Biblio

Pairings on elliptic curves which are carried out by the Miller loop and final exponentiation are used for innovative protocols such as ID-based encryption and group signature authentication. As the recent progress of attacks for finite fields in which pairings are defined, the importance of the use of the curves with prime embedding degrees \$k\$ has been increased. In this manuscript, the authors provide a method for providing efficient final exponentiation algorithms for a specific cyclotomic family of curves with arbitrary prime \$k\$ of \$k\textbackslashtextbackslashequiv 1(\textbackslashtextbackslashtextmod\textbackslashtextbackslash 6)\$. Applying the proposed method for several curves such as \$k=7\$, 13, and 19, it is found that the proposed method gives rise to the same algorithms as the previous state-of-the-art ones by the lattice-based method.

This paper presents a randomized Montgomery Powering Ladder Modular Exponentiation (RMPLME) scheme for side channel attacks (SCA) resistant Rivest-Shamir-Adleman (RSA) and its leakage resilience analysis. This method randomizes the computation time of square-and-multiply operations for each exponent bit of the Montgomery Powering Ladder (MPL) based RSA exponentiation using various radices (Radix – 2, 22, and 24) based Montgomery Modular multipliers (MMM) randomly. The randomized computations of RMPLME generates non-uniform timing channels information and power traces thus protecting against SCA. In this work, we have developed and implemented a) an unmasked right-to-left Montgomery Modular Exponentiation (R-L MME), b) MPL exponentiation and c) the proposed RMPLME schemes for RSA decryption. All the three realizations have been assessed for side channel leakage using Welch’s t-test and analyzed for secured realizations based on degree of side channel information leakage. RMPLME scheme shows the least side-channel leakage and resilient against SPA, DPA, C-Safe Error, CPA and Timing Attacks.

In the present paper the exponentiation algorithm “To Center and Back” based on the idea of the additive chains exponentiation method is developed. The created by authors algorithm allows to reduce the calculation time and to improve the performance of conventional and cryptographic algorithms, as pre-quantum and quantum, and then post-quantum, in which it is necessary to use the fast exponentiation algorithm.

Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyber-attacks. The occurrence of a cyber-attack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation - normal state or cyberattack. The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.

Considering the influence of load, This paper proposes a robust analysis method of cyber-physical power system based on the evolution of adjacency matrix. This method uses the load matrix to detect whether the system has overload failure, utilizes the reachable matrix to detect whether the system has unconnected failure, and uses the dependency matrix to reveal the cascading failure mechanism in the system. Finally, analyze the robustness of the cyber-physical power system. The IEEE30 standard node system is taken as an example for simulation experiment, and introduced the connectivity index and the load loss ratio as evaluation indexes. The robustness of the system is evaluated and analyzed by comparing the variation curves of connectivity index and load loss ratio under different tolerance coefficients. The results show that the proposed method is feasible, reduces the complexity of graph-based attack methods, and easy to research and analyze.

Nowadays, cyber physical systems are playing an important role in human life in which they provide features that make interactions between human and machine easier. To design and analysis such systems, the main problem is their complexity. In this paper, we propose a description language for cyber physical systems based on stochastic processes. The proposed language is called SPDL (Stochastic Description Process Language). For designing SPDL, two main parts are considered for Cyber Physical Systems (CSP): embedded systems and physical environment. Then these parts are defined as stochastic processes and CPS is defined as a tuple. Syntax and semantics of SPDL are stated based on the proposed definition. Also, the semantics are defined as by set theory. For implementation of SPDL, dependencies between words of a requirements are extracted as a tree data structure. Based on the dependencies, SPDL is used for describing the CPS. Also, a lexical analyzer and a parser based on a defined BNF grammar for SPDL is designed and implemented. Finally, SPDL of CPS is transformed to NuSMV which is a symbolic model checker. The Experimental results show that SPDL is capable of describing cyber physical systems by natural language.

Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision on the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there is a clear trade-off between the two metrics. Instead, we argue that attack detection should bias different metrics when a system sits in different states. For example, if the system is close to unsafe states, reducing the detection delay is preferable to lowering the false alarm rate, and vice versa. To achieve this, we make the following contributions. In this paper, we propose a real-time adaptive sensor attack detection framework. The framework can dynamically adapt the detection delay and false alarm rate so as to meet a detection deadline and improve the usability according to different system status. The core component of this framework is an attack detector that identifies anomalies based on a CUSUM algorithm through monitoring the cumulative sum of difference (or residuals) between the nominal (predicted) and observed sensor values. We augment this algorithm with a drift parameter that can govern the detection delay and false alarm. The second component is a behavior predictor that estimates nominal sensor values fed to the core component for calculating the residuals. The predictor uses a deep learning model that is offline extracted from sensor data through leveraging convolutional neural network (CNN) and recurrent neural network (RNN). The model relies on little knowledge of the system (e.g., dynamics), but uncovers and exploits both the local and complex long-term dependencies in multivariate sequential sensor measurements. The third component is a drift adaptor that estimates a detection deadline and then determines the drift parameter fed to the detector component for adjusting the detection delay and false alarms. Finally, we implement the proposed framework and validate it using realistic sensor data of automotive CPS to demonstrate its efficiency and efficacy.

Preventing the cyberattacks has been a concern for any organization. In this research, the authors propose a novel method to detect cyberattacks by monitoring and analyzing the network traffic. It was observed that the various log files that are created in the server does not contain all the relevant traces to detect a cyberattack. Hence, the HTTP traffic to the web server was analyzed to detect any potential cyberattacks. To validate the research, a web server was simulated using the Opensource Damn Vulnerable Web Application (DVWA) and the cyberattacks were simulated as per the OWASP standards. A python program was scripted that captured the network traffic to the DVWA server. This traffic was analyzed in real-time by reading the various HTTP parameters viz., URLs, Get / Post methods and the dependencies. The results were found to be encouraging as all the simulated attacks in real-time could be successfully detected. This work can be used as a template by various organizations to prevent any insider threat by monitoring the internal HTTP traffic.

To ensure a ship’s fully operational status in a wide spectrum of missions, as passenger transportation, international trade, and military activities, numerous interdependent systems are essential. Despite the potential critical consequences of misunderstanding or ignoring those interdependencies, there are very few documented approaches to enable their identification, representation, analysis, and use. From the cybersecurity point of view, if an anomaly occurs on one of the interdependent systems, it could eventually impact the whole ship, jeopardizing its mission success. This paper presents a proposal to identify the main dependencies of layers within and between generic ship’s functional blocks. An analysis of one of these layers, the platform systems, is developed to examine a naval cyber-physical system (CPS), the water management for passenger use, and its associated dependencies, from an intrinsic perspective. This analysis generates a three layers graph, on which dependencies are represented as oriented edges. Each abstraction level of the graph represents the physical, digital, and system variables of the examined CPS. The obtained result confirms the interest of graphs for dependencies representation and analysis. It is an operational depiction of the different systems interdependencies, on which can rely a cybersecurity evaluation, like anomaly detection and propagation assessment.

Naval vessels infrastructures are evolving towards increasingly connected and automatic systems. Such accelerated complexity boost to search for more adapted and useful navigation devices may be at odds with cybersecurity, making necessary to develop adapted analysis solutions for experts. This paper introduces a novel process to visualize and analyze naval Cyber-Physical Systems (CPS) using oriented graphs, considering operational constraints, to represent physical and functional connections between multiple components of CPS. Rapid prototyping of interconnected components is implemented in a semi-automatic manner by defining the CPS’s digital and physical systems as nodes, along with system variables as edges, to form three layers of an oriented graph, using the open-source Neo4j software suit. The generated multi-layer graph can be used to support cybersecurity analysis, like attacks simulation, anomaly detection and propagation estimation, applying existing or new algorithms.

Modern electric power systems are complex cyber-physical systems. The integration of traditional power and digital technologies result in interdependencies that need to be considered in risk analysis. In this paper we argue the need for analysis methods that can combine the competencies of various experts in a common analysis focusing on the overall system perspective. We report on our experiences on using the Vulnerability Analysis Framework (VAF) and bow-tie diagrams in a combined analysis of the power and cyber security aspects in a realistic case. Our experiences show that an extended version of VAF with increased support for interdependencies is promising for this type of analysis.

This paper presents conceptual modelling of the criticality of critical infrastructure (CI) nth order dependency effect using neural networks. Incidentally, critical infrastructures are usually not stand-alone, they are mostly interconnected in some way thereby creating a complex network of infrastructures that depend on each other. The relationships between these infrastructures can be either unidirectional or bidirectional with possible cascading or escalating effect. Moreover, the dependency relationships can take an nth order, meaning that a failure or disruption in one infrastructure can cascade to nth interconnected infrastructure. The nth-order dependency and criticality problems depict a sequential characteristic, which can result in chronological cyber effects. Consequently, quantifying the criticality of infrastructure demands that the impact of its failure or disruption on other interconnected infrastructures be measured effectively. To understand the complex relational behaviour of nth order relationships between infrastructures, we model the behaviour of nth order dependency using Neural Network (NN) to analyse the degree of dependency and criticality of the dependent infrastructure. The outcome, which is to quantify the Criticality Index Factor (CIF) of a particular infrastructure as a measure of its risk factor can facilitate a collective response in the event of failure or disruption. Using our novel NN approach, a comparative view of CIFs of infrastructures or organisations can provide an efficient mechanism for Critical Information Infrastructure Protection and resilience (CIIPR) in a more coordinated and harmonised way nationally. Our model demonstrates the capability to measure and establish the degree of dependency (or interdependency) and criticality of CIs as a criterion for a proactive CIIPR.

Cyber ranges are valuable assets but have limitations in simulating complex realities and multi-sector dependencies; to address this, federated cyber ranges are emerging. This work presents the ECHO Federated Cyber Range, a marketplace for cyber range services, that establishes a mechanism by which independent cyber range capabilities can be interconnected and accessed via a convenient portal. This allows for more complex and complete emulations, spanning potentially multiple sectors and complex exercises. Moreover, it supports a semi-automated approach for processing and deploying service requests to assist customers and providers interfacing with the marketplace. Its features and architecture are described in detail, along with the design, validation and deployment of a training scenario.

Residue number systems provide efficient techniques for speeding up calculations and/or protecting against side channel attacks when used in the context of cryptographic engineering. One of the interests of such systems is their scalability, as the existence of large bases for some specialized systems is often an open question. In this paper, we present highly optimized methods for generating large bases for residue number systems and, in some cases, the largest possible bases. We show their efficiency by demonstrating their improvement over the state-of-the-art bases reported in the literature. This work make it possible to address the problem of the scalability issue of finding new bases for a specific system that arises whenever a parameter changes, and possibly open new application avenues.

There is an increasing trend in cloud adoption of enterprise applications in, for example, manufacturing, healthcare, and finance. Such applications are routinely subject to targeted cyberattacks, which result in significant loss of sensitive data (e.g., due to data exfiltration in advanced persistent threats) or valuable utilities (e.g., due to resource the exfiltration of power in cryptojacking). There is a critical need to train highly skilled cybersecurity professionals, who are capable of defending against such targeted attacks. In this article, we present the design, development, and evaluation of the Mizzou Cyber Range, an online platform to learn basic/advanced cyber defense concepts and perform training exercises to engender the next-generation cybersecurity workforce. Mizzou Cyber Range features flexibility, scalability, portability, and extendability in delivering cyberattack/defense learning modules to students. We detail our “research-inspired learning” and “learn-apply-create” three-phase pedagogy methodologies in the development of four learning modules that include laboratory exercises and self-study activities using realistic cloud-based application testbeds. The learning modules allow students to gain skills in using latest technologies (e.g., elastic capacity provisioning, software-defined everything infrastructure) to implement sophisticated “attack defense by pretense” techniques. Students can also use the learning modules to understand the attacker-defender game in order to create disincentives (i.e., pretense initiation) that make the attacker's tasks more difficult, costly, time consuming, and uncertain. Lastly, we show the benefits of our Mizzou Cyber Range through the evaluation of student learning using auto-grading, rank assessments with peer standing, and monitoring of students' performance via feedback from prelab evaluation surveys and postlab technical assessments.

In recent years web applications that are hacked every day estimated to be 30 000, and in most cases, web developers or website owners do not even have enough knowledge about what is happening on their sites. Web hackers can use many attacks to gain entry or compromise legitimate web applications, they can also deceive people by using phishing sites to collect their sensitive and private information. In response to this, the need is raised to take proper measures to understand the risks and be aware of the vulnerabilities that may affect the website and hence the normal business flow. In the scope of this study, mitigations against the most common web application attacks are set, and the web administrator is provided with ways to detect phishing links which is a social engineering attack, the study also demonstrates the generation of web application logs that simplifies the process of analyzing the actions of abnormal users to show when behavior is out of bounds, out of scope, or against the rules. The methods of mitigation are accomplished by secure coding techniques and the methods for phishing link detection are performed by various machine learning algorithms and deep learning techniques. The developed application has been tested and evaluated against various attack scenarios, the outcomes obtained from the test process showed that the website had successfully mitigated these dangerous web application attacks, and for the detection of phishing links part, a comparison is made between different algorithms to find the best one, and the outcome of the best model gave 98% accuracy.

This note addresses the problem of distributed control for a class of nonlinear multi-agent systems over a communication graph. In many real practical systems, owing to communication limits and the vulnerability of communication networks to be overheard and modified by the adversary, consideration of communication delays and cyber-attacks in designing of the controller is important. To consider these challenges, in the presented approach, a distributed controller for a group of one-link flexible joint manipulators is provided which are connected via data delaying communication network in the presence of cyber-attacks. Sufficient conditions are provided to guarantee that the closed-loop system is stable with prescribed disturbance attenuation, and the parameter of the control law can be obtained by solving a set of linear matrix inequities (LMIs). Eventually, simulations results of four single-link manipulators are provided to demonstrate the performance of the introduced method.

Traditional DDoS attacks mainly send massive data packets through the attacking machine, consuming the network resources or server resources of the target server, making users unable to use server resources to achieve the purpose of denial of service. This type of attack is called a Flooding-based DDoS (FDDoS) attack. It has the characteristics of large traffic and suddenness. However, Low-rate DDoS (LDDoS) attack is a new type of DDoS attack. LDDoS utilize the TCP congestion control mechanism and sends periodic pulses to attack, which can seriously reduce the TCP flow throughput of the attacked link. It has the characteristics of small traffic and strong concealment. Each of these two DDoS attack methods has its own hard-to-handle characteristics, so that there is currently no particularly effective method to prevent such attacks. This paper uses time-frequency analysis to classify and filter DDoS traffic. The proposed filtering method is designed as a system in the actual environment. Experimental results show that the designed filtering algorithm can resist not only FDDoS attacks, but also LDDoS attacks.

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.

With current Traditional / Legacy networks, the reliance on manual intervention to solve a variety of issues be it primary operational functionalities like addressing Link-failure or other consequent complexities arising out of existing solutions for challenges like Link-flapping or facing attacks like DDoS attacks is substantial. This physical and manual approach towards network configurations to make significant changes result in very slow updates and increased probability of errors and are not sufficient to address and support the rapidly shifting workload of the networks due to the fact that networking decisions are left to the hands of physical networking devices. With the advent of Software Defined Networking (SDN) which abstracts the network functionality planes, separating it from physical hardware – and decoupling the data plane from the control plane, it is able to provide a degree of automation for the network resources and management of the services provided by the network. This paper explores some of the aspects of automation provided by SDN capabilities in a Mesh Network (provides Network Security with redundancy of communication links) which contribute towards making the network inherently intelligent and take decisions without manual intervention and thus take a step towards Intelligent Automated Networks.

Distributed Denial-of-Service (DDoS) attack is a long-lived attack that is hugely harmful to the Internet. In particular, the emergence of a new type of DDoS called Link Flooding Attack (LFA) makes the detection and defense more difficult. In LFA, the attacker cuts off a specific area by controlling large numbers of bots to send low-rate traffic to congest selected links. Since the attack flows are similar to the legitimate ones, traditional schemes like anomaly detection and intrusion detection are no longer applicable. Blockchain provides a new solution to address this issue. In this paper, we propose a blockchain-based LFA detection scheme, which is deployed on routers and servers in and around the area that we want to protect. Blockchain technology is used to record and share the traceroute information, which enables the hosts in the protected region to easily trace the flow paths. We implement our scheme in Ethereum and conduct simulation experiments to evaluate its performance. The results show that our scheme can achieve timely detection of LFA with a high detection rate and a low false positive rate, as well as a low overhead.

Software-Defined Networking (SDN) has found applications in different domains, including wired- and wireless networks. The SDN controller has a global view of the network topology, which is vulnerable to topology poisoning attacks, e.g., link fabrication and host-location hijacking. The adversaries can leverage these attacks to monitor the flows or drop them. However, current defence systems such as TopoGuard and TopoGuard+ can detect such attacks. In this paper, we introduce the Link Latency Attack (LLA) that can successfully bypass the systems' defence mechanisms above. In LLA, the adversary can add a fake link into the network and corrupt the controller's view from the network topology. This can be accomplished by compromising the end hosts without the need to attack the SDN-enabled switches. We develop a Machine Learning-based Link Guard (MLLG) system to provide the required defence for LLA. We test the performance of our system using an emulated network on Mininet, and the obtained results show an accuracy of 98.22% in detecting the attack. Interestingly, MLLG improves 16% the accuracy of TopoGuard+.

Despite the high directivity of THz beams, THz wireless links may still suffer compromising emissions when propagate through atmospheric turbulence and suffers scattering. In this work, we investigate the eavesdropping risks of a line-of-sight (LOS) THz link `in atmospheric turbulence with an eavesdropper located close to but outside of the beam path. A theoretical model considering the turbulence induced losses, gaseous absorption and beam divergence is conducted. Theoretical estimations agree well with our measured data. The secrecy capacity and outage probability dependent on the carrier frequency, turbulence strength, eavesdropper’s position and receiver sensitivity are analyzed.

Research has been conducted on wireless network single link failures. However, cascaded link failures due to fraudulent attacks have not received enough attention, whereas this requires solutions. This research developed an enhanced genetic algorithm (EGA) focused on capacity efficiency and fast restoration to rapidly resolve link-link failures. On complex nodes network, this fault-tolerant model was tested for such failures. Optimal alternative routes and the bandwidth required for quick rerouting of video traffic were generated by the proposed model. Increasing cascaded link failures increases bandwidth usage and causes transmission delay, which slows down video traffic routing. The proposed model outperformed popular Dijkstra models, in terms of time complexity. The survived solution paths demonstrate that the proposed model works well in maintaining connectivity despite cascaded link failures and would therefore be extremely useful in pandemic periods on emergency matters. The proposed technology is feasible for current business applications that require high-speed broadband networks.

In recent years, a new type of DDoS attacks against backbone routing links have appeared. They paralyze the communication network of a large area by directly congesting the key routing links concerning the network accessibility of the area. This new type of DDoS attacks make it difficult for traditional countermeasures to take effect. This paper proposes and implements an attack detection method based on non-cooperative active measurement. Experiments show that our detection method can efficiently perceive changes of network link performance and assist in identifying such new DDoS attacks. In our testbed, the network anomaly detection accuracy can reach 93.7%.