Over the last years the Electric Vehicles (EVs) are gradually adopted by users, as they have environmental, technological and financial benefits with respect to combustion engine vehicles. However, the abundance of connectivity interfaces constitutes them prone to cyber-attack scenarios targeting the entire infrastructure and especially the EVs, the EV Charging Stations as well as the Charging Station Management System that is used for their management, control and configuration. The cyber-attacks have increasing impact and risk as they may further propagate to the electrical grid, causing cascading effects as utility service disruptions or blackouts. The impact analysis though requires a threat taxonomy related to their Tactics, Techniques and Procedures (TTPs) and an associated risk model, allowing to select the most prominent threats in the EV infrastructure. Additionally, such threats are currently detected and mitigated by anomaly detection systems also using Machine Learning systems, which nevertheless do not consider the distributed nature of EV infrastructures. The high level overarching objective of this article is three-fold: 1) to provide an initial security posture assessment of EV ecosystems as a reference point against which a detailed risk model is presented; 2) to identify the best practices and integration patterns in the adoption of blockchain technology to EV ecosystems for addressing EV-oriented attacks; and 3) to propose a blockchain-oriented architecture for the protection of the distributed EV ecosystems against cyber-attacks, which is also validated in a proof-of-concept infrastructure. The proposed approach presents a comprehensive Zero-Trust architecture for securing and unifying EV services across retail EV charging, energy trading, and energy management. The analysis illustrates data models, settlement and control flows, but also the security properties established, via a unified blockchain framework, assuming realistic adversarial capabilities.

The electrical grid has significantly evolved over the years, thus creating a smart paradigm, which is well known as the smart electrical grid. However, this evolution creates critical cybersecurity risks due to the vulnerable nature of the industrial systems and the involvement of new technologies. Therefore, in this paper, the ELECTRON architecture is presented as an integrated platform to detect, mitigate and prevent potential cyberthreats timely. ELECTRON combines both cybersecurity and energy defence mechanisms in a collaborative way. The key aspects of ELECTRON are (a) dynamic risk assessment, (b) asset certification, (c) federated intrusion detection and correlation, (d) Software Defined Networking (SDN) mitigation, (e) proactive islanding and (f) cybersecurity training and certification.

The arrival of autonomous vehicles (AVs) promises many great benefits, including increased safety and reduced energy consumption, pollution, and congestion. However, these engines have many security and privacy issues that could undermine the expected benefits if not addressed. AVs will provide new opportunities for hackers to carry out malicious attacks, posing a great threat to the future of mobility and data protection. The research trend in this field indicates that combining Blockchain and AI could bring strong protection for AVs against malicious attacks. Blockchain and AI have different working paradigms, but when merged, they can empower each other, and solve many security and privacy issues of AVs. AI can optimise the construction of the Blockchain to make it more efficient, secure and energy-saving, where Blockchain provides data immutability and trust mechanism for AI-based solutions and makes them more transparent, trustful, and explainable. Although some research is being conducted on this area, the topic of applying Blockchain and AI for securing AVs is not deeply investigated. In this paper, we explore the possible application of an amalgamation of Blockchain and AI solutions for securing AVs. We first introduce a classification of security and privacy threats that may arise from the application of AVs. Then, we provide an overview of recent literature regarding Blockchain and AI usage for securing AVs. Finally, we highlight limitations and challenges that may face the integration of Blockchain and AI with AVs based on our systemic review and suggest potential future directions for research in this field.

A novel system is presented in this paper, which is able to classify the different components that comprise a Federation Cyber Range (FCR) platform in terms of their criticality level and thus maximize the sharing capabilities of environments that include critical infrastructures as part of their scenarios. A characterization method is proposed in order to share the requested information amongst the cyber ranges part of a federation platform with respect to the criticality level of each unique system, lying under a scenario. The proposed model is able to correctly categorize systems based on their criticality level that are part of a scenario, conceal information, evaluate the trust level of any partner and produce and distribute reusable scenarios, according to designated markings. Mechanisms to perform an overall evaluation to the resulting scenario regarding its pliability, reusability and its evaluation aspects are to be considered. This model describes a dynamic and scalable system, able to support security needs inside an FCR environment, hide critical information from non-eligible partners and dynamically create new public scenarios based on the existing ones, without alternating the initial educational objectives.

Smart grid systems build upon existing electrical grid infrastructure by integrating power and information technologies allowing electrical power service providers to optimise their services. The combination of complex networks formed by interconnected heterogeneous devices, and the bidirectional nature of communications between end users and service providers makes security a challenging task. As implicit trust relations formed by smart grid components expand the attack surface considerably, a highly adaptable solution is required to secure these systems. In this paper, the design of an intelligent intrusion response system is explored, which can respond to ongoing multi-stage attacks in an optimal manner with respect to service availability. The smart grid infrastructure’s vulnerabilities are modelled with a graphical network security model allowing the application of probabilistic risk management methods for quantifying threats and their corresponding risks. A game-theoretic approach has been implemented that leverages the security models to efficiently respond to cyber-attacks, whose performance is tightly coupled with the system’s attack detection capabilities. To achieve better results and ensure inter-component privacy a federated learning approach was adopted. Preliminary testing on a simulated home area network with attacks against the Modbus, BACnet, and MQTT protocols, in addition to Mirai and BlackEnergy attacks, was performed to test the viability of this approach. The results illustrated the successful mitigation of attacks but also highlighted the need to implement collaborative mechanisms into the intrusion response part of the model.

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.

Smart Grids are energy delivery networks, constituting an evolution of power grids, in which a bidirectional flow between power providers and consumers is established. These flows support the transfer of electricity and information, in order to support automation actions in the context of the energy delivery network. Insofar, many smart grid implementations and implementation proposals have emerged, with varying degrees of feature delivery and sophistication. While smart grids offer many advantages, their distributed nature and information flow streams between energy producers and consumers enable the launching of a number of attacks against the smart grid infrastructure, where the related consequences may range from economic loss to complete failure of the smart grid. In this paper, we survey the threat landscape of smart grids, identifying threats that are specific to this infrastructure, providing an assessment of the severity of the consequences of each attack type, discerning features that can be utilized to detect attacks and listing methods that can be used to mitigate them.

With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive data are exposed to vulnerabilities, and therefore, easily abused by cybercriminals to perform several illegal activities. Thus, advanced network security mechanisms that are able of performing a real-time traffic analysis and mitigation of malicious traffic are required. To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware). The detection of malicious network traffic in the proposed approach works at the package level, reducing significantly the time of detection with promising results due to the deep learning technologies used. To evaluate our proposed method performance, a dataset is constructed which consists of 1000 pcap files of normal and malware traffic that are collected from different network traffic sources. The experimental results of Residual Neural Network (ResNet50) are very promising, providing a 94.50% accuracy rate for detection of malware traffic.

The blockchain has found numerous applications in many areas with the expectation to significantly enhance their security. The Internet of things (IoT) constitutes a prominent application domain of blockchain, with a number of architectures having been proposed for improving not only security but also properties like transparency and auditability. However, many blockchain solutions suffer from inherent constraints associated with the consensus protocol used. These constraints are mostly inherited by the permissionless setting, e.g. computational power in proof-of-work, and become serious obstacles in a resource-constrained IoT environment. Moreover, consensus protocols with low throughput or high latency are not suitable for IoT networks where massive volumes of data are generated. Thus, in this paper we focus on permissioned blockchain platforms and investigate the consensus protocols used, aiming at evaluating their performance and fault tolerance as the main selection criteria for (in principle highly insecure) IoT ecosystem. The results of the paper provide new insights on the essential differences of various consensus protocols and their capacity to meet IoT needs.

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In the last few years, a countless number of permissioned blockchain solutions have been proposed, with each one to claim that it revolutionizes the way of the transaction processing along with the security and privacy preserving mechanisms that it provides. Hyperledger Fabric is one of the most popular permissioned blockchain architectures that has made a significant impact on the market. However, there are only few papers of finding architectural risks regarding the security and the privacy preserving mechanisms of Hyperledger Fabric. This paper separates the attack surface of the blockchain platform into four components, namely, consensus, chaincode, network and privacy preserving mechanisms, in all of which an attacker (from inside or outside the network) can exploit the platform's design and gain access to or misuse the network. In addition, we highlight the appropriate counter-measures that can be taken in each component to address the corresponding risks and provide a significantly secure and enhanced privacy preserving Fabric network. We hope that by bringing this paper into light, we can aid developers to avoid security flaws and implementations that can be exploited by attackers but also to motivate further research to harden the platform's security and the client's privacy.

Voice assistants have become quite popular lately while in parallel they are an important part of smart-home systems. Through their voice assistants, users can perform various tasks, control other devices and enjoy third party services. The assistants are part of a wider “ecosystem”. Their function relies on the users' voice commands, received through original voice assistant devices or companion applications for smart-phones and tablets, which are then sent through the internet to the vendor's cloud services and are translated into commands. These commands are then transferred to other applications and services. As this huge volume of data, and mainly personal data of the user, moves around the voice assistant ecosystem, there are several places where personal data is temporarily or permanently stored and thus it is easy for a cyber attacker to tamper with this data, bringing forward major privacy issues. In our work we present the types and location of such personal data artifacts within the ecosystems of three popular voice assistants, after having set up our own testbed, and using IoT forensic procedures. Our privacy evaluation includes the companion apps of the assistants, as we also compare the permissions they require before their installation on an Android device.

In modern internet-scale computing, interaction between a large number of parties that are not known a-priori is predominant, with each party functioning both as a provider and consumer of services and information. In such an environment, traditional access control mechanisms face considerable limitations, since granting appropriate authorizations to each distinct party is infeasible both due to the high number of grantees and the dynamic nature of interactions. Trust management has emerged as a solution to this issue, offering aids towards the automated verification of actions against security policies. In this paper, we present a trust- and risk-based approach to security, which considers status, behavior and associated risk aspects in the trust computation process, while additionally it captures user-to-user trust relationships which are propagated to the device level, through user-to-device ownership links.

The technological evolution brought by the Internet of things (IoT) comes with new forms of cyber-attacks exploiting the complexity and heterogeneity of IoT networks, as well as, the existence of many vulnerabilities in IoT devices. The detection of compromised devices, as well as the collection and preservation of evidence regarding alleged malicious behavior in IoT networks, emerge as areas of high priority. This paper presents a blockchain-based solution, which is designed for the smart home domain, dealing with the collection and preservation of digital forensic evidence. The system utilizes a private forensic evidence database, where the captured evidence is stored, along with a permissioned blockchain that allows providing security services like integrity, authentication, and non-repudiation, so that the evidence can be used in a court of law. The blockchain stores evidences' metadata, which are critical for providing the aforementioned services, and interacts via smart contracts with the different entities involved in an investigation process, including Internet service providers, law enforcement agencies and prosecutors. A high-level architecture of the blockchain-based solution is presented that allows tackling the unique challenges posed by the need for digitally handling forensic evidence collected from IoT networks.

The present paper deals with the elucidation and implementation of the Data Protection by Design (DPbD) principle as recently introduced in the European Union data protection law, specifically with regards to cybersecurity systems in a Smart Home environment, both from a legal and a technical perspective. Starting point constitutes the research conducted in the Cyber-Trust project, which endeavours the development of an innovative and customisable cybersecurity platform for cyber-threat intelligence gathering, detection and mitigation within the Internet of Things ecosystem. During the course of the paper, the requirements of DPbD with regards to the conceptualisation, design and actual development of the system are introduced as prescribed in law. These requirements are then translated into technical solutions, as envisaged in the Cyber-Trust system. For trade-offs are not foreign to the DPbD context, technical limitations and legal challenges are also discussed in this interdisciplinary dialogue.

This paper considers the use of novel technologies for mitigating attacks that aim at compromising intrusion detection systems (IDSs). Solutions based on collaborative intrusion detection networks (CIDNs) could increase the resilience against such attacks as they allow IDS nodes to gain knowledge from each other by sharing information. However, despite the vast research in this area, trust management issues still pose significant challenges and recent works investigate whether these could be addressed by relying on blockchain and related distributed ledger technologies. Towards that direction, the paper proposes the use of a trust-based blockchain in CIDNs, referred to as trust-chain, to protect the integrity of the information shared among the CIDN peers, enhance their accountability, and secure their collaboration by thwarting insider attacks. A consensus protocol is proposed for CIDNs, which is a combination of a proof-of-stake and proof-of-work protocols, to enable collaborative IDS nodes to maintain a reliable and tampered-resistant trust-chain.

Internet of Things (IoT) is a whole new ecosystem comprised of heterogeneous connected devices -i.e. computers, laptops, smart-phones and tablets as well as embedded devices and sensors-that communicate to deliver capabilities making our living, cities, transport, energy, and many other areas more intelligent. The main concerns raised from the IoT ecosystem are the devices poor support for patching/updating and the poor on-board computational power. A number of issues stem from this: inherent vulnerabilities and the inability to detect and defend against external attacks. Also, due to the nature of their operation, the devices tend to be rather open to communication, which makes attacks easy to spread once reaching a network. The aim of this research is to investigate if it is possible to extract useful results regarding attacks' trends and be able to predict them, before it is too late, by crawling Deep/Dark and Surface web. The results of this work show that is possible to find the trend and be able to act proactively in order to protect the IoT ecosystem.

The error linear complexity spectrum constitutes a well-known cryptographic criterion for sequences, indicating how the linear complexity of the sequence decreases as the number of bits allowed to be modified per period increases. In this paper, via defining an association between$2^{n}$-periodic binary sequences and Boolean functions on$n$variables, it is shown that the error linear complexity spectrum also provides useful cryptographic information for the corresponding Boolean function$f$- namely, it yields an upper bound on the minimum Hamming distance between$f$and the set of functions depending on fewer number of variables. Therefore, the prominent Lauder-Paterson algorithm for computing the error linear complexity spectrum of a sequence may also be used for efficiently determining approximations of a Boolean function that depend on fewer number of variables. Moreover, it is also shown that, through this approach, low-degree approximations of a Boolean function can be also obtained in an efficient way.

In cognitive radio networks, secondary users (SUs) can access the spectrum licensed by primary users (PUs) in an opportunistic fashion provided they cause no harmful interference to primary transmissions. Assuming that selfish and malicious but rational types of SUs are present in the network, we consider a setting where the PUs can also benefit from the cooperation with the SUs. The SUs are autonomous agents, have disparate interests and aim at maximizing their own type-dependent interests. Since misbehaving users can impede the PUs' communications by malicious or selfish actions, we develop a trust management scheme employed by the PUs that rewards cooperative SUs and punishes non-cooperative ones. We study the impact of trust on both types of misbehaving SUs' optimal decision-making process, by utilizing the Markov Decision Process framework, and we derive conditions that provably thwart malicious and selfish behavior for certain model parameters.

Constructions of secure channel encoders, based on secret pruning, are considered in this paper. The key defines how pruning is applied on a mother convolutional code. This results in a secret subspace that legitimate users are using to perform decoding, in contrast to an eavesdropper that employs the mother code. Both reliability and security aspects of the joint scheme are treated. We derive the expected weight enumerating function of the secret subcode and show that the legitimate users achieve a better performance (that depends on the pruning rate) in terms of word and bit error rate compared with the eavesdroppers. The security relies on the notion of indistinguishability against chosen plaintext attacks. The security proofs are given in the random oracle model, and it is shown that a randomized version of the proposed joint scheme is semantically secure by relying on the hardness of the learning parities with noise problem. The above-mentioned results are achieved by introducing a new model for physical encryption to consider the contribution of the channel noise to the system's security.

A cooperative jamming protocol is studied in this paper and its ability to protect the communications of a pair of users in the presence of an eavesdropper. Communication of users is assisted by many helping interferers, assuming knowledge of channel state information. Closed form expressions are given for the optimal weights and power allocation maximizing the difference in the SNR between destination and eavesdropper; these are determined under transmit, reliability, and security constraints. Simulations show that noticeable improvements, of more than 30dB, may be attained in the SNR difference compared to the non-cooperative case.

Secure encoders are schemes aiming at providing both reliability and security in a lightweight fashion. In this paper, a secure channel encoder based on turbo codes is constructed by using the techniques of puncturing and trellis pruning. Puncturing is employed to downgrade the performance of the code and thus increase the error probability experienced by an eavesdropper at a given SNR. This has the advantage that various cryptanalytic attacks, whose complexity depends on the error probability, will become infeasible. On the other hand, trellis pruning is implemented in a secret fashion to enable legitimate users communicate reliably. An algorithm that, based on EXIT analysis, computes the corresponding puncturing and pruning rates is proposed.

This paper investigates the security offered by PHY schemes that are well oriented towards jointly providing security and protection form channel errors. In particular, we focus on constructions that were recently proposed in the literature, whose security relies on the secrecy of parameters defining the encoding/decoding process of convolutional codes. Such schemes were shown to be quite promising in terms of error correcting capabilities, but no security analysis was provided to justify their use for wireless communications. To this end, we evaluate the strength of the PHY security scheme against chosen plaintext attacks, as well as known plaintext attacks that are built upon an extension of the known algorithm of Blum, Kalai, and Wasserman. The security analysis derives the parameters to be used for achieving a high security level against such type of attacks with low encoding and decoding complexity.

Constructions of secure channel encoders based on trellis pruning are considered in this paper. The key defines how pruning is applied on the trellis of a mother convolutional code; this results into a secret pruned trellis that legitimate users are using to perform decoding, in contrast to the eavesdroppers that employ the full mother trellis diagram. We focus on two special forms of the pruning function, and in each case we compute the expected weight enumerating function of the secret pruned code. The theoretical analysis ensures that the legitimate users achieve superior performance, in terms of word and bit error rate, than the eavesdroppers, which depends on the pruning rate. We also derive design guidelines on properties that mother encoders must have to fully exploit the proposed scheme. Simulation results also show the potential of catastrophic encoders for PHY security and yet the ability of the legitimate users to communicate reliably.

Greedy algorithms are proposed for fast decoding of linear block codes over a binary symmetric channel. They are motivated by matching pursuit schemes developed in compressive sensing. Theoretical guarantees are provided for regular LDPC codes. The algorithms are highly efficient, as they only require vector-matrix multiplications and mostly use binary arithmetic. Their complexity is completely determined and depends on the code's block length and a sparsity parameter. The experimental results validate the performance of the proposed algorithms.

The algebraic immunity of cryptographic Boolean functions with odd number of variables is studied in this paper. We prove that minor modifications of functions achieving maximum algebraic immunity yield functions which are bound to have maximum or almost maximum algebraic immunity. Based on this, a new efficient algorithm to produce functions of guaranteed maximum algebraic immunity is developed. Moreover, it is shown that known constructions of functions with maximum algebraic immunity may also be generalized by using the same concepts.

This paper studies the error linear complexity spectrum of binary sequences with period2n. A precise categorization of those sequences having two distinct critical points in their spectra, as well as an enumeration of these sequences, is given. An upper bound on the maximum number of distinct critical points that the spectrum of a sequence can have is proved, and a construction which yields a lower bound on this number is given. In the process simpler proofs of some known results on the linear complexity andk-error linear complexity of sequences with period2nare provided.

In this paper, we consider the problem of computing best low-order approximations of Boolean functions; we focus on the best quadratic approximations of a subclass of cubic functions with arbitrary number of variables and we provide formulas for their efficient calculation. Our methodology is developed upon properties of the best affine approximations of quadratic functions, for which formulas for their direct computation (not by means of the Walsh-Hadamard transform) are given. We determine the cubic functions in the above subclass that achieve the maximum second-order nonlinearity, thus yielding a lower bound for the covering radius of the second order Reed-Muller code\ssr RM(2,n) in\ssr RM(3,n). Simple extensions of these results to some special cases of higher degree functions, are seen to hold. Furthermore, a preliminary analysis of well-known constructions for bent functions, in terms of their second-order nonlinearity, is performed that indicates potential weaknesses if construction parameters are not properly chosen.

This paper studies the error linear complexity profiles of binary sequences with period 2n. We give a precise categorization of those sequences having 2 distinct critical points in their profiles, as well as an enumeration of these sequences. We also give an upper bound on the maximum number of distinct critical points that the profile of a sequence can have, along with several constructions for sequences having many distinct critical points.

The best quadratic approximations of cubic Boolean functions are studied in this paper. By exploiting recent results on the classification of Boolean functions, we introduce the notion of symplectic matrices of cubic forms and show the special structure obtained by forms of almost maximum distance from all quadratic functions. These results lead to new lower bound on the covering radius of R(2, n) in R(3, n), i.e. the second order nonlinearity of cubic functions, better than the Cohen et al. bound for n les 15.

Binary sequences generated from finite state automata are studied in this correspondence by utilizing system theoretic concepts. We develop a new unified approach for analyzing the linear complexity of such sequences, via controllability and observability conditions. A vectorial trace representation of sequences with arbitrary period is provided, which leads to a new generalized discrete Fourier transform allowing the generation of sequences with prescribed linear complexity. Furthermore, we introduce new classes of nonlinear filters, using the proposed approach, which generalize currently known classes and guarantee the same lower bound on the linear complexity.

Low order approximations of Boolean functions are studied in this paper. In particular, best affine approximations of quadratic functions are analyzed using Dickson theorem, leading to an explicit formula for their direct computation, without using the Walsh transform. Expressions to determine all the best affine approximations of linear combinations of quadratic functions are proved. The tools developed are suitable to determining low order approximations; they are applied to certain low degree functions with arbitrary number of variables and allow to efficiently derive all of their best quadratic approximations.

The nonlinear complexity of binary sequences and its connections with Lempel-Ziv complexity is studied in this paper. A new recursive algorithm is presented, which produces the minimal nonlinear feedback shift register of a given binary sequence. Moreover, it is shown that the eigenvalue profile of a sequence uniquely determines its nonlinear complexity profile, thus establishing a connection between Lempel-Ziv complexity and nonlinear complexity. Furthermore, a lower bound for the Lempel-Ziv compression ratio of a given sequence is proved that depends on its nonlinear complexity.

The cryptographic properties of keystreams that are generated by stream ciphers based on T-functions are studied in this paper. Such constructions, which have been lately proposed by Klimov and Shamir, are of great interest as they allow building efficient and secure cryptographic primitives. By using concepts from the analysis of sequences, namely the linear complexity, we are able to derive simple linear equations of small weight satisfied by all T-functions. This indicates the non-randomness exhibited by state sequences produced from such mappings. Furthermore, we consider the particular class of algebraic T-functions and give necessary and sufficient conditions to generate a single cycle

The complexity of binary sequences generated by state-space systems is studied in this paper via utilization of system theoretic concepts. Application of controllability and observability conditions lead to a new block-trace representation of binary sequences enabling the efficient generation of sequences with maximum period and linear complexity. These arguments are also used to study nonlinearly filtered m-sequences, resulting in a new type of filters that achieve the same lower bound for the linear complexity as Rueppel's equidistant filters

The problem of finding the shortest feedback shift register, with quadratic feedback function that generates a given finite-length sequence is considered. An algorithm for the determination of the quadratic span and the feedback function, which takes advantage of the special block structure of the associated system of linear equations, is proposed.

Binary sequences of period 2/sup n/-1 generated by a linear feedback shift register (LFSR) whose stages are filtered by a nonlinear function, f, are studied. New iterative formulas are derived for the calculation of the linear complexity of the output sequences. It is shown that these tools provide an efficient mechanism for controlling the linear complexity of the nonlinearly filtered maximal-length sequences.

The determination of the minimum linear span sequence that differs from a given binary sequence, of period N=2/sup n/-1, by at most one digit is discussed and three methods are presented: the sequential divisions method, the congruential equations method, and the phase synchronization method. High-level algorithm organizations are provided. Finally, guidelines on sequence characterization and design via the notion of robustness are given.