 platforms. In effect, 17/52 platforms were affected by vulnerabilities that could lead to platform-wide attacks. Overall, vulnerabilities were uncovered in 33 platforms, out of which 28 platforms responded to our responsible disclosure. We were also assigned 11 common vulnerabilities and exposures and awarded bounty for our findings.

The stealthy and persistent nature of Advanced Persistent Threats (APTs) makes them one of the most challenging cyber threats to uncover. Several systems adopted the development of provenance-graph-based security solutions to capture this persistent nature. Provenance graphs (PGs) represent system audit logs by connecting system entities using causal relations and information flows. Hunting APTs demands the processing of ever-growing large-scale PGs of audit logs for a wide range of activities over months or years, i.e., multi-terabyte graphs. Existing APT hunting systems are typically memory-based, which suffers colossal memory consumption, or disk-based, which suffers from performance hits. Therefore, these systems are hard to scale in terms of graph size or time performance. In this paper, we propose MEGR-APT, a scalable APT hunting system to discover suspicious subgraphs matching an attack scenario (query graph) published in Cyber Threat Intelligence (CTI) reports. MEGR-APT hunts APTs in a twofold process: (i) memory-efficient extraction of suspicious subgraphs as search queries over a graph database, and (ii) fast subgraph matching based on graph neural network (GNN) and our effective attack representation learning. We compared MEGR-APT with state-of-the-art (SOTA) APT systems using popular APT benchmarks, such as DARPA TC3 and OpTC. We also tested it using a real enterprise dataset. MEGR-APT achieves an order of magnitude reduction in memory consumption while achieving comparable performance to SOTA in terms of time and accuracy.

Industrial advancements in information and communications technology facilitated the widespread use of line current differential relays (LCDRs) for protecting critical transmission lines due to their fast, sensitive, selective, and secure performance. Despite their advantages, LCDRs' reliance on vulnerable communication networks to swap current measurements makes them vulnerable to cyberattacks. In this article, a scheme is proposed to protect LCDRs from direct-false-tripping (DFT), fault-masking (FM), and sympathetic-tripping (ST) cyberattacks, which have not been studied together before for transmission-level LCDRs. The proposed scheme utilizes a deep neural network (DNN), trained offline on features extracted from only the measurements available for LCDRs. The trained DNN model can then be implemented within LCDRs. Unlike the previous solutions, which only differentiate between faults and DFT cyberattacks, the proposed scheme actively differentiates between authentic and manipulated LCDR measurements to detect and mitigate possible cyberattacks. The performance of the proposed scheme is evaluated using the IEEE 39-bus benchmark system. Our results show that the proposed scheme can accurately detect different forms of DFT, ST, and FM cyberattacks while maintaining the LCDR's protective characteristics. The proposed scheme is tested for real-time capability using an OPAL-RT simulator.

JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications. Because of its popularity, JavaScript has become a frequent target for attackers who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing studies in vulnerable code detection in JavaScript mostly consider package-level vulnerability tracking and measurements. However, such package-level analysis is largely imprecise, as real-world services that include a vulnerable package may not use the vulnerable functions in the package. Moreover, even the inclusion of a vulnerable function may not lead to a security problem if the function cannot be triggered with exploitable inputs. In this article, we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world JavaScript projects, combined with a static multi-file taint analysis mechanism to further assess the impact of the vulnerabilities on the whole project (i.e., whether the vulnerability can be exploited in a given project). We compose a comprehensive dataset of 1,360 verified vulnerable JavaScript functions using the Snyk vulnerability database and the VulnCode-DB project. From this ground-truth dataset, we build our vulnerable patterns for two common vulnerability types: prototype pollution and Regular Expression Denial of Service (ReDoS). With our framework, we analyze 9,205,654 functions (from 3,000 NPM packages, 1,892 websites and 557 Chrome Web extensions), and detect 117,601 prototype pollution and 7,333 ReDoS vulnerabilities. By further processing all 5,839 findings from NPM packages with our taint analyzer, we verify the exploitability of 290 zero-day cases across 134 NPM packages. In addition, we conduct an in-depth contextual analysis of the findings in 17 popular/critical projects and study the practical security exposure of 20 functions. With our semi-automated vulnerability reporting functionality, we disclosed all verified findings to project owners. We also obtained 25 published CVEs for our findings, 19 of them rated as “Critical” severity and six rated as “High” severity. Additionally, we obtained 169 CVEs that are currently “Reserved” (as of Apr. 2023). As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to the function level and thus improve the accuracy of detection and aid timely patching.

Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.

IoT devices have become an integral part of our day to day activities, and are also being deployed to fulfil a number of industrial, enterprise and agricultural use cases. To efficiently manage and operate these devices, the IoT ecosystem relies on several IoT management platforms. Given the security-sensitive nature of the operations performed by these platforms, analyzing them for security vulnerabilities is critical to protect the ecosystem from potential cyber threats. In this work, by exploring the core functionalities offered by leading platforms, we first design a security evaluation framework. Subsequently, we use our framework to analyze 42 IoT management platforms. Our analysis uncovers a number of high severity unauthorized access vulnerabilities in 9/42 platforms, which could lead to attacks such as remote SIM deactivation, IoT SIM overcharging and device data forgery. Furthermore, we find broken authentication in 11/42 platforms, including complete account takeover on 7/42 platforms, along with remote code execution on one of the platforms. Overall, on 11/42 platforms, we find vulnerabilities that could lead to platform-wide attacks, that affect all users and all devices connected to those platforms.

In second-hand vehicle markets, blockchains are being proposed as means to provide verification of vehicle history, a.k.a. vehicle passport (VP). However, given that confidentiality of VPs often contradicts public verification, blockchains are not used to their full potential in the proposed frameworks. Specifically, although blockchain smart contracts offer a decentralized mechanism for untrusted parties to fairly exchange digital assets without the need for a trusted third party, VP exchange is always carried off-chain. In this work, we investigate the problem of “fair exchange” of confidential VPs over public blockchains where its plain information must be verified against its publicly committed value. We propose a zero-knowledge proof, called Consistent Commitment Encryption (CCE), that enables the public verification of the consistency between ElGamal encryption of a given VP and its Pedersen commitment. We employ our CCE to build vPass, a decentralized vehicle passport framework that enables second-hand vehicle buyers to purchase vehicle history information from designated service providers and get it verified and delivered on-chain while preserving its confidentiality. The security of CCE relies on the intractability of the discrete logarithm problem in elliptic curve groups and it has no trusted setup. We formally prove that CCE is sound, complete, and witness indistinguishable proof of knowledge, and report on comparisons with other generic proof systems. Moreover, we show that vPass provides fair exchange and confidentiality of the vehicle history, and compare it to existing VP systems. Finally, we provide a proof of concept implementation on Ethereum and report the system performance metrics.

To cope with the unprecedented ubiquity of smart applications, Machine-Type Communication (MTC), the cellular communication backbone of the Internet of Things (IoT), has become an inevitable choice. In this paper, we investigate the achievable performance of MTC in an Ultra-Dense Network (UDN). To fully utilize the available resources in 5G and beyond networks, we exploit the propagation characteristics and excess bandwidth of the Millimeter wave (mmWave) band. Using tools from stochastic geometry, we provide a mathematical framework to evaluate the achievable Signal-to-Interference plus Noise Ratio (SINR) per user and the average capacity per Small Cell (SC) while considering the severe Inter-Cell Interference (ICI) of UDNs and the blockage effect in mmWave. The accuracy of the formu-lated analytical expressions is verified through extensive Monte-Carlo simulations. The obtained results show the existence of an optimal Small Cell (SC) density that maximizes the utilization of the deployed SCs.

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works which proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves.

Super-apps such as WeChat and Baidu host millions of mini-apps, which are very popular among users and developers because of the mini-apps’ convenience, lightweight, ease of sharing, and not requiring explicit installation. Such ecosystems involve several entities, such as the super-app and mini-app clients, the super-app backend server, the mini-app developer server, and other hosting platforms and services used by the mini-app developer. To support various user-level functionalities, these components must authenticate each other, which differs from regular user authentication to the super-app platform. In this paper, we explore the mini-app to super-app authentication problem caused by insecure development practices. This type of authentication allows the mini-app code to access super-app services on the developer’s behalf.

Software as a Service (SaaS) e-commerce platforms for merchants allow individual business owners to set up their online stores almost instantly. Prior work has shown that the checkout flows and payment integration of some e-commerce applications are vulnerable to logic bugs with serious financial consequences, e.g., allowing “shopping for free”. Apart from checkout and payment integration, vulnerabilities in other e-commerce operations have remained largely unexplored, even though they can have far more serious consequences, e.g., enabling “store takeover”. In this work, we design and implement a security evaluation framework to uncover security vulnerabilities in e-commerce operations beyond checkout/payment integration. We use this framework to analyze 32 representative e-commerce platforms, including web services of 24 commercial SaaS platforms and 15 associated Android apps, and 8 open source platforms; these platforms host over 10 million stores as approximated through Google dorks. We uncover several new vulnerabilities with serious consequences, e.g., allowing an attacker to take over all stores under a platform, and listing illegal products at a victim’s store—in addition to “shopping for free” bugs, without exploiting the checkout/payment process. We found 12 platforms vulnerable to store takeover (affecting 41000+ stores) and 6 platforms vulnerable to shopping for free (affecting 19000+ stores, approximated via Google dorks on Oct. 8, 2022). We have responsibly disclosed the vulnerabilities to all affected parties, and requested four CVEs (three assigned, and one is pending review).

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as machine-type communication (MTC). Hence, we consider an ultradense network (UDN) environment supported by wireless backhaul links (BHs) and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber BHs for the massive number of small cells (SCs) found in UDNs. We utilize the millimeter wave (mmWave) band to support the communications between the IoT Devices (IoTD) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe intercell interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the access link (AL) and the BH. In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity BHs. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

Motivated by the fact that mass authentication is one of the desirable security features in the edge computing paradigm, we propose a lightweight group authentication protocol with a session key-agreement. Most of the previously proposed group authentication schemes (GASs) are heavyweight and do not support multiple authentications or key-agreement. On the other hand, our protocol, which is based on secret sharing scheme and aggregated message authentication code, is lightweight and provides multiple asynchronous authentications. Furthermore, we implement a simple key refreshing mechanism in which, in each session, a new session-key between an Internet of Things node and the authenticating server is established without the need for redistributing new shares. Our security analysis includes proving that our protocol provides group authentication, message forward secrecy, and prevents several attacks. Additionally, we present a formal automated verification using Verifpal tool. Furthermore, we show that our scheme has better performance than other relative schemes in terms of communication complexity, secret-share redistribution, and session key derivations.

This paper formally investigates the problem of unauthorized yet required access to electronically protected information, a.k.a. Break-the-Glass (BtG) access. Reflecting on the rising deployment of such protocols in the current digitized healthcare system, we present Mjolnir, a blockchain-based BtG framework that offers accountability of unauthorized accesses by healthcare practitioners, dependable right of notification to patients, and privacy of healthcare records accesses. Mjolnir is a smart contract-based protocol which provides undisputed public verifiability of the identity of BtG access entities while maintaining their anonymity except from concerned individual patients, hence protecting the patients’ privacy. We employ an application specific non-interactive cryptographic zero knowledge proof system which ensures that the signing entity (healthcare practitioner) belongs to a given authorized group and that the anonymity of their identity is only revocable by a given opening entity (patient). The security of our system relies on the hardness of the discrete logarithm and decisional Diffie–Hellman problems in elliptic curve groups, and the utilized proof system requires no trusted setup. We formally define and prove the security goals of Mjolnir, provide a proof of concept blockchain implementation on Ethereum, and report on performance experiments and comparisons with other generic zero knowledge proof systems.

JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications, and is currently used in almost all websites. Because of its popularity, JavaScript has become a frequent target for attackers, who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing work mostly deals with package-level vulnerability tracking and measurements. However this approach is limited to detecting usage of already known vulnerabilities. In this paper we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world projects. We build our framework with the help of a comprehensive dataset of 1,360 verified vulnerable JavaScript functions that we compose based on Snyk vulnerability database and the VulnCode-DB project. Using our framework, we identify 11,148 vulnerable functions in three environments: NPM packages, Chrome web extensions and popular websites. In addition,we conduct an in-depth contextual analysis of the findings in several popular/critical projects and confirm the security exposure of 15 cases. As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to function level, and thus improve accuracy of detection and aid timely patching.

The Android operating system is currently the most popular mobile operating system in the world. Android is based on Linux and therefore inherits its features including its Inter-Process Communication (IPC) mechanisms. These mechanisms are used by processes to communicate with one another and are extensively used in Android. While Android-specific IPC mechanisms have been studied extensively, Unix domain sockets have not been examined comprehensively, despite playing a crucial role in the IPC of highly privileged system daemons. In this paper, we propose Sausage, an efficient novel static analysis framework to study the security properties of these sockets. Sausage considers access control policies implemented in the Android security model, as well as authentication checks implemented by the daemon binaries. It is a fully static analysis framework, specifically designed to analyze Unix domain socket usage in Android system daemons, at scale. We use this framework to analyze 200 Android images across eight popular smartphone vendors spanning Android versions 7–9. As a result, we uncover multiple access control misconfigurations and insecure authentication checks. Our notable findings include a permission bypass in highly privileged Qualcomm system daemons and an unprotected socket that allows an untrusted app to set the scheduling priority of other processes running on the system, despite the implementation of mandatory SELinux policies. Ultimately, the results of our analysis are worrisome; all vendors except the Android Open Source Project (AOSP) have access control issues, allowing an untrusted app to communicate to highly privileged daemons through Unix domain sockets introduced by hardware manufacturer or vendor customization.

Past privacy measurement studies on web tracking focused on high-ranked commercial websites, as user tracking is extensively used for monetization on those sites. Conversely, governments across the globe now offer services online, which unlike commercial sites, are funded by public money, and do not generally make it to the top million website lists. As such, web tracking on those services has not been comprehensively studied, even though these services deal with privacy and security-sensitive user data, and used by a significant number of users. In this paper, we perform privacy and security measurements on government websites and Android apps: 150,244 unique websites (from 206 countries) and 1166 Android apps (from 71 countries). We found numerous commercial trackers on these services—e.g., 17% of government websites and 37% of government Android apps host Google trackers; 13% of government sites contain YouTube cookies with an expiry date in the year of 9999. 27% of government Android apps leak sensitive information (e.g., user/device identifiers, passwords, API keys) to third parties, or any network attacker (when sent over HTTP). We also found 304 government sites and 40 apps are flagged by VirusTotal as malicious. We hope our findings to help improve privacy and security of online government services, given that governments are now apparently taking Internet privacy/security seriously and imposing strict regulations on commercial sites.

The recent advances and unprecedented ubiquity of computation-intensive applications such as virtual reality and mobile augmented reality force new approaches to handle the accompanying challenges. Ultra-Dense Network (UDN) as a leading direction in 5G and beyond offers an opportunistic degree of freedom to be exploited where a massive number of low-power and low-cost Small Cells (SCs) are deployed. In particular, integrating Edge Computing Servers (ECSs) within the SCs at the edge of the cellular network paves the way to tackle several challenges including the processing of computation-intensive tasks with low latency. To exploit the full potentials of UDNs and ECSs, we deploy multiple associations of SCs to the same user while partitioning and offloading its computation-intensive task to the integrated ECSs therein. In this regard, we formulate a mathematical framework using tools from stochastic geometry to evaluate the average processing delay per user. Extensive Monte-Carlo simulations are conducted to verify the accuracy of our analytical results under different system parameters. Results show the existence of an optimal order of multiple-association. In addition, we propose a novel offline task division approach which significantly reduces the overall delay.

According to the IEC 62351-7 standard, data collection using network and system management (NSM) can be used to support the security monitoring of the smart grid. In this article, an NSM security monitoring platform for a realistic IEC 61850 substation model is developed using the specifications provided in IEC 62351-7. In the developed model, grid measurements are ready to take operative decisions, whereas collected NSM data are leveraged to detect cyberattacks and/or identify anomalies. The model includes power components (e.g., transformers, lines, and generators), controllers (e.g., voltage control), protection devices (e.g., overcurrent, distance, differential, and under/overvoltage), communication protocols (e.g., sampled value and generic object-oriented substation event), and NSM (e.g., agents and managers) applications. Moreover, a two-step deep learning framework is proposed for anomaly detection and cyberattack identification with enhanced accuracy. The first step can apply long short-term memory, recurrent neural network, and gated recurrent units, each in combination with an autoencoder. Then, the ensemble learning technique is used in the second step to augment the outputs of these deep learning models. To evaluate the effectiveness of the proposed cyberattack and anomaly detection framework, we detail and simulate potential cyberattacks targeting the performance of the IEEE 9-bus system. The proposed anomaly detection scheme can identify these threats using NSM data in a hardware-in-the-loop testbed. Finally, based on our assessment results, recommendations are provided for cybersecurity guidelines concerning IEC 62351-7.

Traditional cryptographic approaches may not always be suitable for cyber–physical systems (CPSs). In this correspondence, we present a control-theoretic approach allowing a networked controller and a smart actuator of a stochastic CPS to agree on a common secret key without resorting to classical cryptographic approaches. More precisely, by utilizing the asymmetry in the system model knowledge available to the control system defender and to the eavesdropper, we propose a key-agreement scheme utilizing a simultaneous input and state estimation algorithm. The validity of the proposed solution is shown through a numerical example.

Ultra-Dense Networks (UDNs), where Small Cells (SCs) will be deployed in very high densities, are considered as a leading promising technology in 5G and beyond. Such SCs being equipped with multiple antennas can further enhance the achievable performance. In particular, significant gains can be obtained in securing the wireless data transfer through Physical Layer Security (PLS) protocols. In this paper, we study the combined effects and trade-offs between densifying the SCs and increasing the number of antennas per SC on the achievable downlink secrecy rate per user. Using tools from stochastic geometry, we derive an analytical expression for the achievable average secrecy rate. The obtained results show that both approaches of densifying the SCs and/or increasing the number of antennas per cell enhance the secrecy level of communication. Interestingly, we show that reducing the density of SCs can be compensated by increasing the number of antennas per cell in terms of the achievable secrecy rate.

Coexistence of Human-Type Communications (HTCs) and Machine-Type Communications (MTCs) is inevitable. Ultra-Dense Networks (UDNs) will be efficacious in supporting both types of communications. In a UDN, a massive number of low-power and low-cost Small Cells (SCs) are deployed with density higher than that of the HTC users. In such a scenario, the backhaul capacities constitute an intrinsic bottleneck for the system. Hence, we propose a multiple association scheme where each HTC user associates to and activates multiple SCs to overcome the backhaul capacity constraints mainly encountered in the downlink. In addition, having more active cells allows for more MTC devices to be supported by the network. Using tools from stochastic geometry, we formulate a novel mathematical framework investigating the performance of HTC in both downlink and uplink as well as the uplink MTC. Stretched Exponential Path Loss (SEPL) model is considered to practically reflect the UDN environment. Extensive simulations were conducted to verify the accuracy of the mathematical analysis under different system parameters. Results show the existence of an optimum number of SCs to which an HTC user may connect under backhaul capacity constraints. Besides, the proposed multiple-association scheme improves the performance of MTC in terms of both ASE and density of supported devices.

Industrial Control Systems (ICSs) are cyber-physical systems that offer attractive targets to threat actors due to the scale of damages, both physical and cyber, that successful exploitation can cause. As such, ICSs often find themselves victims to reconnaissance campaigns - coordinated scanning activity that targets a wide subset of the Internet - that aim to discover vulnerable systems. As these campaigns likely scan broad netblocks of the Internet, some traffic is directed to network telescopes, which are routable, allocated, and unused IP space. In this paper, we explore the threat landscape of ICS devices by analyzing and investigating network telescope traffic. Our network traffic analysis tool takes darknet traffic and generates threat intelligence on scanning campaigns targeting ICSs in the form of campaign fragments, which we leverage in new ways to get more in-depth knowledge of the cybersecurity threats. We investigate the payloads of the identified campaigns using a custom Deep Packet Inspection (DPI) technique to dissect and analyze the packets. We found 13 distinct payload templates and deduced their purpose, and by extension the campaign goals. We use machine learning to classify the sources behind the campaigns and identify threat actors such as botnets, malicious attackers, or researchers, and establish a methodology to rank our campaigns to prioritize our analysis. To conduct our analysis of the threats targeting ICSs, we have leveraged 12.85 TB (330 days) of network traffic received by our observed darknet IP space. Combining these investigative threads, we provide a thorough overview of the threat landscape targeting ICS systems.

As the penetration rate of flexible loads and distributed energy resources in the distribution networks increases, congestion management techniques that utilize demand-side management (DSM) have been developed. These are indirect methods that rely on information exchange between the distribution network operator, aggregators, and consumers’ meters to encourage customers to change their demand to relieve congestion. Cyber attacks against aggregators can compromise the operation of DSM-based congestion management methods, and hence, affect the security and reliability of electrical networks. In this article, the vulnerability of indirect congestion management methods to load-altering attacks is studied. An optimization algorithm is developed to determine the aggregators a cyber attacker would compromise, via minimum alteration of their load profiles, to cause congestion problems. The impact of such attacks on congestion and consumers’ electricity bill is then studied. A mitigation scheme is formulated to determine the most critical aggregators in the network. The security of these aggregators is then reinforced to mitigate such cyber attacks.

In this paper, we present a$\textsf {S}$ymmetric$\textsf {K}$ey$\textsf {I}$nter-$\textsf {C}$loud$\textsf {A}$uthentication and redeemable micropayment$\textsf {P}$rotocol ($\textsf {SKICAP}$) for IoT-Edge computing applications. The proposed protocol takes into account the mobile and limited resource nature of IoT devices by enabling them to register with their home cloud admin for computation offloading service provided by foreign Edge-Cloud instances. More precisely,$\textsf {SKICAP}$ensures that mobile IoT devices can be authenticated and serviced by edge nodes outside of their home cloud coverage, while guaranteeing the associated charge redemption. Additionally, since our protocol considers the mobile and anywhere deployable requirements of IoT devices, we utilize physical unclonable function and cryptographic hash function to make it privacy-preserving and resilient to hardware compromise. Under the assumption of an indistinguishably secure physical unclonable function, we provide a formal security analysis of our protocol. Finally, we report$\textsf {SKICAP}$performance on an ARM Cortex-A72 processor, and compare it with other closely related protocols.

For parents of young children and adolescents, the digital age has introduced many new challenges, including excessive screen time, inappropriate online content, cyber predators, and cyberbullying. To address these challenges, many parents rely on numerous parental control solutions on different platforms, including parental control network devices (e.g., WiFi routers) and software applications on mobile devices and laptops. While these parental control solutions may help digital parenting, they may also introduce serious security and privacy risks to children and parents, due to their elevated privileges and having access to a significant amount of privacy-sensitive data. In this paper, we present an experimental framework for systematically evaluating security and privacy issues in parental control software and hardware solutions. Using the developed framework, we provide the first comprehensive study of parental control tools on multiple platforms including network devices, Windows applications, Chrome extensions and Android apps. Our analysis uncovers pervasive security and privacy issues that can lead to leakage of private information, and/or allow an adversary to fully control the parental control solution, and thereby may directly aid cyberbullying and cyber predators.

Non-Orthogonal Multiple-Access (NOMA) where multiple users share the same resources simultaneously, is one promising candidate for beyond 5G. Besides, an Ultra-Dense Network (UDN) with massive numbers of deployed Small Cells (SCs) can significantly boost the performance of the network. In this paper, we propose a hybrid NOMA/OMA scenario deployed within a UDN environment to support a massive number of devices under the umbrella of the massive MachineType Communication (mMTC) use case. NOMA is performed through pairing devices from two disjoint groups with different normalized Signal-to-Interference Ratio (SIR). Using tools from stochastic geometry, we derive an analytical expression for the Area Spectral Efficiency (ASE) gain when deploying our proposed hybrid NOMA/OMA scheme and compare it to a scenario of pure Orthogonal Multiple-Access (OMA). Moreover, in order to reflect the characteristics of the UDN environment, we model the large scale fading using the Stretched Exponential Path Loss (SEPL) model. We show through both simulations and analyses that the gain obtained from the hybrid NOMA/OMA scheme can be optimized based on the different system parameters. We also investigate the impact of densifying the network on the significance of NOMA deployment.

In this article, we study the uplink coverage and the uplink network throughput of an ultradense network (UDN) where human-type communication (HTC) users and machine-type communication (MTC) devices coexist. We employ non-orthogonal multiple access (NOMA) radio access to address the massive connectivity requirements of MTC while satisfying the high data rate requirements of HTC. To this end, stochastic geometry is utilized to model the network exploiting the inherent randomness of the proposed scenario. The enormous number of small cells in UDN along with NOMA can provide a common ground to satisfy the diverse requirements of both MTC and HTC. The distinguishing features of both UDNs and MTC are considered in modeling the network where stretched exponential path-loss (SEPL) is used to capture short-link distances. Moreover, the truncated channel inversion power control is employed in both HTC users (HTCUs) and MTC devices (MTCDs) to mitigate the uplink intercell interference. The results show the significant impact of various system parameters on the network performance. Closed-form and easy-computable expressions are derived for the considered performance metrics, and are assessed by Monte Carlo simulations.

In this article, we propose a lightweight authentication protocol that provides forward secrecy for edge-based applications. Motivated by the general consensus that centralized authentication solutions are not suitable for an expanding Internet of Things (IoT), our edge-based authentication reduces latency for critical applications, lowers cloud dependency, and employs cryptographic primitives, which are efficiently implemented on resource-constrained low-end devices. Moreover, the edge entity broadcast messages using session keys that are derived securely from a hash function. The protocol utilizes hash chains and authenticated encryption which makes it resilient to quantum attacks. Moreover, entities are not required to hold a permanent master key, and all session keys are derived securely from a hash function. As a use case, we present a smart emergency system where an edge application broadcasts alert messages for individual responder groups when specific events occur. We formally define and prove the main security properties of our protocol, and compare it to other lightweight protocols in terms of security and performance. The computational complexity of our protocol comprises of three decryption operations, two HMAC, and five hash computations. The required storage for each node is 96 B and the communication overhead is only 56 B per session.

The U.S. Department of Homeland Security (DHS) has recently identified digital relays as targets vulnerable to cyber-attacks. The DHS has also noted that attacks to multiple relays can bring about cascading outages of transmission lines, leading to blackouts. As a result, making protective relays cyber-resilient is a prominent security issue in power networks. Line current differential relays (LCDRs) are among the potentially vulnerable digital relays that are increasingly deployed for protecting critical transmission lines. LCDRs, however, lack the required resiliency against cyber attacks, due to their high dependence on communication systems. This paper unveils that such susceptibilities can result in unwarranted trip signals through false data injection attacks (FDIAs), and so cause instability if several attacks are coordinated. It also presents a solution for detecting FDIAs and distinguishing them from real internal faults. To detect attacks, the proposed method compares the estimated and locally measured voltages at an LCDR's terminal for both the positive sequence (PS) and negative sequence (NS). To estimate the local voltage for each sequence, the proposed technique uses an unknown input observer (UIO), the state-space model of the faulty line, and remote and local measurements, all associated with that sequence. The difference between the measured and estimated local voltages for each sequence remains close to zero during real internal faults because, in this condition, the state-space model based on which the UIO operates correctly represents the line. Nevertheless, the state-space model mismatch during FDIAs leads to a large difference between measured and estimated values in both sequences. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus network.

Modern mobile operating systems such as Android and Apple iOS allow apps to access various system resources, with or without explicit user permission. Running multiple concurrent apps is also commonly supported, although the OS generally maintains strict separation between apps. However, an app can still get access to another app's private information, such as the user input, through numerous side-channels, mostly enabled by having access to permissioned or permission-less (sometimes even unrelated) resources, e.g., inferring keystroke and swipe gestures from a victim app via the accelerometer or gyroscope. Current mobile OSes do not empower an app to defend itself from such implicit interference from other apps; few exceptions exist such as blocking screenshot captures in Android. We propose a general mechanism for apps to defend themselves from any unwanted implicit or explicit interference from other concurrently running apps. Our AppVeto solution enables an app to easily configure its requirements for a safe environment; a foreground app can request the OS to disallow access---i.e., to enable veto powers---to selected side-channel-prone resources to all other running apps for a certain (short) duration, e.g., no access to the accelerometer during password input. In a sense, we enable a finer-grained access control policy than the current runtime permission model, and delegate the responsibility of the resource access decision (for vetoing) from users to app developers. We implement AppVeto on Android using the Xposed framework, without changing Android APIs. Furthermore, we show that AppVeto imposes negligible overhead, while being effective against several well-known side-channel attacks.

Smart toys have captured an increasing share of the toy market, and are growing ubiquitous in households with children. Smart toys are a subset of Internet of Things (IoT) devices, containing sensors, actuators, and/or artificial intelligence capabilities. They frequently have Internet connectivity, directly or indirectly through companion apps, and collect information about their users and environments. Recent studies have found security flaws in many smart toys that have led to serious privacy leaks, or allowed tracking a child's physical location. Some well-publicized discoveries of this nature have prompted actions from governments around the world to ban some of these toys. Compared to other IoT devices, smart toys pose unique risks because of their easily vulnerable user base, and this paper is intended to define these risks and assess a subset of toys against them. We provide a classification of threats specific to smart toys in order to unite and complement existing adhoc analyses, and help comprehensive evaluation of other smart toys. Our vulnerability taxonomy addresses the potential security and privacy flaws that can lead to leakage of private information or allow an adversary to control the toy to lure, harm, or distress a child. Using this taxonomy, we perform a thorough experimental analysis of eleven smart toys and their companion apps. Our systematic analysis has uncovered that several current toys still expose children to multiple threats for attackers with physical, nearby, or remote access to the toy.

Many enterprise-grade network appliances host a TLS proxy to facilitate interception of TLS-protected traffic for various purposes, including malware scanning, phishing detection, and preventing data exfiltration. When deployed, the TLS proxy acts as the security validating client for external TLS web servers, on behalf of the original requesting client; on the other hand, the proxy acts as the web server to the client. Consequently, TLS proxies must maintain a reliable level of security, at least, at the same level as modern web browsers and properly configured TLS servers. Failure to do so increases the attack surface of all the proxied clients served the network appliance. We develop a framework for testing TLS inspecting appliances, combining and extending tests from existing work on client-end and network-based interception. Utilizing this framework, we analyze six representative network appliances, and uncover several security issues regarding TLS version and certificate parameters mapping, CA trusted stores, private keys, and certificate validation tests. For instance, we found that two appliances perform no certificate validation at all, exposing their end-clients to trivial Man-in-the-Middle attacks. The remaining appliances that perform certificate validation, still do not follow current best practices, and thus making them vulnerable against certain attacks. We also found that all the tested appliances deceive the requesting clients, by offering TLS parameters that are different from the proxy-to-server TLS parameters, such as the TLS versions, hashing algorithms, and RSA key sizes. We hope that this work bring focus on the risks and vulnerabilities of using TLS proxies that are being widely deployed in many enterprise and government environments, potentially affecting all their users and systems.

Extended coverage is an essential requirement of the successful deployment of Machine-Type Communication (MTC) in harsh scenarios. This would require the MTC nodes to transmit their measurement reports with a high transmit power. At the same time, it is desirable to minimize the transmit power of the MTC nodes for the sake of a longer battery lifetime. These contradicting targets make the uplink coverage one of the main limiting factors to the fruition of MTC applications. In this paper, we analyze the uplink coverage of MTC considering the distinguishing features of a dense network including the high density of cells and the short distances between the cells and the MTC nodes. We model the path loss as a stretched exponential path-loss (SEPL) to capture the short link distances. In addition, a truncated channel inversion power control is considered to satisfy the strict requirements on the power consumption of MTC nodes. The accurate and tractable results unveiled the impact of the system parameters on the network performance. Particularly, the uplink coverage significantly improved at moderate cell density, reasonable bandwidth, and low transmission power. Interestingly, our results reveal that the maximum transmit power has no impact on the uplink coverage in the considered scenario. Fortunately, this allows for a longer lifetime for the battery-powered devices of future IoT applications.

False data injection attacks (FDIAs) against the automatic generation control (AGC) system can lead to unstable or non-optimal operation of the power grid. This paper introduces a method to detect FDIAs targeting the AGC system by developing a stochastic unknown input estimator (SUIE). The SUIE estimates the states of the load-frequency control system, which contains the AGC as a control loop. An increase in the SUIE's residual function (RF) beyond a defined threshold signifies an FDIA. The SUIE can be designed such that it works independently from some or all inputs to the system's state-space model. In addition, the effect of process and measurement noise on the estimated states is minimized through an optimal gain setting technique for the SUIE. Therefore, not only does the SUIE eliminate the need for information about real-time load changes throughout the grid, it also maximizes the state estimation accuracy. The combination of these features distinguishes the proposed method from existing FDIA detection techniques for the AGC system. This paper also develops a number of attack identification SUIEs (AISUIEs) to determine which measurements are compromised by an FDIA, thus facilitating FDIA mitigation strategies. The AISUIEs model FDIAs targeting each AGC measurement by an attack input. These inputs serve as the unknown inputs of different AISUIEs, whose RFs indicate the type of attack. The designed AISUIEs also differentiate between attacks and non-attack abnormalities such as faults. Simulation analysis of a three-area power system corroborates the effectiveness of the proposed method. In addition, the performance of the proposed method is tested using an OPAL real-time simulator, and is compared with another technique from the literature.

Code reuse detection is a key technique in reverse engineering. However, existing source code similarity comparison techniques are not applicable to binary code. Moreover, compilers have made this problem even more difficult due to the fact that different assembly code and control flow structures can be generated by the compilers even when implementing the same functionality. To address this problem, we present a fuzzy matching approach to compare two functions. We first obtain an initial mapping between basic blocks by leveraging the concept of longest common subsequence on the basic block level and execution path level. We then extend the achieved mapping using neighborhood exploration. To make our approach applicable to large data sets, we designed an effective filtering process using Minhashing. Based on the proposed approach, we implemented a tool named BinSequence and conducted extensive experiments with it. Our results show that given a large assembly code repository with millions of functions, BinSequence is efficient and can attain high quality similarity ranking of assembly functions with an accuracy of above 90%. We also present several practical use cases including patch analysis, malware analysis and bug search.

The distinct features of Ultra-Dense Networks (UDNs), namely, the close proximity of the users to the serving base stations (BSs), the high idle mode probability and the increasing probability of Line-of-Sight (LOS) links to the serving BS, impose a set of requirements on the realistic and accurate modeling of the performance of such networks. In this paper, we consider modeling the path loss by a stretched exponential model which accurately addresses the short distances (5m-350m) between the (serving/interfering) BSs and the users in UDN. Moreover, we study the impact of turning off inactive BSs, as an effective interference mitigation scheme, on the performance of the network in terms of the coverage probability, the network throughput, and the area spectral efficiency. We employ tools from stochastic geometry to model the network as a Homogeneous Poisson Point Process (HPPP). Also, Rayleigh channel fading is assumed for tractability purposes. The results show the significant impact of the users' density on the network performance where the system's interference is upper-bounded mainly by the density of the active users, thanks to turning off the inactive BSs.

In this paper, we study the average downlink rate in Ultra-Dense Networks (UDNs) considering the idle mode capability of the base stations (BSs). Wireless networks are densified by deploying a surplus of small cells in order to provide uniform coverage and high capacity. Consequently, the density of the small cells outpaces that of the active users in UDNs. Idle mode capability permits the idle BSs, due to lack of connected users, to turn off in order to mitigate their interference. We employ tools from stochastic geometry to compute the average downlink rate considering a general fading channel in both signal and interference links. In UDN, the likelihood of Line-of-Sight (LOS) transmission in some scenarios is high due to the closeness of the serving cells to the associated users. This propagation environment requires the consideration of Rician channel rather than Rayleigh channel in this context. The analytical results show a high degree of accuracy which is confirmed by extensive simulations in different combinations of the system parameters including small cell density, active users density, path loss exponent, and fading channels. Moreover, we simulate the impact of the association of a user to many BSs on the average downlink rate.

In this paper, we study the impact of the coexistence of Machine-to-Machine (M2M) communication and Human-to-Human (H2H) communication on the network performance in Ultra-Dense Networks (UDNs). The performance evaluation of the network considers the downlink coverage, the average cell load of H2H users and the average uplink cell load of M2M devices. Using tools from stochastic geometry, we develop tractable expressions for the considered performance metrics. Furthermore, we investigate two association schemes to address the severe interference in UDNs, namely, connect to closest (de) base station and connect to active (CIA) base station. Considering the distinguishing features of UDNs, we model the path loss by a Stretched Exponential Path Loss (SEPL) model to address the close proximity of users to the base stations (BSs) and the high probability of Line-of-Sight (LOS) transmission as well. The simulation results show an accurate match with the analytical results. The network coverage significantly improves in C2A association scheme with no impact on the average cell load of H2H users. On the other hand, the average uplink cell load of M2M devices increases in C2A association.

Real world physical shopping offers customers the privilege of maintaining their privacy by giving them the option of using cash, and thus providing no personal information such as their names and home addresses. On the contrary, electronic shopping mandates the use of all sorts of personally identifiable information for both billing and shipping purposes. Cryptocurrencies such as Bitcoin have created a stimulated growth in private billing by enabling pseudonymous payments. However, the anonymous delivery of the purchased physical goods is still an open research problem. In this work, we present a blockchain-based physical delivery system called Lelantos1 that within a realistic threat model, offers customer anonymity, fair exchange and merchant-customer unlinkability. Our system is inspired by the onion routing techniques which are used to achieve anonymous message delivery. Additionally, Lelantos relies on the decentralization and pseudonymity of the blockchain to enable pseudonymity that is hard to compromise, and the distributed consensus mechanisms provided by smart contracts to enforce fair irrefutable transactions between distrustful contractual parties.

In this paper, we propose a general mathematical framework to compute the average downlink rate in a multiple connectivity context considering ultra-dense network (UDN) environment. UDN is a dense small cells network featured by the high density of small cells that may exceed the density of active users. In multiple association, a user connects to M base stations (BSs) that provide the maximum average received power forming a multicell. This provides the user with a “data-shower,” where the user's traffic is split into multiple paths, which helps overcoming the capacity limitations imposed by the backhaul links. The developed framework significantly simplifies the computation of the average downlink rate of the individual connections to the cells of a multicell. Moreover, the accuracy of the mathematical framework is confirmed by extensive simulations. The simulation results show a perfect match with the numerical results computed from the mathematical framework in different combinations of the system parameters including multicell size, small cells density, active users density, pathloss exponent, and fading channel distribution of the signal link.

The market for civilian unmanned aerial vehicles, also known as drones, is expanding rapidly as new applications are emerging to incorporate the use of civilian drones in our daily lives. On one hand, the convenience of offering certain services via drones is attractive. On the other hand, the mere operation of these airborne machines, which rely heavily on their cyber capabilities, poses great threats to people and property. Also, while the Federal Aviation Administration NextGen project aims to integrate civilian drones into the national airspace, the regulation is still a work-in-progress and does not cope with their threats. This article surveys the main security, privacy, and safety aspects associated with the use of civilian drones in the national airspace. In particular, we identify both the physical and cyber threats of such systems and discuss the security properties required by their critical operation environment. We also identify the research challenges and possible future directions in the fields of civilian drone security, safety, and privacy. Based on our investigation, we forecast that security will be a central enabling technology for the next generation of civilian unmanned aerial vehicles.

The cell association is of a paramount effect on the operation of cellular network. In Ultra-Dense Networks (UDN), different types of small cells are deployed with extremely large densities. This comes with a great advantage of dense reuse of spectrum. In this paper, we investigate the downlink association of a given user equipment (UE) to multiple small cells, which we termed multiple association. In multiple association, a user connects to M ≥ 1 small cells forming what we call MultiCell. Consequently, this overcomes the backhaul limitation of individual cells. Specifically, we derive the idle mode probability in the proposed setting, and based on that we derive an analytical expression for the average ergodic downlink rate of the link between the typical user and its jth nearest cell. Additionally, we exploit the aforementioned findings to study the area spectral efficiency in multiple association, and to investigate its relation to the main system parameters, namely: small cells density, users density, and MultiCell size M. We simulate the proposed model to assess the accuracy of the analytical results. Our results provide a mathematical framework, and pave the way to consider the association of a user to multiple small cells.

In August 2012, the Streebog hash function was selected as the new Russian cryptographic hash standard (GOST R 34.11‐2012). In this study, the authors investigate the new standard in the context of malicious hashing and present a practical collision for a malicious version of the full hash function. In particular, they apply the rebound attack to find three solutions for three different differential paths for four rounds. Then, using the freedom of the round constants they connect them to obtain a collision for the 12 rounds of the compression function. Additionally, and because of the simple processing of the counter, they bypass the barrier of the checksum finalisation step and transfer the compression function collision to the hash function output with no additional cost. The presented attack has a practical complexity and is verified by an example. Although the results of this study may not have a direct impact on the security of the current Streebog hash function, it presents an urge for the designers to publish the origin of the used parameters and the rational behind their choices in order for this function to gain enough confidence and widespread adoption by the security community.

Botmasters are known to use different protocols to hide their activities. Throughout the past few years, several protocols have been abused, and recently Domain Name System (DNS) also became a target of such malicious activities. In this paper, we study the use of DNS as a malicious payload distribution channel. We present a system to analyze the resource record activities of domain names and build DNS zone profiles to detect payload distribution channels. Our work is based on an extensive analysis of malware datasets for one year, and a near real-time feed of passive DNS traffic. The experimental results reveal a few previously unreported long-running hidden domains used by the Morto worm for distributing malicious payloads. Our experiments on passive DNS traffic indicate that our system can detect these channels regardless of the payload format.

Scan-based Design-for-Test (DFT) is a widely deployed technique for testing hardware chips. Using this approach, all flip-flops in the design under test are connected to a scan chain where their states can be scanned out through this chain during the testing phase. Scan-based side channel attacks exploit the information obtained by analyzing the scanned data in order to retrieve secret information from cryptographic hardware devices that are designed with this testability feature. The NTRU encryption algorithm (NTRUEncrypt) is a parameterized family of lattice-based public key cryptosystems which has recently been accepted to the IEEE P1363 standards under the specifications for lattice-based public-key cryptography. In this paper, we present a scan-based side channel attack on NTRUEncrypt hardware implementations that employ scan based DFT techniques. Our attack determines the scan chain structure of the polynomial multiplication circuits used in the decryption algorithm which allows the cryptanalyst to efficiently retrieve the secret key.

RC4 is a stream cipher that makes use of aninternal state table, S, which represents a permutation over Z28 . GGHN is a relatively more efficient stream cipher whose design is inspired from RC4 but whose S table, however, does not represent a permutation over Z2m. In this paper, we point out one challenging aspect of the latter design principle. In particular, we assess GGHN-like algorithms with respect to weak states, in which all internal state words and output elements are even. Once GGHN is absorbed in a weak state, the least significant bit of the plaintext words will be revealed only by looking at the ciphertext. By modelling the algorithm by a Markov chain and calculating chain's absorption time, we show that the average number of steps required by these algorithms to enter this weak state can be lower than expected at first glance and hence caution should be exercised when estimating this number.

Honeypots are traps designed to resemble easy-to-compromise computer systems in order to deceive botmasters. Such security traps help security professionals to collect valuable information about botmasters' techniques and true identities. Depending on the complexity of services provided by honeypots, botmasters might be able to detect these traps by performing a series of tests. In particular, to detect honeypots, botmasters can command compromised machines to perform specific actions such as targeting sensor machines controlled by them. If honeypots were designed to completely ignore these commands, then they can easily be detected by the botmasters. On the other hand, full participation by honeypots in such activities has its associated costs and may lead to legal liabilities. This raises the need for finding the optimal response strategy needed by the honeypot in order to prolong its stay within the botnet without sacrificing liability. In this paper, we address the problem of honeypot detection by botmasters. In particular, we present a Bayesian game theoretic framework that models the interaction between honeypots and botmasters as a non-zero-sum noncooperative game with uncertainty. The game solution illustrates the optimal response available for both players. Simulation results are conducted to show the botmasters' behavior update and possible interactions between the game players. The obtained results can be utilized by security professionals to determine their best response to these kind of probes by botmasters.

Cooperative diversity is a powerful tool that can be used to improve the performance of wireless networks. The use of directional antennas has also shown to offer an effective way for efficient bandwidth utilisation. In this study, the authors investigate the effect of both the transmission power and the number of cooperative relays on the maximum achievable throughput in energy-constrained cooperative ad hoc networks with directional antennas. In particular, the authors develop an analytical model for the network throughput in terms of the number of cooperative relays and nodes' transmission power. Using our model, we determine the optimum number of relays for a given transmission power, and the optimum transmission power for a given number of relays. Furthermore, we propose a cooperative relaying protocol that utilises the above optimal values to maximise the achievable throughout in such energy-constrained networks. The obtained analytical results as well as the performance of the proposed protocol are validated by simulations over a Rayleigh fading channel.

SNOW 3G is a stream cipher chosen by the 3rd Generation Partnership Project (3GPP) as a crypto-primitive to substitute KASUMI in case its security is compromised. SNOW 2.0 is one of the stream ciphers chosen for the ISO/IEC standard IS 18033-4. In this study, the authors show that the initialisation procedure of the two ciphers admits a sliding property, resulting in several sets of related-key pairs. In case of SNOW 3G, a set of 232 related-key pairs is presented, whereas in the case of SNOW 2.0, several such sets are found, out of which the largest are of size 264 and 2192 for the 128-bit and 256-bit variant of the cipher, respectively. In addition to allowing related-key recovery attacks against SNOW 2.0 with 256-bit keys, the presented properties reveal non-random behaviour that yields related-key distinguishers and also questions the validity of the security proofs of protocols that are based on the assumption that SNOW 3G and SNOW 2.0 behave like perfect random functions of the key–IV.

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over frequency-selective fading channels. Based on the expectation-maximization (EM) algorithm, the proposed iterative receiver achieves a performance close to the optimum maximum-likelihood (ML) receiver. In addition, the performance of the proposed receiver is optimized through weight coefficients using the minimum mean-square error (MMSE) criterion. Compared to the single-user bound, our results show that the proposed receiver can mitigate the multiple-access interference and attain the full system diversity. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

In cdma2000 1xEV, each mobile station determines its data rate based on a reverse activity bit (RAB) broadcasted by the base station and a set of up/down probabilities. In this paper, we propose a new rate control scheme that allows mobile stations to choose between two optimal rates determined by the rise over thermal (RoT) constraints. The base station determines the RAB signal based on the current RoT and estimated parameters of the next frame transmission. The proposed scheme is modelled using a Markov process. Both our simulation and analytical results confirm that the proposed scheme achieves better performance than previously proposed rate assignment schemes.

In this paper, we present our reverse engineering results for the Zeus crimeware toolkit which is one of the recent and powerful crimeware tools that emerged in the Internet underground community to control botnets. Zeus has reportedly infected over 3.6 million computers in the United States. Our analysis aims at uncovering the various obfuscation levels and shedding the light on the resulting code. Accordingly, we explain the bot building and installation/infection processes. In addition, we detail a method to extract the encryption key from the malware binary and use that to decrypt the network communications and the botnet configuration information. The reverse engineering insights, together with network traffic analysis, allow for a better understanding of the technologies and behaviors of such modern HTTP botnet crimeware toolkits and opens an opportunity to inject falsified information into the botnet communications which can be used to defame this crimeware toolkit.

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over Rayleigh fading channels. The proposed receiver performs the channel estimation and data detection using the expectation-maximization (EM) algorithm. We derive a closed-form expression for the optimized weight coefficients of the EM algorithm which is shown to provide a large performance improvement relative to the conventional equal-weight EM-based signal decomposition. Our results show that the receiver can achieve near-optimum performance with modest complexity using very few training symbols. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

Efficient utilisation of bandwidth and high data rates have a great impact on the performance of ad hoc wireless networks. The use of cooperative diversity, where neighbouring stations may act as relay nodes to transfer the source data to the desired destination node through an independent relay channel, has shown to provide diversity gain and consequently improve the achievable bit rate. However, this is usually attained at the expense of loosing some wireless resources such as bandwidth. On the other hand, the use of directional antennas has shown to offer an effective way for efficient bandwidth utilisation. Here, the authors propose a new channel access protocol for transmission over cooperative ad hoc networks. The proposed protocol, which aims at minimising the number of blocked nodes and consequently improving the system throughput, employs directional antennas at both the source and relay stations. The network performance under the proposed settings is modelled using continuous Markov chains. The steady-state transmission blocking probability and the average network throughput are obtained by analysing the derived Markov model. The analytical results, which are validated through simulations, show the improvement in performance compared to the carrier sense multiple access with collision avoidance protocol that employs omnidirectional antennas.

In this paper, we investigate the performance of superimposed training-based channel estimation techniques considering an asynchronous code-division multiple-access (CDMA) uplink transmission over frequency-selective fading channels. In that, we analyze the performance of a channel estimation and data detection scheme based on superimposed training for space-time spreading (STS) systems. Our results show that the scheme enhances the performance of the space-time system by eliminating the interference effect from both the channel and data estimates using two decorrelators; channel and data decorrelators. Compared with other conventional estimation techniques, the underlying system is more robust to channel estimation errors. Furthermore, both simulations and analytical results indicate that full system diversity is achieved.

In this paper, we propose a new channel access protocol for transmission over cooperative ad-hoc networks. The proposed protocol, which aims at minimizing the number of blocked nodes and consequently improving the system throughput, employs directional antennas at both source and relay stations. The network performance under the proposed settings is modelled using continues Markov chains. Steady state transmission blocking probability and average network throughput are obtained by analyzing the Markov model. Both analytical and simulation results show the improvement in performance compared to carrier sense multiple access with collision avoidance protocol that employs omnidirectional antennas.

The performance of direct-sequence code division multiple access (DS-CDMA) using space–time spreading system, over frequency-selective fading channels, is investigated. The underlying transmit diversity scheme, previously introduced in the literature, is based on two transmit and one receive antenna. It was shown that when employed in flat fast-fading channels, the received signal quality can be improved by utilising the spatial and temporal diversities at the receiver side. We study the problem of multiuser interference in asynchronous CDMA systems that employ transmit/receive diversity using space–time spreading. To overcome the effects of interference, a decorrelator detector is used at the base station. Considering binary phase-shift keying transmission, we analyse the system performance in terms of its probability of bit error. In particular, we derive the probability of error over frequency-selective Rayleigh fading channels for both fast and slow-fading channels. For the fast-fading channel, both simulations and analytical results show that the full system diversity is achieved. On the other hand, when considering a slow-fading channel, we show that the scheme reduces to conventional space–time spreading schemes where the diversity order is half of that of fast-fading.

Using a heuristic search combined with some algebraic techniques, several examples for10-variable Boolean functions with nonlinearity488, algebraic degree7, and resiliency degree2, were constructed. This construction affirmatively answers the open problem about the existence of such functions.

In this paper, we propose a new joint channel and data detection scheme based on the superimposed training technique for space-time spreading systems. The proposed scheme enhances the performance by eliminating the multiple access interference from both the channel and data estimation by employing two decorrelators: channel and data decorrelators. On a frequency-selective fading channel, our simulation results show that the proposed scheme outperforms other conventional joint channel and data estimation techniques. Moreover, unlike other conventional detection techniques, our proposed estimation technique is shown to achieve the full system diversity.

Pei and Hsue (IEEE Signal Processing Letters, Vol. 13, No. 6, pp. 329-332, June 2006) proposed an encryption scheme based on multiple-parameter discrete fractional Fourier transform. We show that all the building blocks of this scheme are linear, and hence, breaking this scheme, using a known plaintext attack, is equivalent to solving a set of linear equations.

Abstract The use of directional antennas in mobile ad hoc networks (MANETs) has shown to offer large throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium‐access‐control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS). Although GPS systems are designed to be as nearly accurate as possible, there are still estimation errors that can cause a relatively large deviation from the actual GPS receiver position. In this paper, we investigate the effect of inaccurate node position estimation on the throughput of these protocols. Our results clearly indicate that the advantages of DMAC protocols diminish if the available position information is not accurate enough. As an alternative, we propose an efficient DMAC protocol that utilizes signal parameter estimation via the rotational invariance technique (ESPRIT) for direction‐of‐arrival (DOA) estimation; alleviating the need for GPS and, hence, avoiding the degrading associated with typical GPS position estimation errors. Moreover, unlike GPS‐based protocols, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11. Copyright © 2007 John Wiley & Sons, Ltd.

Asynchronous direct-sequence code-division multiple-access (DS - CDMA) using space-time spreading system is investigated over frequency-selective fast-fading channels. The underlaying transmit diversity scheme, previously introduced in the literature, is based on two transmit and one receive antenna. It was shown that when employed in flat fast-fading channels, the received signal quality can be improved by utilizing the spatial and temporal diversities at the receiver side. Here, the bit-error-rate (BER) performance of the underlying system is investigated for an uplink transmission where a decorrelator detector is used at the base station receiver. In particular, we derive a closed form expression for the probability of error over frequency-selective fast-fading channels. The analytical BER is derived as a function of both the number of multipath and users. The BER results show that the space-time spreading scheme exploits both the temporal and spatial diversity in a time-varying multipath channel. The results also show the accuracy of the derived expression when compared with simulation results for different number of users.

The use of directional antennas in mobile ad hoc networks has shown to offer large potential throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium-access-control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS) which, typically, requires a line of sight in order to avoid the large signal attenuation and hence is not suitable for indoor applications. Moreover, as the inaccuracy associated with the GPS position estimation increases, the system throughput dramatically degrades. In this paper, we propose an efficient two-channel two-mode DMAC protocol. Our protocol employs two frequency division multiplexed channels: channel one used for omni-mode transmission and channel two for directional mode transmission. Signal parameter estimation via the rotational invariance technique (ESPRIT) is used for direction-of-arrival (DOA) estimation. By avoiding the reliance on GPS for obtaining the position information, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results clearly show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11.

We investigate the use of Particle Swarm Optimization (PSO) in automated cryptanalysis of classical simple substitution ciphers. Based on our experimental results, PSO-based attacks proved to be very effective on various sets of encoding keys.

The application of directional antennas in mobile ad-hoc networks (MANETs) has proved to offer large throughput gains relative to single-antenna systems. Recent works on directional medium-access-control (MAC) protocols have been only focused on their performance over additive white-Gaussian noise (AWGN) channels. In this paper, we evaluate the performance of directional MAC protocols when the channel is modeled as a slow-fading one. In this case, directional antennas are used for the transmission of data frames while control frames are sent using an omnidirectional antenna. Considering a slow-fading channel, our results show large throughput improvements when using directional MAC protocols relative to the IEEE 802.11 standard. Furthermore we show that the throughput loss on the fading channels relative to the AWGN channel, can be well compensated using antenna arrays. All our results show that the use of directional antennas at the mobile station can improve the channel efficiency in an ad-hoc network.

Using a heuristic search technique, several examples for 9-variable Boolean functions with nonlinearity 240, algebraic degree 5, and resiliency degree 3 were constructed. This construction affirmatively answers the open problem about the existence of such functions.

Welch-Gong (WG) transformation sequences are binary sequences of period 2/sup n/ - 1 with two-level autocorrelation. These sequences were discovered by Golomb, Gong, and Gaal (1998) and they verified the validity of their construction for 5 /spl les/ n /spl les/ 20. Later, No, Chung, and Yun (1998) found another way to construct the WG sequences and verified their result for 5 /spl les/ n /spl les/ 20. Dillon (1998) first proved this result for odd n, and, finally, Dobbertin and Dillon (1999) proved it for even n. In this paper, we investigate a two-faced property of the WG transformation sequences for application in stream ciphers and pseudorandom number generators. One is to present the randomness or unpredictability of the WG transformation sequences. The other is to exhibit the security properties of the WG transformations regarded as Boolean functions. In particular, we prove that the WG transformation sequences, in addition to the known two-level autocorrelation and three-level cross correlation with m-sequences, have the ideal 2-tuple distribution, and large linear span increasing exponentially with n. Moreover, it can be implemented efficiently. This is the first type of pseudorandom sequences with good correlation, statistic properties, large linear span, and efficient implementation. When WG transformations are regarded as Boolean functions, they have high nonlinearity. We derive a criterion for the Boolean representation of WG transformations to be r-resilient and show that they are at least 1-resilient under some basis of the finite field GF (2/sup n/). An algorithm to find such bases is given. The degree and linear span of WG transformations are presented as well.