Programmable wireless environments (PWEs) have emerged as a key paradigm for next-generation communication networks, aiming to transform wireless propagation from an uncontrollable phenomenon into a reconfigurable process that can adapt to diverse service requirements. In this framework, pinching-antenna systems (PASs) have recently been proposed as a promising enabling technology, as they allow the radiation location and effective propagation distance to be adjusted by selectively exciting radiating points along a dielectric waveguide. However, most existing studies on PASs rely on the idealized assumption that pinching-antenna (PA) positions can be continuously adjusted along the waveguide, while realistically only a finite set of pinching locations is available. Motivated by this, this paper analyzes the performance of two-state PASs, where the PA positions are fixed and only their activation state can be controlled. By explicitly accounting for the spatial discreteness of the available pinching points, closed-form analytical expressions for the outage probability and the ergodic achievable data rate are derived. In addition, we introduce the pinching discretization efficiency to quantify the performance gap between discrete and continuous pinching configurations, enabling a direct assessment of the number of PAs required to approximate the ideal continuous case. Finally, numerical results validate the analytical framework and show that near-continuous performance can be achieved with a limited number of PAs, offering useful insights for the design and deployment of PASs in PWEs.

Digital infrastructure is vital for the economy, democracy, and everyday life, yet it is becoming increasingly vulnerable to strategic cyber-attacks. These attacks can lead to significant disruptions, resulting in widespread service outages, financial losses, and a decline in public trust. Ensuring resilience is difficult due to the infrastructure's complexity, the large volume of data involved, and the growing need for quick, coordinated responses. In the EU Horizon project CyberSecDome, we propose a multi-layered framework that provides AI-driven solutions for incident prediction and detection, automated testing, risk assessment, and rapid incident response, supporting continuity amid complex, large-scale cyber threats. Additionally, Cyber-SecDome introduces a virtual reality interface to enhance AI model explainability and provide real-time contextual awareness of ongoing attacks and defense mechanisms. It also enables privacy-aware model sharing across AI systems, fostering secure collaboration among different domes.

Unmanned Aerial Vehicles (UAVs) have become essential in search and rescue operations, especially in disaster management scenarios. Their effective navigation and the integration of a plethora of sensors assist in efficient person detection, making them an essential technological tool to first responders. Multi-UAV systems extend these benefits by using coordinated strategies to cover large areas efficiently, reducing overall mission response time and enhancing its success. Despite these advantages, challenges remain in ensuring the safety, security, and dependability of (mutli-)UAV missions. Issues such as navigation risks, potential cyber threats, and hardware-/software-related reliability issues can impact the mission results. Additionally, UAVs are highly constrained devices with limited battery capacity, requiring the use of lightweight technologies. In this paper, we present part of the results of the SESAME project, an EU multi-partner project that aims to develop safe and secure multi-robot Systems. In particular, we present some of the developed SESAME Executable Digital Dependability Identities (EDDI) technologies based on Markov models, statistical distance measures, and other advanced approaches for enhancing safety, security and dependability of the UAV platform and underlying models. These EDDI technologies are seamlessly integrated using the ConSerts framework in a multi-UAV platform and tested using search and rescue scenarios. The results demonstrate significant improvements in multi-UAV safety, with an availability rate of 91% and a search and rescue algorithmic accuracy of 99.8%. Additionally, the system achieves precise detection of spoofing attacks, using collaborative localization as a mitigation technique to guide the UAV to a safe landing, even in the absence of GPS signals,

The modern web ecosystem relies heavily on the inclusion of third-party scripts as they offer useful, and often necessary, functionality. This inclusion leads to the "blending" of code from different origins, which has significant ramifications. Specifically, the inability to effectively and robustly disambiguate between first-party and embedded third-party scripts can severely undermine the security and privacy guarantees of existing defenses (e.g., blocking trackers or preventing vulnerabilities such as DOM XSS), as well as the validity of web measurement studies. To address that gap we propose StyxJS, a system that is able to provide real-time attribution of third-party scripts while preventing evasive tactics that can be employed by malicious scripts. This is achieved through an automated pipeline consisting of stack walking, script rewriting, browser API overriding, and tamper-proofing mechanisms. Crucially, our system does not require any developer input or prior knowledge about the website and can, thus, be readily incorporated into any countermeasure or web measurement apparatus that requires robust script attribution. We conduct an extensive experimental evaluation of our system and demonstrate that it accurately captures more script inclusion techniques compared to prior work, while incurring a negligible performance overhead, and effectively maintains page-deployed security mechanisms (e.g., CSP). We also detail the straightforward process and benefits of retrofitting a varied set of existing defenses on top of StyxJS, as well as leveraging it to analyze the web ecosystem. We will release our system as an open source project, to allow security researchers and practitioners to benefit from StyxJS’ capabilities.

Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RISes) to actively shape electromagnetic (EM) propagation, enabling advanced control over wireless channels. Beyond improved performance in B5G/6G networks, this control also introduces new security capabilities. Exploiting this, we propose RF-Fencing: a service that selectively suppresses EM signals toward eavesdroppers while preserving reliable communication for legitimate users, thereby significantly enhancing network covertness. Building on that, in this paper, we introduce SHIELD, the first RF-Fencing algorithm that partitions the PWE into Signal Suppression Areas (SSAs) and Signal Delivery Areas (SDAs) through on-the-fly merging of RIS configurations. Extensive EM analysis confirms SHIELD’s effectiveness in preventing wardens from intercepting critical information and achieving covert communications with minimal impact on legitimate users. Moreover, SHIELD can serve also as a jamming-mitigation mechanism and is applicable across various frequency bands and RIS designs.

The evolution toward sixth-generation wireless networks introduces the concept of intelligent and reconfigurable environments designed to support advanced services. Achieving this paradigm shift requires addressing the limitations of traditional wireless systems, particularly their inability to effectively counteract path loss or adapt to diverse user scenarios. Pinching antenna systems (PASs) have emerged as a promising solution, enabling dynamic control over path loss by leveraging dielectric waveguides to support low-loss transmission at high frequencies. This work presents an analytical framework for assessing the reliability of PASs through the derivation of closed-form expressions for the outage probability under both free-space and waveguide attenuation. In addition, a rigorous formulation is provided for the optimal positioning of the pinching antennas to maximize signal reception, taking into account the trade-off between waveguide losses and spatial separation. Simulation results validate the impact of waveguide attenuation on performance and show that PASs consistently outperform conventional architectures in terms of outage behavior, confirming their suitability for next-generation wireless networks.

Programmable Wireless Environments aim to render the communication environment a controllable, software-defined medium. Reconfigurable Intelligent Surfaces (RISes) are the key enabling technology, which can offer the real-time capability to manipulate impinging waves. RISes are expected to be widely deployed in B5G/6G networks to serve a large number of users simultaneously. Despite numerous analyses highlighting the benefits of utilizing previously unexploitable propagation factors through the use of RISes, there is a lack of analysis regarding their relation to the concept of network resource, their allocation to users/stakeholders and their fair pricing. Thus, this paper models RISes as networked resources. Based on this definition, the PRIME algorithm is proposed, the first algorithm for RIS resource allocation and joint pricing. PRIME strives for proportionality between the offered end-user performance level and the corresponding resource pricing, promoting fairness. The algorithm is validated via full-wave electromagnetic simulations and applies to multiple RIS functionalities and frequency bands.

In an era of escalating cyber threats, the imperative for robust and comprehensive cybersecurity measures has never been more pressing. To address this challenge, SYNAPSE presents a pioneering approach by conceptualising, designing, and delivering an Integrated cybersecurity Risk & Resilience Management Platform. The innovation of this platform lies in the integration of key elements, such as situational awareness, incident response, and preparedness (i.e., cyber range), augmented by advanced AI capabilities. Through its holistic approach, SYNAPSE aims to elevate cyber resilience by not only mitigating threats but also fostering a culture of proactive defence, informed decision-making, and collaborative response within organisations and across industries.

In this paper, we present the SECURED project11Funded in part by the European Union (EU), Grant Agreement no. 10109571. Views and opinions expressed are those of the authors and do not necessarily reflect those of the EU or the Health and Digital Executive Agency. Neither the EU nor the granting authority are responsible for them., aimed at improving privacy-preserving processing of data in the health domain. The technologies developed in the project will be demonstrated in four health-related use cases and with the involvement of SME's selected through an open funding call.

Background. A smart contract is a computer program enclosing the terms of a legal agreement between two or more parties which is automatically verified and executed via a computer network called blockchain. Once a smart contract transaction is completed the blockchain is updated and the transaction cannot be changed anymore. This implies that any error codified in the smart contract program cannot be rectified. Therefore, it is of vital importance that developers of smart contracts properly exploit error handling to prevent issues during and after the contract execution. Existing programming languages for smart contracts, support developers in this task by providing a set of Error Handling (EH) features. However, it is unclear the extent to which developers effectively use EH in practice. Aims. Our work aims to fill this gap by empirically investigating the state of practice on the adoption of EH features of one of the most popular programming languages for smart contracts, namely Solidity. Method. We empirically analyse the usage of EH features in 283K unique open-source Solidity smart contracts for the Ethereum blockchain. Results. Our analysis of the documentation of the different versions of Solidity coupled with the empirical evaluation of the EH uses and misuses found in real-word smart contracts, indicate that, among other things, Solidity EH features have been changing frequently across versions, and that the adoption of most of the Solidity EH features has been limited in practice. However, we observe an upward trend in the usage of the require EH feature, which is specifically designed for smart contract development. Conclusions. The insights from our study could help developers improve their EH practice as well as designers of smart contract programming languages to equip their language with appropriate EH features.

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks.

Over the last few years, the adoption of encryption in network traffic has been constantly increasing. The percentage of encrypted communications worldwide is estimated to exceed 90%. Although network encryption protocols mainly aim to secure and protect users' online activities and communications, they have been exploited by malicious entities that hide their presence in the network. It was estimated that in 2022, more than 85% of the malware used encrypted communication channels.

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode-and-forward (DF) protocol, aiming to maximize the network’s minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

The powerful capabilities of modern browsers have pushed the web application logic to the user side, in order to minimize latency, increase scalability of the service and improve users’ quality of experience. What is more, browsers provide a rich toolchest for browser extensions to provide additional functionality, but at the same time enable them to become a powerful vehicle for malicious actors. Such actors may spy, phish or fraud users, thus making the user's browser untrusted for the web servers. In this article, we present WRIT, a practical framework that enables websites to protect critical functionality from abuse in the presence of malicious extensions. In WRIT, the integrity of outgoing web requests is attested and verified to ensure they were triggered by a user's action and not automatically generated by a malicious browser extension. WRIT is immediately applicable by leveraging existing HTML5 and other native browser features and does not require any modification of the browser. Performance results of our prototype show that it adds a negligible 7.29 ms latency to sensitive user-triggered actions (e.g., post message).

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-of-service. In this paper, we analyze the performance of a network supported byzero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRIS-assisted wireless networks are drawn from the presented results.

Modern applications, written in high-level programming languages, enjoy the security benefits of memory and type safety. Unfortunately, even a single memory-unsafe library can wreak havoc on the rest of an otherwise safe application, nullifying all the security guarantees offered by the high-level language and its managed runtime. We perform a study across the Node.js ecosystem to understand the use patterns of binary add-ons. Taking the identified trends into account, we propose a new hybrid permission model aimed at protecting both a binary add-on and its language-specific wrapper. The permission model is applied all around a native add-on and is enforced through a hybrid language-binary scheme that interposes on accesses to sensitive resources from all parts of the native library. We infer the add-on’s permission set automatically over both its binary and JavaScript sides, via a set of novel program analyses. Applied to a wide variety of native add-ons, we show that our framework, BinWrap, reduces access to sensitive resources, defends against real-world exploits, and imposes an overhead that ranges between 0.71%–10.4%.

On 24 February 2022, Russia invaded Ukraine, starting what is now known as the Russo-Ukrainian War, initiating an online discourse on SNs. Twitter one of the most popular SNs, with an open and democratic character, enables a transparent discussion among its large user base. Unfortunately, this often leads to Twitter's policy violations, propaganda, abusive actions, civil integrity violations, and consequently to user accounts' suspension and deletion. This study focuses on the Twitter suspension mechanism and the analysis of shared content and features leading to an accurate machine-learning suspension prediction. Toward this goal, we have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. We extract the categories of shared content of the suspended accounts and explain their characteristics, through the extraction of text embeddings in junction with cosine similarity clustering. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we apply a ML methodology including a SHapley Additive explainability model to understand and explain how user accounts get suspended.

The growing adoption of network encryption protocols, like TLS, has altered the scene of monitoring network traffic. With the advent increase in network encryption, typical DPI systems that monitor network packet payload contents are becoming obsolete, while in the meantime, adversaries abuse the utilization of the TLS protocol to bypass them. In this paper, aiming to understand the botnet ecosystem in the wild, we contact IP addresses known to participate in malicious activities using the JARM tool for active probing. Based on packets acquired from TLS handshakes, server fingerprints are constructed during a time period of 7 months. We investigate if it is feasible to detect suspicious servers and re-identify other similar within blocklists with no prior knowledge of their activities. We show that it is important to update fingerprints often or follow a more effective fingerprinting approach, since the overlapping ratio with legitimate servers rises over time.

Sliding Window Random Linear Network Coding (RLNC) offers a clear path towards achieving ultra-high reliability and low latency at the same time. Such requirements are pivotal for a wide range of applications in the future Internet as well as in 5G and beyond networks. While traditional RLNC has been extensively used for some years now, its Sliding Window flavor is rather recent and extremely promising because of its implementation advantages and the high degree of customization. Probably the most essential parameter of Sliding Window RLNC is the coding depth, i.e., the extent of non-coded packets protected by a coded one. In this work, for the first time, we elaborate on properly choosing the coding depth and shed light on the related trade-offs. We, first, show, experimentally, that significant performance gains can be obtained by fine-tuning the coding depth. Then, we propose and validate an analytical framework that allows us to decide the coding depth based on a channel’s reliability profile. Finally, we introduce a dynamic algorithm that, based on our analytical findings, can improve the performance of sliding window RLNC in the presence of bursts of errors.

With digital technologies now being part of the fabric of our societies, identifying and managing cybersecurity threats becomes imperative. Within the European Union, several initiatives are underway, aiming to motivate, regulate and eventually orchestrate the establishment of capacity and enhancement of situational awareness, incident response, and preparedness capabilities, with an expected emphasis on operators of essential services and state actors entrusted with cybersecurity. In this context, the institution of cooperation and information exchange channels to allow for coordinated cross-border responses to large-scale incidents is particularly prioritized. Motivated by the above, this work presents a conceptual blueprint in support of architecting and establishing interoperable Cyber Security Operations Centres that combine capacity for situational awareness, incident response, and preparedness, also benefiting from the interplay between them, ultimately enhancing national cybersecurity capabilities, cross-border collaboration, and national supervision of their critical sectors, in line with current and upcoming regulatory requirements and the ever-increasing need for national and international cooperation.

Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.

The rising use of FPGAs, in the context of cloud computing, has created security concerns. Previous works have shown that malicious users can implement voltage fluctuation sensors and mount successful power analysis attacks against cryptographic algorithms that share the same Power Distribution Network (PDN). So far, masking and hiding schemes are the two main mitigation strategies against such attacks and previous work has shown that the use of an active fence of Ring Oscillators (ROs) holds the potential for constituting an effective hiding countermeasure if placed between two adversary users. Nevertheless, developing an effective proposition against remote Side-Channel Attacks (SCAs) remains an open research topic. This work presents the mapping of an intra-FPGA adversary scenario on a Xilinx UltraScale+ MPSoC to assess the effectiveness of the Ring Oscillator active fence countermeasure. We compare different active fence configurations, with a varying number of Ring Oscillators, while using a new, resource efficient, activation method aiming at the achievement of noise injection hiding. The results show that by using our active fence scheme, which exhibits lower area overhead and lower power consumption than the algorithm under attack, the side-channel leakage is reduced to such a degree that the amount of traces that need to be collected for a successful attack is more than ten times higher compared to no fence present. Moreover, this work presents qualitative results that FPGA cloud providers can consider in order to assess the benefits gained through the deployment of active fence mechanisms within their platforms for multi-tenant services.

Healthcare ecosystems form a critical type of infrastructures that provide valuable services in today societies. However, the underlying sensitive information is also of interest of malicious entities around the globe, with the attack volume being continuously increasing. Safeguarding this complex computerized setting constitutes a major challenge for the involved organizations. This paper presents an incident handling system for healthcare organizations and their supply-chain. The proposed approach utilizes swarm intelligence in order to assess the current security posture in a continuous basis and respond to attacks in real-time. The overall solution is based on the related NIST 800.61 standard and implements the operations of i) preparation, ii) detection and analysis, iii) containment, eradication, and recovery, and iv) post-incident activity. The system is developed under the EU funded project AI4HEALTHSEC and is applied in the relevant healthcare pilots.

Software-Defined Metasurfaces (SDMs) have ad-vanced from basic theoretical ideas to a key enabler for next generation wireless systems. When massively deployed in a wireless environment, they can realize precise, software-defined propagation of signals and wireless channel customizations. A plethora of studies have thoroughly investigated the physical design of the SDMs and their wireless channel engineering capa-bilities. This work introduces a novel architecture and associated processes for integrating SDMs into existing networked wireless systems. First, the integration architecture and the according complex underlying physics of the SDMs are presented. Then the software abstractions are proposed that enable the interaction of the surfaces in a physics-agnostic manner. These software abstractions require an SDM profile to operate in real-time, i.e., a database that accurately describes the capabilities of a given metasurface. Thus, the manufacturing time workflows are suggested to produce such a profile. The proposed solutions are evaluated in an actual physical setup/testbed.

Programmable wireless environments (PWEs) utilize internetworked intelligent metasurfaces to transform wireless propagation into a software-controlled resource. In this article, the interplay is explored between the user devices, the metasurfaces, and the PWE control system from the theory to the end-to-end implementation. This article first discusses the metasurface hardware and software, covering the complete workflow from the user device initialization to its final service via the PWE. Furthermore, to be compatible with the 5G and 6G wireless systems, the software-defined networking (SDN) paradigm is extended to achieve scalable internetworking and central control in PWE deployments with multiple metasurfaces and multihop communication. Subsequently, the set of SDN foundations is exploited in order to abstract the physics behind PWEs and a theoretical framework is established to describe and manipulate them in an algorithmic form. This can lead to smart radio environments that are readily accessible from various engineering disciplines, facilitating their integration into existing networks, wireless systems, and applications. This article is concluded by outlining strategies for the optimal placement of metasurfaces within a PWE-controlled space, open challenges in PWE security, specialized SDN integration issues, and theoretical problems toward the graph-driven modeling of PWEs.

Mobile sensors have transformed how users interact with modern smartphones and enhance their overall experience. However, the absence of sufficient access control for protecting these sensors enables a plethora of threats. As prior work has shown, malicious apps and sites can deploy a wide range of attacks that use data captured from sensors. Unfortunately, as we demonstrate, in the modern app ecosystem where most apps fetch and render third-party web content, attackers can use ads for delivering attacks. In this paper, we introduce a novel attack vector that misuses the advertising ecosystem for delivering sophisticated and stealthy attacks that leverage mobile sensors. These attacks do not depend on any special app permissions or specific user actions, and affect all Android apps that contain in-app advertisements due to the improper access control of sensor data in WebView. We outline how motion sensor data can be used to infer users' sensitive touch input (e.g., credit card information) in two distinct attack scenarios, namely intra-app and inter-app data exfiltration. While the former targets the app displaying the ad, the latter affects every other Android app running on the device. To make matters worse, we have uncovered serious flaws in Android's app isolation, life cycle management, and access control mechanisms that enable persistent data exfiltration even after the app showing the ad is moved to the background or terminated by the user. Furthermore, as in-app ads can "piggyback" on the permissions intended for the app's core functionality, they can also obtain information from protected sensors such as the camera, microphone and GPS. To provide a comprehensive assessment of this emerging threat, we conduct a large-scale, end-to-end, dynamic analysis of ads shown in apps available in the official Android Play Store. Our study reveals that ads in the wild are already accessing and leaking data obtained from motion sensors, thus highlighting the need for stricter access control policies and isolation mechanisms.

Third-party libraries ease the software development process and thus have become an integral part of modern software engineering. Unfortunately, they are not usually vetted by human developers and thus are often responsible for introducing bugs, vulnerabilities, or attacks to programs that will eventually reach end-users. In this demonstration, we present a combined static and dynamic program analysis for inferring and enforcing third-party library permissions in server-side JavaScript. This analysis is centered around a RWX permission system across library boundaries. We demonstrate that our tools can detect zero-day vulnerabilities injected into popular libraries and often missed by state-of-the-art tools such as snyk test and npm audit.

Botnets constitute nowadays one of the most dangerous security threats worldwide. High volumes of infected machines are controlled by a malicious entity and perform coordinated cyber-attacks. The problem will become even worse in the era of the Internet of Things (IoT) as the number of insecure devices is going to be exponentially increased. This paper presents WARDOG - an awareness and digital forensic system that informs the end-user of the botnet's infection, exposes the botnet infrastructure, and captures verifiable data that can be utilized in a court of law. The responsible authority gathers all information and automatically generates a unitary documentation for the case. The document contains undisputed forensic information, tracking all involved parties and their role in the attack. The deployed security mechanisms and the overall administration setting ensures non-repudiation of performed actions and enforces accountability. The provided properties are verified through theoretic analysis. In simulated environment, the effectiveness of the proposed solution, in mitigating the botnet operations, is also tested against real attack strategies that have been captured by the FORTHcert honeypots, overcoming state-of-the-art solutions. Moreover, a preliminary version is implemented in real computers and IoT devices, highlighting the low computational/communicational overheads of WARDOG in the field.

The number of applications and services that are hosted on cloud platforms is constantly increasing. Nowadays, more and more applications are hosted as services on cloud platforms, co-existing with other services in a mutually untrusted environment. Facilities such as virtual machines, containers and encrypted communication channels aim to offer isolation between the various applications and protect sensitive user data. However, such techniques are not always able to provide a secure execution environment for sensitive applications nor they offer guarantees that data are not monitored by an honest but curious provider once they reach the cloud infrastructure. The recent advancements of trusted execution environments within commodity processors, such as Intel SGX, provide a secure reverse sandbox, where code and data are isolated even from the underlying operating system. Moreover, Intel SGX provides a remote attestation mechanism, allowing the communicating parties to verify their identity as well as prove that code is executed on hardware-assisted software enclaves. Many approaches try to ensure code and data integrity, as well as enforce channel encryption schemes such as TLS, however, these techniques are not enough to achieve complete isolation and secure communications without hardware assistance or are not efficient in terms of performance. In this work, we design and implement a practical attestation system that allows the service provider to offer a seamless attestation service between the hosted applications and the end clients. Furthermore, we implement a novel caching system that is capable to eliminate the latencies introduced by the remote attestation process. Our approach allows the parties to attest one another before each communication attempt, with improved performance when compared to a standard TLS handshake.

In this paper, we focus on authentication and authorization flaws in web apps that enable partial or full access to user accounts. Specifically, we develop a novel fully automated black-box auditing framework that analyzes web apps by exploring their susceptibility to various cookie-hijacking attacks while also assessing their deployment of pertinent security mechanisms (e.g., HSTS). Our modular framework is driven by a custom browser automation tool developed to transparently offer fault-tolerance during extended interactions with web apps. We use our framework to conduct the first automated large-scale study of cookie-based account hijacking in the wild. As our framework handles every step of the auditing process in a completely automated manner, including the challenging process of account creation, we are able to fully audit 25K domains. Our framework detects more than 10K domains that expose authentication cookies over unencrypted connections, and over 5K domains that do not protect authentication cookies from JavaScript access while also embedding third party scripts that execute in the first party's origin. Our system also automatically identifies the privacy loss caused by exposed cookies and detects 9,324 domains where sensitive user data can be accessed by attackers (e.g., address, phone number, password). Overall, our study demonstrates that cookie-hijacking is a severe and prevalent threat, as deployment of even basic countermeasures (e.g., cookie security flags) is absent or incomplete, while developers struggle to correctly deploy more demanding mechanisms.

While the number of connected devices is constantly growing, we observe an increased incident rate of cyber attacks that target user data. Typically, personal devices contain the most sensitive information regarding their users, so there is no doubt that they can be a very valuable target for adversaries. Typical defense solution to safeguard user devices and data, are based in malware analysis mechanisms. To amortize the processing and maintenance overheads, the outsourcing of network inspection mechanisms to the cloud has become very popular recently. However, the majority of such cloud-based applications usually offers limited privacy preserving guarantees for data processing in third-party environments. In this work, we propose TrustAV, a practical cloud-based malware detection solution destined for a plethora of device types. TrustAV is able to offload the processing of malware analysis to a remote server, where it is executed entirely inside, hardware supported, secure enclaves. By doing so, TrustAV is capable to shield the transfer and processing of user data even in untrusted environments with tolerable performance overheads, ensuring that private user data are never exposed to malicious entities or honest-but-curious providers. TrustAV also utilizes various techniques in order to overcome performance overheads, introduced by the Intel SGX technology, and reduce the required enclave memory --a limiting factor for malware analysis executed in secure enclave environments-- offering up to 3x better performance.

Programmable wireless environments (PWEs) utilize electromagnetic metasurfaces to transform wireless propagation into a software-controlled resource. In this work we study the effects of user device mobility on the efficiency of PWEs. An analytical model is proposed, which describes the potential misalignment between user-emitted waves and the active PWE configuration, and can constitute the basis for studying queuing problems in PWEs. Subsequently, a novel, beam steering approach is proposed which can effectively mitigate the misalignment effects. Ray-tracing-based simulations evaluate the proposed scheme.

Modern commodity computing systems are composed of a number of heterogeneous processing units, each one with its own unique performance and energy characteristics. However, the majority of current network packet processing frameworks targets only one device (either the CPU or an accelerator), leaving the remaining computational resources underutilized or even idle. In this paper, we propose an adaptive scheduling approach for network packet processing applications that exploits any heterogeneous architecture that can be found in a commodity high-end hardware setup. Our scheduler not only distributes the workloads to the appropriate devices in the system to achieve the desired performance results, but also enables the multiplexing of diverse, concurrently executed network packet processing applications, eliminating the interference effects introduced at run-time. The evaluation results show that our scheduler is able to tackle any interference in the shared hardware resources as well to respond quickly to dynamic fluctuations (e.g., application overloads, traffic bursts, infrastructural changes, etc.) that may occur at real time.

Instruction Set Randomization (ISR) is able to protect against remote code injection attacks by randomizing the instruction set of each process. Thereby, even if an attacker succeeds to inject code, it will fail to execute on the randomized processor. The majority of existing ISR implementations is based on emulators and binary instrumentation tools that unfortunately: (i) incur significant runtime performance overheads, (ii) limit the ease of deployment, (iii) cannot protect the underlying operating system kernel, and (iv) are vulnerable to evasion attempts that bypass the ISR protection itself. To address these issues, we present the design and implementation of ASIST, an architecture with both hardware and operating system support for ISR. ASIST uses our extended SPARC processor that is mapped onto a FPGA board and runs our modified Linux kernel to support the new features. In particular, before executing a new user-level process, the operating system loads its randomization key into a newly defined register, and the modified processor decodes the process’s instructions with this key. Besides that, ASIST uses a separate randomization key for the operating system to protect the base system against attacks that exploit kernel vulnerabilities to run arbitrary code with elevated privileges. Our evaluation shows that ASIST can transparently protect both user-land applications and the operating system kernel from code injection and code reuse attacks, with about 1.5% runtime overhead when using simple encryption schemes, such as XOR and Transposition; more secure ciphers, such as AES, even though they are much more complicated for mapping them to hardware, they are still within acceptable margins,with approximately 10% runtime overhead, when efficiently leveraging the spatial locality of code through modern instruction cache configurations.

Intelligent surfaces exert deterministic control over the wireless propagation phenomenon, enabling novel capabilities in performance, security and wireless power transfer. Such surfaces come in the form of rectangular tiles that cascade to cover large surfaces such as walls, ceilings or building facades. Each tile is addressable and can receive software commands from a controller, manipulating an impinging electromagnetic wave upon it by customizing its reflection direction, focus, polarization and phase. A new problem arises concerning the orchestration of a set of tiles towards serving end-to-end communication objectives. Towards that end, we propose a novel intelligent surface networking algorithm based on interpretable neural networks. Tiles are mapped to neural network nodes and any tile line-of-sight connectivity is expressed as a neural network link. Tile wave manipulation functionalities are captured via geometric reflection with virtually rotatable tile surface norm, thus being able to tunable distribute power impinging upon a tile over the corresponding neural network links, with the corresponding power parts acting as the link weights. A feedforward/backpropagate process optimizes these weights to match ideal propagation outcomes (normalized network power outputs) to wireless user emissions (normalized network power inputs). An interpretation process translates these weights to the corresponding tile wave manipulation functionalities.

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy.

Modern smartphone sensors can be leveraged for providing novel functionality and greatly improving the user experience. However, sensor data can be misused by privacy-invasive or malicious entities. Additionally, a wide range of other attacks that use mobile sensor data have been demonstrated; while those attacks have typically relied on users installing malicious apps, browsers have eliminated that constraint with the deployment of HTML5 WebAPI. In this article, we conduct a comprehensive evaluation of the multifaceted threat that mobile web browsing poses to users by conducting a large-scale study of mobile-specific HTML5 WebAPI calls across more than 183K of the most popular websites. We build a novel testing infrastructure consisting of actual smartphones on top of a dynamic Android app analysis framework, allowing us to conduct an end-to-end exploration. In detail, our system intercepts and tracks data access in real time, from the WebAPI JavaScript calls down to the Android system calls. Our study reveals the extent to which websites are actively leveraging the WebAPI for collecting sensor data, with 2.89% of websites accessing at least one sensor. To provide a comprehensive assessment of the risks of this emerging practice, we create a taxonomy of sensor-based attacks from prior studies and present an in-depth analysis by framing our collected data within that taxonomy. We find that 1.63% of websites can carry out at least one attack and emphasize the need for a standardized policy across all browsers and the ability for users to control what sensor data each website can access.

Security solutions for cloud applications usually exploit security tools as is by utilising their default configuration. On one hand, this can lead to a waste of resources. On the other hand, it can also lead to not properly protecting the different application components based on their diverse security requirements. To this end, this paper proposes a security solution for cross-cloud applications which is configurable according to the flexible configuration specification given by the devops. Such a specification conforms to a certain UML-based meta-model and is independent of the underlying security tools exploited. In this way, devops can enable to produce a varied security level per each application component that better suits its security requirements. We demonstrate the suitability of our solution through an evaluation showcasing that it can lead to reduced resource consumption without compromising the security of the components that it protects.

Android's app ecosystem relies heavily on third-party libraries as they facilitate code development and provide a steady stream of revenue for developers. However, while Android has moved towards a more fine-grained run time permission system, users currently lack the required resources for deciding whether a specific permission request is actually intended for the app itself or is requested by possibly dangerous third-party libraries. In this paper we present Reaper, a novel dynamic analysis system that traces the permissions requested by apps in real time and distinguishes those requested by the app's core functionality from those requested by third-party libraries linked with the app. We implement a sophisticated UI automator and conduct an extensive evaluation of our system's performance and find that Reaper introduces negligible overhead, rendering it suitable both for end users (by integrating it in the OS) and for deployment as part of an official app vetting process. Our study on over 5K popular apps demonstrates the large extent to which personally identifiable information is being accessed by libraries and highlights the privacy risks that users face. We find that an impressive 65% of the permissions requested do not originate from the core app but are issued by linked third-party libraries, 37.3% of which are used for functionality related to ads, tracking, and analytics. Overall, Reaper enhances the functionality of Android's run time permission model without requiring OS or app modifications, and provides the necessary contextual information that can enable users to selectively deny permissions that are not part of an app's core functionality.

The growth of the global middle class provokes significant increment in product consumption. As the available resources are limited, Circular Economy (CE) raises as a promising initiative towards the sustainable development. Except from the traditional approaches of reusing or recycling products, the current trend utilizes modern computer technologies and involves a data-driven aspect. The Internet of Things (IoT) is the main enabler for the integration of CE with technology. This paper proposes a framework for implementing the cooperative vision of CE and IoT. Via this solution, a pilot system is developed in a medium size telecommunication company for administrating the lifecycle of the deployed electronic equipment and the management of the related supply chains. Mechanisms and devices are maintained/repaired/fabricated in a regular basis, green computing techniques are efficiently applied, and the productive period is prolonged. When the business upgrades the system, the retired counterparts can be sold in start-ups or gifted in third-world countries. The overall approach extends the working period of the well-maintained electronic assets not only for the examined business but for the collaborating organizations as well. Recycling companies can then trace this supply chain and the assets' status in order to define their investment strategy at the end-consumer, contributing in the reduction of the electronic waste problem in the third-world.

Day-by-day modern circular economy (CE) models gain ground and penetrate the traditional business sectors. The Internet of Medical Things (IoMT) is the main enabler for this interplay of CE with healthcare. Novel services, like remote sensing, assisting of elder people, and e-visit, enhance the people's health and convenience, while reducing the per-patient cost for the medical institutions. However, the rise of mobile, wearable, and telemedicine solutions means that security can no longer be examined within the neat, physical walls as it was considered before. The problem for a healthcare system further increases as the Bring Your Own Device (BYOD) reality, affects the way that the health services are accommodated nowadays. Both patients and healthcare staff utilize their personal devices (e.g. smart phones or tablets) in order to access, deliver, and process medical data. As the IoMT is materialized and the underlying devices maintain so valuable data, they become a popular target for ransomware and other attacks. In the CE case, the problem is further emerging as several of these assets can be used over-and-over by many actuators. However, medical users and vendors are less aware of the underlying vulnerabilities and spend less on the IoMT security. Nevertheless, the risk from exploiting vulnerabilities can be drastically reduced when the known and relevant controls are placed. This paper presents an overview of the core security and privacy controls that must be deployed in modern IoMT settings in order to safeguard the involved users and stakeholders. The overall approach can be considered as a best-practices guide towards the safe implementation of IoMT systems, featuring CE.

The traditional cycle of industrial products has been linear since its inception. Raw resources are acquired, processed, distributed, used and ultimately disposed of. This linearity has led to a dangerously low efficiency degree in resource use, and has brought forth serious concerns for the viability of our natural ecosystem. Circular economy is introducing a circular workflow for the lifetime of products. It generalizes the disposal phase, reconnecting it to manufacturing, distribution and end-use, thus limiting true deposition to the environment. This process has not been extended so far to software. Nonetheless, the development of software follows the same phases, and also entails the use-and waste-of considerable resources. This include human effort, as well as human and infrastructure sustenance products such as food, traveling and energy. This paper introduces circular economy principles to the software development, and particularly to network management logic and security. It employs a recently proposed concept-the Socket Store-which is an online store distributing end-user network logic in modular form. The Store modules act as mediators between the end-user network logic and the network resources. It is shown that the Socket Store can implement all circular economy principles to the software life-cycle, with considerable gains in resource waste.

Programmable, intelligent surfaces can manipulate electromagnetic waves impinging upon them, producing arbitrarily shaped reflection, refraction and diffraction, to the benefit of wireless users. Moreover, in their recent form of HyperSurfaces, they have acquired inter-networking capabilities, enabling the Internet of Material Properties with immense potential in wireless communications. However, as with any system with inputs and outputs, accurate sensing of the impinging wave attributes is imperative for programming HyperSurfaces to obtain a required response. Related solutions include field nano-sensors embedded within HyperSurfaces to perform minute measurements over the area of the HyperSurface, as well as external sensing systems. The present work proposes a sensing system that can operate without such additional hardware. The novel scheme programs the HyperSurface to perform compressed sensing of the impinging wave via simple one-antenna power measurements. The HyperSurface can jointly be programmed for both wave sensing and wave manipulation duties at the same time. Evaluation via simulations validates the concept and highlight its promising potential.

Interoperability stands for the capacity of a system to interact with the units of another entity. Although it is quite easy to accomplish this within the products of the same brand, it is not facile to provide compatibility for the whole spectrum of the Internet-of-Things (IoT) and the Linked Data (LD) world. Currently, the different applications and devices operate in their own cloud/platform, without supporting sufficient interaction with different vendor-products. As it concerns the meaning of data, which is the main focus of this paper, semantics can settle commonly agreed information models and ontologies for the used terms. However, as there are several ontologies for describing each distinct 'Thing', we need Semantic Mediators (SMs) in order to perform common data mapping across the various utilized formats (i.e. XML or JSON) and ontology alignment (e.g. resolve conflicts). Our goal is to enable end-to-end vertical compatibility and horizontal cooperation at all levels (field/network/backend). Moreover, the implication of security must be taken into consideration as the unsafe adoption of semantic technologies exposes the linking data and the user's privacy, issues that are neglected by the majority of the semantic-web studies. A motivating example of smart sensing is described along with a preliminary implementation on real heterogeneous devices. Two different IoT platforms are integrating in the case study, detailing the main SM features. The proposed setting is secure, scalable, and the overall overhead is sufficient for runtime operation, while providing significant advances over state-of-the-art solutions.

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

Smartphone sensors can be leveraged by malicious apps for a plethora of different attacks, which can also be deployed by malicious websites through the HTML5 WebAPI. In this paper we provide a comprehensive evaluation of the multifaceted threat that mobile web browsing poses to users, by conducting a large-scale study of mobile-specific HTML5 WebAPI calls used in the wild. We build a novel testing infrastructure consisting of actual smartphones on top of a dynamic Android app analysis framework, allowing us to conduct an end-to-end exploration. Our study reveals the extent to which websites are actively leveraging the WebAPI for collecting sensor data, with 2.89% of websites accessing at least one mobile sensor. To provide a comprehensive assessment of the potential risks of this emerging practice, we create a taxonomy of sensor-based attacks from prior studies, and present an in-depth analysis by framing our collected data within that taxonomy. We find that 1.63% of websites could carry out at least one of those attacks. Our findings emphasize the need for a standardized policy across browsers and the ability for users to control what sensor data each website can access.

Programmable wireless environments enable the software-defined propagation of waves within them, yielding exceptional performance. Several building-block technologies have been implemented and evaluated at the physical layer in the past. The present work contributes a network-layer solution to configure such environments for multiple users and objectives, and for any underlying physical-layer technology. Supported objectives include any combination of Quality of Service and power transfer optimization, eavesdropping, and Doppler effect mitigation, in multi-cast or uni-cast settings. In addition, a graph-based model of programmable environments is proposed, which incorporates core physical observations and efficiently separates physical and networking concerns. The evaluation takes place in a specially developed simulation tool, and in a variety of environments, validating the model and reaching insights into the user capacity of programmable environments.

Wireless communication environments are unaware of the ongoing data exchange efforts within them. Moreover, their effect on the communication quality is intractable in all but the simplest cases. The present work proposes a new paradigm, where indoor scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Moreover, the controlled scattering can surpass natural behavior, exemplary overriding Snell's law, reflecting waves towards any custom angle (including negative ones). Thus, path loss and multi-path fading effects can be controlled and mitigated. The core technology of this new paradigm are metasurfaces, planar artificial structures whose effect on impinging electromagnetic waves is fully defined by their macro-structure. The present study contributes the software-programmable wireless environment model, consisting of several HyperSurface tiles controlled by a central, environment configuration server. HyperSurfaces are a novel class of metasurfaces whose structure and, hence, electromagnetic behavior can be altered and controlled via a software interface. Multiple networked tiles coat indoor objects, allowing fine-grained, customizable reflection, absorption or polarization overall. A central server calculates and deploys the optimal electromagnetic interaction per tile, to the benefit of communicating devices. Realistic simulations using full 3D ray-tracing demonstrate the groundbreaking potential of the proposed approach in 2.4GHz and 60GHz frequencies.

New strains of distributed denial-of-service (DDoS) attacks have exhibited potential to disconnect communication networks, even cutting off entire countries from the Internet. The “crossfire” is a new, indirect DDoS link-flooding attack, which masks itself as natural congestion, making it very hard to counter. Several studies have proposed online attack detection schemes, whose efficiency has been shown to vary in different network topologies. However, the topology/detection relation has been studied qualitatively, without formal proof or quantification metric. This paper is motivated by the fact that network topology changes are generally expensive and slow. Therefore, network designers should be provided with means of evaluating the effects of topology modifications to the attack detection efficiency. This paper fills this gap by contributing a formal proof for the topology-detection efficiency relation, as well as a novel off-line metric that quantifies it. Full attack prototypes are implemented and evaluated in real-Internet topologies, validating the analytical findings. It is shown that the novel metric expresses the topology-detection relation efficiently, while existing and widely used metrics do not constitute good choices for this task.

Content selection in social networks is driven by numerous extraneous factors that can result in the loss of content of interest. In this paper we present Reveal, a fine-grained recommender system for social networks, designed to recommend media content posted by the user's friends. The intuition is to leverage the abundance of pre-existing information and identify overlapping user interests in specific sub-categories. While our system is intended as a component of the social network, we develop a proof-of-concept implementation for Facebook and experimentally evaluate the effectiveness of our approach.

According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience.

A developer of mobile or desktop applications is responsible for implementing the network logic of his software. Nonetheless: i) Developers are not network specialists, while pressure for emphasis on the visible application parts places the network logic out of the coding focus. Moreover, computer networks undergo evolution at paces that developers may not follow. ii) From the network resource provider point of view, marketing novel services and involving a broad audience is also challenge for the same reason. Moreover, the objectives of end-user networking logic are neither clear nor uniform. This constitutes the central optimization of network resources an additional challenge. As a solution to these problems, we propose the Socket Store. The Store is a marketplace containing end-user network logic in modular form. The Store modules act as intelligent mediators between the end-user and the network resources. Each module has a clear, specialized objective, such as connecting two clients over the Internet while avoiding transit networks suspicious for eavesdropping. The Store is populated and peer-reviewed by network specialists, whose motive is the visibility, practical applicability and monetization potential of their work. A developer first purchases access to a given socket module. Subsequently, he incorporates it to his applications under development, obtaining state-of-the-art performance with trivial coding burden. A full Store prototype is implemented and a critical data streaming module is evaluated as a driving case.

The vast majority of online services nowadays, provide both a mobile friendly website and a mobile application to their users. Both of these choices are usually released for free, with their developers, usually gaining revenue by allowing advertisements from ad networks to be embedded into their content. In order to provide more personalized and thus more effective advertisements, ad networks usually deploy pervasive user tracking, raising this way significant privacy concerns. As a consequence, the users do not have to think only their convenience before deciding which choice to use while accessing a service: web or app, but also which one harms their privacy the least.

Heterogeneous and asymmetric computing systems are composed by a set of different processing units, each with its own unique performance and energy characteristics. Still, the majority of current network packet processing frameworks targets only a single device (the CPU or some accelerator), leaving the rest processing resources unused and idle. In this paper, we propose an adaptive scheduling approach that supports the heterogeneous and asymmetric hardware, tailored for network packet processing applications. Our scheduler is able to respond quickly to dynamic performance fluctuations that occur at real time, such as traffic bursts, application overloads, and system changes. The experimental results show that our system is able to match the peak throughput of a diverse set of packet processing applications, while consuming up to $3.5\times$ less energy.

Graphics processing units (GPUs) are a powerful platform for building the high-speed network traffic processing applications using low-cost hardware. The existing systems tap the massively parallel architecture of GPUs to speed up certain computationally intensive tasks, such as cryptographic operations and pattern matching. However, they still suffer from significant overheads due to critical-path operations that are still being carried out on the CPU, and redundant inter-device data transfers. In this paper, we present GASPP, a programmable network traffic processing framework tailored to modern graphics processors. GASPP integrates optimized GPU-based implementations of a broad range of operations commonly used in the network traffic processing applications, including the first purely GPU-based implementation of network flow tracking and TCP stream reassembly. GASPP also employs novel mechanisms for tackling the control flow irregularities across SIMT threads, and for sharing the memory context between the network interfaces and the GPU. Our evaluation shows that GASPP can achieve multigigabit traffic forwarding rates even for complex and computationally intensive network operations, such as stateful traffic classification, intrusion detection, and packet encryption. Especially when consolidating multiple network applications on the same system, GASPP achieves up to 16.2× speedup compared with different monolithic GPU-based implementations of the same applications.

Today, a considerable proportion of the public political discourse that proceeds nationwide elections is happening through Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis and sarcasm correction. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.

Control-flow hijacking is the principal method for code-reuse techniques like Return-oriented Programming (ROP) and Jump-oriented Programming (JOP). For defending against such attacks, the community has proposed Control-flow Integrity (CFI), a technique capable of preventing exploitation by verifying that every (indirect) control-flow transfer points to a legitimate address. Enabling CFI in real systems is not straightforward, since in many cases the actual Control-flow Graph (CFG) of a program can be only approximated. Even in the case that there is perfect knowledge of the CFG, ensuring that all return instructions will return to their actual call sites, without employing a shadow stack, is questionable. On the other hand, the community has expressed concerns related to significant overheads stemming from enabling a shadow stack.

Nanonetworks comprise numerous wireless nodes, assembled at micro-to-nano scale. The unique manufacturing challenges and cost considerations of these networks make for minimal complexity solutions at all network layers. From a networking aspect, packet retransmissions should be kept minimal, while ensuring communication between any two nanonodes. In addition, assigning unique addresses to nanonodes is not straightforward, since it can entail a prohibitively high number of packet exchanges. Thus, efficient data routing is considered an open issue in nanonetworking. The present paper proposes a routing system which can be dynamically deployed within a nanonetwork. Static, dense topologies with numerous, identical nodes are examined. These attributes are especially important in the context of recently proposed applications of nanonetworks. The proposed scheme incurs a trivial setup overhead and requires integer processing capabilities only. Once deployed, it operates efficiently, inducing lower packet retransmission rates than related schemes.

The capabilities of modern devices, coupled with the almost ubiquitous availability of Internet connectivity, have resulted in photos being shared online at an unprecedented scale. This is further amplified by the popularity of social networks and the immediacy they offer in content sharing. Existing access control mechanisms are too coarse-grained to handle cases of conflicting interests between the users associated with a photo; stories of embarrassing or inappropriate photos being widely accessible have become quite common. In this paper, we propose to rethink access control when applied to photos, in a way that allows us to effectively prevent unwanted individuals from recognizing users in a photo. The core concept behind our approach is to change the granularity of access control from the level of the photo to that of a user's personally identifiable information (PII). In this work, we consider the face as the PII. When another user attempts to access a photo, the system determines which faces the user does not have the permission to view, and presents the photo with the restricted faces blurred out. Our system takes advantage of the existing face recognition functionality of social networks, and can interoperate with the current photo-level access control mechanisms. We implement a proof-of-concept application for Facebook, and demonstrate that the performance overhead of our approach is minimal. We also conduct a user study to evaluate the privacy offered by our approach, and find that it effectively prevents users from identifying their contacts in 87.35% of the restricted photos. Finally, our study reveals the misconceptions about the privacy offered by existing mechanisms, and demonstrates that users are positive towards the adoption of an intuitive, straightforward access control mechanism that allows them to manage the visibility of their face in published photos.

Accessing the large volume of information that is available on the Web is more important than ever before. Search engines are the primary means to help users find the content they need. To suggest the most closely related and the most popular Web pages for a user's query, search engines assign a ranking to each Web page, which typically increases with the number and ranking of other Web sites that link to this page. However, link spammers have developed several techniques to exploit this algorithm and improve the ranking of their Web pages. These techniques are commonly based on underground forums for collaborative link exchange; building a relationship network among spammers to favor their Web pages in search engine results. In this study, we provide a systematic analysis of the spam link exchange performed through 15 Search Engine Optimization (SEO) forums. We design a system, which is able to capture the activity of link spammers in SEO forums, identify spam link exchange, and visualize the link spam ecosystem. The outcomes of this study shed light on a different aspect of link spamming that is the collaboration among spammers.

Heterogeneous and asymmetric computing systems are composed by a set of different processing units, each with its own unique performance and energy characteristics. Still, the majority of current network packet processing frameworks targets only a single device (the CPU or some accelerator), leaving other processing resources idle. In this paper, we propose an adaptive scheduling approach that supports heterogeneous and asymmetric hardware, tailored for network packet processing applications. Our scheduler is able to respond quickly to dynamic performance fluctuations that occur at real-time, such as traffic bursts, application overloads and system changes. The experimental results show that our system is able to match the peak throughput of a diverse set of packet processing workloads, while consuming up to 3.5x less energy.

In an effort to hinder attackers from compromising user accounts, Facebook launched a form of two-factor authentication called social authentication (SA), where users are required to identify photos of their friends to complete a log-in attempt. Recent research, however, demonstrated that attackers can bypass the mechanism by employing face recognition software. Here we demonstrate an alternative attack. that employs image comparison techniques to identify the SA photos within an offline collection of the users' photos.

Protecting the confidentiality of cryptographic keys in the event of partial or full system compromise is crucial for containing the impact of attacks. The Heartbleed vulnerability of April 2014, which allowed the remote leakage of secret keys from HTTPS web servers, is an indicative example. In this paper we present PixelVault, a system for keeping cryptographic keys and carrying out cryptographic operations exclusively on the GPU, which allows it to protect secret keys from leakage even in the event of full system compromise. This is possible by exposing secret keys only in GPU registers, keeping PixelVault's critical code in the GPU instruction cache, and preventing any access to both of them from the host. Due to the non-preemptive execution mode of the GPU, an adversary that has full control of the host cannot tamper with PixelVault's GPU code, but only terminate it, in which case all sensitive data is lost. We have implemented a PixelVault-enabled version of the OpenSSL library that allows the protection of existing applications with minimal modifications. Based on the results of our evaluation, PixelVault not only provides secure key storage using commodity hardware, but also significantly speeds up the processing throughput of cryptographic operations for server applications.

Distributed key-value and always-in-memory store is employed by large and demanding services, such as Facebook and Amazon. It is apparent that generic implementations of such caches can not meet the needs of every application, therefore further research for optimizing or speeding up cache operations is required. In this paper, we present an incremental optimization strategy for accelerating the most popular key-value store, namely memcached. First we accelerate the computational unit by utilizing commodity GPUs, which offer a significant performance increase on the CPU-bound part of memcached, but only moderate performance increase under intensive I/O. We then proceed to improve I/O performance by replacing TCP with a fast UDP implementation in user-space. Putting it all together, GPUs for computational operations instead of CPUs, and UDP for communication instead of TCP, we are able to experimentally achieve 20 Gbps line-rate, which significantly outperforms the original implementation of memcached.

Code injection attacks continue to pose a threat to today's computing systems, as they exploit software vulnerabilities to inject and execute arbitrary, malicious code. Instruction Set Randomization (ISR) is able to protect a system against remote machine code injection attacks by randomizing the instruction set of each process. This way, the attacker will inject invalid code that will fail to execute on the randomized processor. However, all the existing implementations of ISR are based on emulators and binary instrumentation tools that (i) incur a significant runtime performance overhead, (ii) limit the ease of deployment of ISR, (iii) cannot protect the underlying operating system kernel, and (iv) are vulnerable to evasion attempts trying to bypass ISR protection.

Two-factor authentication is widely used by high-value services to prevent adversaries from compromising accounts using stolen credentials. Facebook has recently released a two-factor authentication mechanism, referred to as Social Authentication, which requires users to identify some of their friends in randomly selected photos. A recent study has provided a formal analysis of social authentication weaknesses against attackers inside the victim's social circles. In this paper, we extend the threat model and study the attack surface of social authentication in practice, and show how any attacker can obtain the information needed to solve the challenges presented by Facebook. We implement a proof-of-concept system that utilizes widely available face recognition software and cloud services, and evaluate it using real public data collected from Facebook. Under the assumptions of Facebook's threat model, our results show that an attacker can obtain access to (sensitive) information for at least 42% of a user's friends that Facebook uses to generate social authentication challenges. By relying solely on publicly accessible information, a casual attacker can solve 22% of the social authentication tests in an automated fashion, and gain a significant advantage for an additional 56% of the tests, as opposed to just guessing. Additionally, we simulate the scenario of a determined attacker placing himself inside the victim's social circle by employing dummy accounts. In this case, the accuracy of our attack greatly increases and reaches 100% when 120 faces per friend are accessible by the attacker, even though it is very accurate with as little as 10 faces.

We collected more than ten million Microsoft Office documents from public websites, analyzed the metadata stored in each document and extracted information related to social activities. Our analysis revealed the existence of exactly identified cliques of users that edit, revise and collaborate on industrial and military content. We also examined cliques in documents downloaded from Fortune-500 company websites. We constructed their graphs and measured their properties. The graphs contained many connected components and presented social properties. The a priori knowledge of a company's social graph may significantly assist an adversary to launch targeted attacks, such as targeted advertisements and phishing emails. Our study demonstrates the privacy risks associated with metadata by cross-correlating all members identified in a clique with users of Twitter. We show that it is possible to match authors collaborating in the creation of a document with Twitter accounts. To the best of our knowledge, this study is the first to identify individuals and create social cliques solely based on information derived from document metadata. Our study raises major concerns about the risks involved in privacy leakage due to document metadata.

Network intrusion detection systems are faced with the challenge of identifying diverse attacks, in extremely high speed networks. For this reason, they must operate at multi-Gigabit speeds, while performing highly-complex per-packet and per-flow data processing. In this paper, we present a multi-parallel intrusion detection architecture tailored for high speed networks. To cope with the increased processing throughput requirements, our system parallelizes network traffic processing and analysis at three levels, using multi-queue NICs, multiple CPUs, and multiple GPUs. The proposed design avoids locking, optimizes data transfers between the different processing units, and speeds up data processing by mapping different operations to the processing units where they are best suited. Our experimental evaluation shows that our prototype implementation based on commodity off-the-shelf equipment can reach processing speeds of up to 5.2 Gbit/s with zero packet loss when analyzing traffic in a real network, whereas the pattern matching engine alone reaches speeds of up to 70 Gbit/s, which is an almost four times improvement over prior solutions that use specialized hardware.

Short URLs have become ubiquitous. Especially popular within social networking services, short URLs have seen a significant increase in their usage over the past years, mostly due to Twitter's restriction of message length to 140 characters. In this paper, we provide a first characterization on the usage of short URLs. Specifically, our goal is to examine the content short URLs point to, how they are published, their popularity and activity over time, as well as their potential impact on the performance of the web.

Honeypots are valuable tools for detecting and analyzing malicious activity on the Internet. Successful and time-critical detection of such activity often depends on large-scale deployment. However, commercial organizations usually do not share honeypot data, and large, open honeypot initiatives only provide read-only alert feeds. As a result, while large and resourceful organizations can afford the high cost of this technology, smaller security firms and security researchers are fundamentally constrained. We propose and build a shared infrastructure for deploying and monitoring honeypots, called HoneyLab, that is similar in spirit to PlanetLab. With an overlay and distributed structure of address space and computing resources, HoneyLab increases coverage and accelerates innovation among security researchers as well as security industry experts relying on honeypot-based attack detection technology. Unlike current honeypot infrastructures, HoneyLab allows security firms and security researchers to deploy their own honeypot services, instrumentation code, and detection algorithms, dispensing the need for setting up a separate honeypot infrastructure whenever a new attack detection method needs to be deployed or tested.

Organizations use security policies to regulate how they share and exchange information, e.g., under what conditions data can be exchanged, what protocols are to be used, who is granted access, etc. Agreement on specific policies is achieved though policy reconciliation, where multiple parties, with possibly different policies, exchange their security policies, resolve differences, and reach a consensus. Current solutions for policy reconciliation do not take into account the privacy concerns of reconciliating parties. This paper addresses the problem of preserving privacy during security policy reconciliation. We introduce new protocols that meet the privacy requirements of the organizations and allow parties to find a common policy rule which maximizes their individual preferences.

Network monitoring is a vital part of modern network infrastructure management. Existing techniques either present a restricted view of network behavior and state, or do not efficiently scale to higher network speeds and heavier monitoring workloads. We present a novel architecture for programmable packet-level network monitoring that addresses these shortcomings. Our approach allows users to customize the monitoring function at the lowest possible level of abstraction to suit a wide range of monitoring needs: we use operating system mechanisms that result in a programming environment providing a high degree of flexibility, retaining fine-grained control over security, and minimizing the associated performance overheads. We present an implementation of this architecture as well as a set of experimental applications.

Conventional rewalls rely on topology restrictions and controlled network entry points to enforce traÆc ltering.Furthermore, a rewall cannot lter traÆc it does not see, so, eectively, e v eryone on the protected side is trusted.While this model has worked well for small to medium size networks, networking trends such as increased connectivity, higher line speeds, extranets, and telecommuting threaten to make it obsolete.To address the shortcomings of traditional rewalls, the concept of a \distributed rewall" has been proposed.In this scheme, security policy is still centrally de ned, but enforcement is left up to the individual endpoints.IPsec may be used to distribute credentials that express parts of the overall network policy.Alternately, these credentials may be obtained through out-of-band means.In this paper, we present the design and implementation of a distributed rewall using the KeyNote trust management system to specify, distribute, and resolve policy, and OpenBSD, an open source UNIX operating system.