This paper proposes methods to upstage the best-known defences against query-based black-box attacks. These benchmark defences incorporate gaussian noise into input data during inference to achieve state-of-the-art performance in protecting image classification models against the most advanced query-based black-box attacks. Even so there is a need to improve upon them; for example, the widely benchmarked Random noise defense (RND) method has demonstrated limited robustness – achieving only 53.5% and 18.1% with a ResNet-50 model on the CIFAR-10 and ImageNet datasets, respectively – against the square attack, which is commonly regarded as the state-of-the-art black-box attack. Therefore, in this work, we propose two alternatives to gaussian noise addition at inference time: random crop-resize and random rotation of the input images. Although these transformations are generally used for data augmentation while training to improve model invariance and generalisation, their protective potential against query-based black-box attacks at inference time is unexplored. Therefore, for the first time, we report that for such well-trained models either of the two transformations can also blunt powerful query-based black-box attacks when used at inference time on three popular datasets. The results show that the proposed randomised transformations outperform RND in terms of robust accuracy against a strong adversary that uses a high budget of 100,000 queries based on expectation over transformation (EOT) of 10, by 0.9% on the CIFAR-10 dataset, 9.4% on the ImageNet dataset and 1.6% on the Tiny ImageNet dataset. Crucially, in two even tougher attack settings, that is, high-confidence adversarial examples and EOT-50 adversary, these transformations are even more effective as the margin of improvement over the benchmarks increases further.

The Internet of Things (IoT) consists of a rapidly growing network of heterogeneous devices that autonomously monitor, collect, and exchange data across a wide range of application domains. The rapid increase of IoT devices highlighted the importance of scalable, secure, and adaptive network management strategies for dynamic networks. A key challenge in this context is the automatic identification of devices, which is critical for detecting and mitigating malicious devices that can compromise network integrity. Accurate device identification strengthens the security of dynamic IoT environments by facilitating early detection of anomalous or adversarial traffic. Device fingerprinting offers a non-intrusive solution by leveraging protocol and traffic characteristics, without relying on vendor-specific identifiers. In this work, we propose a lightweight and efficient framework for IoT device identification based on machine learning. Our model utilises a Random Forest classifier in conjunction with a data-driven feature selection strategy that emphasises low-overhead features derived from packet headers and traffic flow statistics. The proposed approach achieves high classification performance, attaining 97.32% accuracy in identifying general device categories and 94.39% accuracy for specific device types. It also demonstrates approximately a 40% improvement in computational efficiency compared to traditional classifiers, making it well-suited for deployment in resource-constrained edge environments. We evaluate the model under various real-world conditions, including spatiotemporal traffic variations, changes in operational modes, and different sampling intervals. Comparative experiments with established classifiers—such as J48, SMO, BayesNet, and Naive Bayes—are performed using standard metrics, including precision, recall, F1-score, and inference latency. Our approach strengthens network security by automatically identifying and classifying IoT devices in dynamic, heterogeneous environments. It is lightweight, scalable, and well-suited for deployment in resource-constrained IoT scenarios. • The research focuses on using device fingerprints and machine learning techniques to efficiently identify and classify IoT devices in heterogeneous networks. • The study employs Random Forest as the primary classification algorithm, achieving an accuracy of 97.32% for classifying IoT devices and 94.39% for identifying specific device types. • By leveraging optimized feature selection techniques, the study ensures high accuracy and reduces computational overhead. • The study highlights the importance of balancing accuracy and time efficiency in IoT device identification, offering a robust model for real-world applications.

Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission has mandated telecom providers in the U.S. to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with IP-based systems (e.g., SIP), leaving the traditional non-IP systems (e.g., SS7) unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this article, we propose a PKI-free solution, called Caller ID Verification (CIV). CIV authenticates the caller ID based on a challenge-response process instead of digital signatures, hence requiring no PKI. It supports both IP and non-IP systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-tone Multi-frequency, to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for Voice over Internet Protocol, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user’s phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to.

Cyber threats continue to grow with novel methods to attack computing systems, highlighting the need for sophisticated mechanisms and techniques to protect against such dynamic threats. Contemporary cyber defence mechanisms utilise a range of methods which rely on monitoring network or system-level events. However, with the growing use of the dark web by mal-actors to share exploits, breaches, and data leaks, the use of such information to strengthen defence mechanisms becomes an intriguing prospect. In this paper, we present our efforts to develop a text mining engine (Threat Miner) which analyses data from dark web forums and transforms it into actionable intelligence. Leveraging cutting-edge machine learning techniques and utilising a bespoke threat dictionary, Threat Miner extracts useful information from dark web forums into STIX form, enabling it to be used with threat intelligence platforms. We also present the results of a thorough evaluation of our scheme which was conducted with the CrimeBB dataset [1] to understand the feasibility of the approach as well as its effectiveness in strengthening defence capability against cyber threats.

Today's smartphones are equipped with a large number of powerful value-added sensors and features, such as a low-power Bluetooth sensor, powerful embedded sensors, such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways, such as tracking the health of the patients and the movement of doctors, tracking employees movement in large manufacturing units, monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people's thoughts to have symptoms of certain diseases or viruses, e.g., COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking, and isolating the persons showing COVID-19 symptoms to limit the spread of the disease to the larger community. The use of embedded sensors could disclose private information of the users, thus potentially bring a threat to the privacy and security of users. In this article, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices are transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.

Today we are relying on Internet technologies for numerous services, for example, personal communication, online businesses, recruitment, and entertainment. Over these networks, people usually create content, a skillful worker profile, and provide services that are normally watched and used by other users, thus developing a social network among people termed as the Internet of People. Malicious users could also utilize such platforms for spreading unwanted content that could bring catastrophic consequences to a social network provider and the society, if not identified on time. The use of trust management over these networks plays a vital role in the success of these services. Crowd-sensing people or network users for their views about certain content or content creators could be a potential solution to assess the trustworthiness of content creators and their content. However, the human involvement in crowd-sensing would have challenges of privacy preservation and preventing intentional assignment of the fake high score given to certain user/content. To address these challenges, in this article, we propose a novel trust model that evaluates the aggregate trustworthiness of the content creator and the content without compromising the privacy of the participating people in a crowdsource group. The proposed system has inherent properties of privacy protection of participants, performs operations in the decentralized setup, and considers the trust weights of participants in a private and secure way. The system ensures privacy of participants under the malicious and honest-but-curious adversarial models. We evaluated the performance of the system by developing a prototype and applying it to different real data from different online social networks.

Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism.

The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.

The Internet of Things (IoT) is the network of connected computing devices that have the ability to transfer valued data between each other via the Internet without requiring human intervention. In such a connected environment, the social IoT (SIoT) has become an emerging trend where multiple IoT devices owned by users support communication within a social circle. Trust management in the SIoT network is imperative as trusting the information from compromised devices could lead to serious compromises within the network. It is important to have a mechanism where the devices and their users evaluate the trustworthiness of other devices and users before trusting the information sent by them. The privacy preservation, decentralization, and self-enforcing management without involving trusted third parties are the fundamental challenges in designing a trust management system for SIoT. To fulfill these challenges, this article presents a novel framework for computing and updating the trustworthiness of participants in the SIoT network in a self-enforcing manner without relying on any trusted third party. The privacy of the participants in the SIoT is protected by using homomorphic encryption in the decentralized setting. To achieve the properties of self-enforcement, the trust score of each device is automatically updated based on its previous trust score and the up-to-date tally of the votes by its peers in the network with zero-knowledge proofs (ZKPs) to enforce that every participant follows the protocol honestly. We evaluate the performance of the proposed scheme and present evaluation benchmarks by prototyping the main functionality of the system. The performance results show that the system has a linear increase in computation and communication overheads with more participants in the network. Furthermore, we prove the correctness, privacy, and security of the proposed system under a malicious adversarial model.

The Internet of Things (IoT) or the cyber-physical system (CPS) is the network of connected devices, things, and people that collect and exchange information using the emerging telecommunication networks (4G, 5G IP-based LTE). These emerging telecommunication networks can also be used to transfer critical information between the source and destination, informing the control system about the outage in the electrical grid, or providing information about the emergency at the national express highway. This sensitive information requires authorization and authentication of source and destination involved in the communication. To protect the network from unauthorized access and to provide authentication, the telecommunication operators have to adopt the mechanism for seamless verification and authorization of parties involved in the communication. Currently, the next-generation telecommunication networks use a digest-based authentication mechanism, where the call-processing engine of the telecommunication operator initiates the challenge to the request-initiating client or caller, which is being solved by the client to prove his credentials. However, the digest-based authentication mechanisms are vulnerable to many forms of known attacks, e.g., the man-in-the-middle (MITM) attack and the password guessing attack. Furthermore, the digest-based systems require extensive processing overheads. Several public-key infrastructure (PKI)-based and identity-based schemes have been proposed for the authentication and key agreements. However, these schemes generally require a smart card to hold long-term private keys and authentication credentials. In this article, we propose a novel self-enforcing authentication protocol for the session-initiation-protocol-based next-generation network, based on a low-entropy shared password without relying on any PKI or the trusted third party system. The proposed system shows effective resistance against various attacks, e.g., MITM, replay attack, password guessing attack, etc. We analyze the security properties of the proposed scheme in comparison to the state of the art.

An end-to-end verifiable (E2E) voting system enables candidates, voters and observers to monitor the integrity of an election process and verify the results without relying on trusted systems. In this paper, we propose a DRE-based Borda count e-voting system called DRE-Borda. The proposed system is E2E verifiable without involving any tallying authorities. Furthermore, it outputs only the total score a candidate gets without revealing any other information such as the breakdown of scores with respect to different ranks. This reduces the information leakage from the tallying result to the minimum, hence effectively preventing Italian attacks. When the DRE machine is completely compromised, the integrity of the tallying result is still preserved and what an adversary can learn from a compromised machine is strictly limited to the partial tally at the time of compromise.

In 2006, Hao and Zieliński presented a two‐round veto protocol named anonymous veto network (AV‐net), which is exceptionally efficient in terms of the number of rounds, computation and bandwidth usage. However, AV‐net has two generic issues: (i) a participant who has submitted a veto can find out whether she is the only one who vetoed; (ii) the last participant who submits her input can pre‐compute the Boolean‐OR result before submission, and may amend her input based on that knowledge. These two issues generally apply to any multi‐round veto protocol where participants commit their input in the last round. In this study, the authors propose a novel solution to address both issues within two rounds, which are the best possible round efficiency for a veto protocol. Their new private veto protocol, called PriVeto, has similar system complexities to AV‐net, but it binds participants to their inputs in the very first round, eliminating the possibility of runtime changes to any of the inputs. At the end of the protocol, participants are strictly limited to learning nothing more than the output of the Boolean‐OR function and their own inputs.

The Internet of Vehicles is the network of connected vehicles and transport infrastructure units [roadside units (RSUs)], which utilizes emerging wireless systems (4G, 5G, LTE) for the communication and sharing of information. The network of connected vehicles enables users to disseminate critical information about events happening on the road (for example, accidents, traffic congestions, and hazards). The exchange of information between vehicles and RSUs could improve the driving experience and road safety, as well as help drivers to identify the hazardous and safe routes in a timely manner. The sharing of critical information between vehicles is advantageous to the driver; however, at the same time, malicious actors could mislead drivers by spreading fraudulent and fake messages. Fraudulent messages can have a negative impact on the infrastructure, and more significantly, have potential to cause threats to life. It is, therefore, essential that vehicles can evaluate the credibility of those who send messages (vehicles or RSUs) before taking any action. In this paper, we present TrustVote, a collaborative crowdsourcing-based vehicle reputation system that enables vehicles to evaluate the credibility of other vehicles in a connected vehicular network. The TrustVote system allows participating vehicles to hide their rating/feedback scores and the list of interacted vehicles under a homomorphic cryptographic layer, which can only be unfolded as an aggregate. The proposed approach also considers the trust weight of a vehicle providing the rating scores while computing the aggregate reputation of the vehicles. A prototype of TrustVote is developed and its performance is evaluated in terms of the computational and communication overheads.

The Internet of Things (IoT) is the integration of a large number of autonomous heterogeneous devices that report information from the physical environment to the monitoring system for analytics and meaningful decisions. The compromised machines in the IoT network may not only be used for spreading unwanted content such as spam, malware, viruses etc, but can also report incorrect information about the physical world that might have a disastrous consequence. The challenge is to design a collaborative reputation system that calculates trustworthiness of machines in the IoT-based machine-to-machine network without consuming high system resources and breaching the privacy of participants. To address the challenge of privacy preserving reputation system for the decentralized IoT environment, this paper presents a novel M2M-REP (Machine to Machine Reputation) system that computes global reputation of the machine by aggregating the encrypted local feedback provided by machines in a fully decentralized and secure way. The privacy of participating machines is well protected such that machines or analyst would not learn any information about the feedback score provided by the participating machines other than the final aggregated statistical score. We present a decentralized reputation aggregation system for two scenarios: a semi-honest (honest-but-curious) setup where machines are trustworthy in providing feedback but are curious to learn sensitive information about the collaborating machines, and the malicious model where machines not only try to learn the sensitive information of participants but also do not follow the protocol specification in providing feedback. We analyzed the security and privacy properties of the M2M-REP system for different adversarial models.

In recent years, VoIP (Voice over Internet Protocol) has emerged as cheap telephony medium for a long distance international and domestic calls. The number of unwanted calls from telemarketers and scammers has also risen recently, because of VoIP telephony that makes easier to initiate large number of calls without being tracing back by authorities. It is utmost important for the VoIP operators to gain trust of their customers by blocking telemarketers and scammers at the edge of the network. To address this challenge, in this paper, we present a system called Caller-Centrality that effectively identifies and blocks telemarketers/spammers without being intrusive to the caller and the callee. Caller-Centrality first models the user relationships as a caller graph and then computes reputation of the caller using weighted centrality measure. The edge weights between caller and the callee are assigned from call rate and call duration between caller and the callee. We evaluated our approach anonymized real-data set collected from a small VoIP operator. The evaluation results reveal that Caller-Centrality successfully identifies suspected telemarketers.

Abstract The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non‐spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non‐spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set‐up phase. Copyright © 2016 John Wiley & Sons, Ltd.

Clouds represent a major paradigm shift from contemporary systems, inspiring the contemporary approach to computing. They present fascinating opportunities to address dynamic user requirements with the provision of flexible computing infrastructures that are available on demand. Clouds, however, introducing novel challenges particularly with respect to security that require dedicated efforts to address them. This study is focused at one such challenge, that is, determining the extent of damage caused by an intrusion for a victim virtual machine. It has significant implications especially with respect to effective response to the intrusion. This study presents the efforts to address this challenge for Clouds in the form of a novel scheme for intrusion damage assessment for Clouds. In addition to its context‐aware operation, the scheme facilitates protection against multi‐stage attacks. The study also includes the formal specification and evaluation of the scheme, which successfully demonstrate its effectiveness to achieve rigorous damage assessment for Clouds.

SPIT (Spam over Internet Telephony) is unsolicited, unwanted phone calls made for advertising products or voice phishing. The real time nature of voice calls makes traditional email anti-spam techniques un-applicable to SPIT detection in a VoIP (Voice over Internet Protocol) network. The VoIP users have social network with colleagues, friends, family members, and other acquaintances. Various social reputation approaches have been proposed but these were mainly based on average call duration or require user feedback to assign reputation score. We believe that the computation of reputation should be two fold; firstly it should not involve user feedback and secondly it considers other network features in addition to call duration. In this paper, we propose a social strength for detecting SPIT callers. We analyze how similarities and social ties among VoIP users effect SPIT detection. The local social strength among users are computed considering more features like out-degree, number of repetitive calls, reciprocity and interaction rate. The global strength of the caller is computed using the Eigen trust algorithm and represents the strength of a caller as whole in a network. The global strength values are then compared with the automated threshold value for finally classifying a caller as legitimate and non-legitimate. A distinct feature of our approach is that it does not involve users for feedback and can be easily deployed in real VoIP network without any change in architecture and SIP protocol stack. We evaluate our social strength approach on different types of random network data and shows that the system detects SPIT callers with false positive rate less then 10% and true positive rate of 99% for all type of underlying random networks.

This paper devises a new multicriteria image-based controller for the control of six degrees of freedom (PUMA560) robotic arm, based upon Linear Matrix Inequality (LMI). The aim lies in developing such a method that neither involves camera calibration parameters nor inverse kinematics. The approach adopted in this paper includes transpose Jacobian control; thus, inverse of the Jacobian matrix is no longer required. The proposed controller allows stabilizing the camera despite the unknown value of the target point depth. To make sure that the features remain in the camera field of view, and to restrict the controller's input using some bounds, visibility and kinematic constraints are introduced in the form of LMIs. By invoking the Lyapunov's direct method, closed-loop stability of the system is ensured. Simulation results are shown for three different cases, which exhibit the system stability and convergence even in the presence of large errors, and present the comparative analysis of both i.e., systems with and without visibility and kinematic constraints.

With the passage of time and increase in the need for mobility wireless or mobile networks emerged to replace the wired networks. This new generation of networks is different from the earlier one in many aspects like network infrastructure, resources and routing protocols, routing devices etc. These networks are bandwidth and resource constrained with no network infrastructure and dedicated routing devices. Moreover, every node in such networks has to take care of its routing module itself. These characteristics become reasons for the importance of security in mobile ad-hoc networks as there is very high probability of attacks in such networks. Some work has been done to compare different protocols on basis of security but keeping in view the resource limitations in such networks, evaluation based on networking context is also important. We evaluate the overall performance overhead associated with secure routing protocols for mobile ad-hoc networks (MANETs). We implement the secure ad-hoc on-demand distance vector routing protocol (SAODV) extensions with AODV in the network simulator 2 (NS-2) and use the Monarch project implementation of Ariadne for our evaluation purpose. We try to figure out the amount of extra work a mobile node has to do in order to operate securely