Biblio

The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behaviour of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation.

Cyber-Physical Power Systems (CPPSs) currently face an increasing number of security attacks and lack methods for optimal proactive security decisions to defend the attacks. This paper proposed an optimal defensive method based on game theory to minimize the system performance deterioration of CPPSs under cyberspace attacks. The reinforcement learning algorithmic solution is used to obtain the Nash equilibrium and a set of metrics of system vulnerabilities are adopted to quantify the cost of defense against cyber-attacks. The minimax-Q algorithm is utilized to obtain the optimal defense strategy without the availability of the attacker's information. The proposed solution is assessed through experiments based on a realistic power generation microsystem testbed and the numerical results confirmed its effectiveness.

Network resilience is crucial to ensure reliable and secure operation of critical infrastructures. Although graph theoretic methods have been developed to quantify the topological resilience of networks, i.e., measuring resilience with respect to connectivity, in this study we propose to use the tools from Topological Data Analysis (TDA), Algebraic Topology, and Optimal Transport (OT). In our prior work, we used these tools to create a resilience metric that bypassed the need to embed a network onto a space. We also hypothesized that embeddings could encode different information about a network and that different embeddings could result in different outcomes when computing resilience. In this paper we attempt to test this hypothesis. We will utilize the WEGL framework to compute the embedding for the considered network and compare the results against our prior work, which did not use an embedding process. To our knowledge, this is the first attempt to study the ramifications of choosing an embedding, thus providing a novel understanding into how to choose an embedding and whether such a choice matters when quantifying resilience.

Many popular metrics used for the quantification of the quality or complexity of a codebase (e.g. cyclomatic complexity) were developed in the 1970s or 1980s when source code sizes were significantly smaller than they are today, and before a number of modern programming language features were introduced in different languages. Thus, the many thresholds that were suggested by researchers for deciding whether a given function is lacking in a given quality dimension need to be updated. In the pursuit of this goal, we study a number of open-source high-performance codes, each of which has been in development for more than 15 years—a characteristic which we take to imply good design to score them in terms of their source codes' quality and to relax the above-mentioned thresholds. First, we employ the LLVM/Clang compiler infrastructure and introduce a Clang AST tool to gather AST-based metrics, as well as an LLVM IR pass for those based on a source code's static call graph. Second, we perform statistical analysis to identify the reference thresholds of 22 code quality and callgraph-related metrics at a fine grained level.

Internet always remains the target for the cyberattacks, and attackers are getting equipped with more potent tools due to the advancement of technology to preach the security of the Internet. Industries and organizations are sponsoring many projects to avoid these kinds of problems. As a result, SDN (Software Defined Network) architecture is becoming an acceptable alternative for the traditional IP based networks which seems a better approach to defend the Internet. However, SDN is also vulnerable to many new threats because of its architectural concept. SDN might be a primary target for DoS (Denial of Service) and DDoS (Distributed Denial of Service) attacks due to centralized control and linking of data plane and control plane. In this paper, the we propose a novel technique for detection of DDoS attacks using information theory metric. We compared our approach with widely used Intrusion Detection Systems (IDSs) based on Shannon entropy and Renyi entropy, and proved that our proposed methodology has more power to detect malicious flows in SDN based networks. We have used precision, detection rate and FPR (False Positive Rate) as performance parameters for comparison, and validated the methodology using a topology implemented in Mininet network emulator.

Centrality measures on graphs have found applications in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.

The fifth-generation (5G) mobile telecom network has been garnering interest in both academia and industry, with better flexibility and higher performance compared to previous generations. Along with functionality improvements, new attack vectors also made way. Network operators and regulatory organizations wish to have a more precise idea about the security posture of 5G environments. Meanwhile, various security metrics for IT environments have been around and attracted the community’s attention. However, 5G-specific factors are less taken into consideration.This paper considers such 5G-specific factors to identify potential gaps if existing security metrics are to be applied to the 5G environments. In light of the layered nature and multi-ownership, the paper proposes a new approach to the modular computation of security metrics based on cross-layer projection as a means of information sharing between layers. Finally, the proposed approach is evaluated through simulation.

The growing complexity of server architectures, which incorporate several components with state, has necessitated rigorous assessment of the security risk both during design and operation. In this paper, we propose a novel technique to model the security risk of servers by mapping their architectures to graphs. This allows us to leverage tools from computational graph theory, which we combine with probability theory for deriving quantitative metrics for risk assessment. Probability of attack is derived for server components, with prior probabilities assigned based on knowledge of existing vulnerabilities and countermeasures. The resulting analysis is further used to compute measures of impact and exploitability of attack. The proposed methods are demonstrated on two open-source server designs with different architectures.

A code smells detection rule is a combination of metrics with their corresponding crisp thresholds and labels. The goal of this paper is to deal with metrics' thresholds uncertainty; as usually such thresholds could not be exactly determined to judge the smelliness of a particular software class. To deal with this issue, we first propose to encode each metric value into a binary possibility distribution with respect to a threshold computed from a discretization technique; using the Possibilistic C-means classifier. Then, we propose ADIPOK-UMT as an evolutionary algorithm that evolves a population of PK-NN classifiers for the detection of smells under thresholds' uncertainty. The experimental results reveal that the possibility distribution-based encoding allows the implicit weighting of software metrics (features) with respect to their computed discretization thresholds. Moreover, ADIPOK-UMT is shown to outperform four relevant state-of-art approaches on a set of commonly adopted benchmark software systems.

There are problems in which the use of linear regression is not sufficiently justified. In these cases, fuzzy linear regression can be used as a modeling tool. The problem of constructing a fuzzy linear regression can usually be reduced to a linear programming problem. One of the features of the resulting linear programming problem is that it uses a relatively large number of constraints in the form of inequalities with a relatively small number of variables. It is known that the problem of constructing a fuzzy linear regression is reduced to the problem of linear programming. If the user does not have enough computing power the resulting problem can be transferred to the cloud server. Two approaches are used for the confidential transfer of the problem to the server: the approach based on cryptographic encryption, and the transformational approach. The paper describes a protocol based on the transformational approach that allows for secure outsourcing of fuzzy linear regression.

Current network assessment models often ignore the impact of attack-defense timing on network security, making it difficult to characterize the dynamic game of attack-defense effectively. To effectively manage the network security risks and reduce potential losses, in this article, we propose a selection strategy for network defense based on a time game model. By analyzing the attack-defense status by analogy with the SIR infectious disease model, construction of an optimal defense strategy model based on time game, and calculation of the Nash equilibrium of the the attacker and the defender under different strategies, we can determine an optimal defense strategy. With the Matlab simulation, this strategy is verified to be effective.

By taking advantages of graphs and potential functions, a security reinforcement method for edge computing terminals is proposed in this paper. A risk graph of the terminal security protection system is constructed, and importance of the security protection and risks of the terminals is evaluated according to the topological potential of the graph nodes, and the weak points of the terminal are located, and the corresponding reinforcement method is proposed. The simulation experiment results show that the proposed method can upgrade and strengthen the key security mechanism of the terminal, improve the performance of the terminal security protection system, and is beneficial to the security management of the edge computing system.

In this work an overview of basic approaches to choosing protective measures for automated process control systems is done. The aim of the research was to develop a method for choosing protection measures for information security at every APCs level using set theory within analysis of basic sets of protection measures. In the framework of the research relevant attacks on industrial infrastructure are considered, an algorithm of choosing APCs protective measures is constructed, and it is suggested that it is required to use protective measures for every system level in accordance with an individual assessment of data protection class at the corresponding level. The authors concluded that it is necessary to exclude from consideration “specification of an adapted basic set” of the algorithm for choosing APCs protection measures in case the adapted basic set of APCs protective measures provides blocking all security threats at the considered system level. The approach to choosing protection measures based on building Euler-Venn diagrams is suggested. The results of the research are recommended to be used when modeling information security threats and developing requirements for APCs information protection means.

Compared with other virtualization technologies, container technology is widely used in edge computing because of its low cost, high reliability, high flexibility and fast portability. However, the use of container technology can alleviate the pressure of massive data, but also bring complex and diverse security problems. Reliable information security risk assessment method is the key to ensure the smooth application of container technology. According to the risk assessment theory, a security risk assessment method for edge computing containers based on dynamic game theory is proposed. Aiming at the complex container security attack and defense process, the container system's security model is constructed based on dynamic game theory. By combining the attack and defense matrix, the Nash equilibrium solution of the model is calculated, and the dynamic process of the mutual game between security defense and malicious attackers is analyzed. By solving the feedback Nash equilibrium solution of the model, the optimal strategies of the attackers are calculated. Finally, the simulation tool is used to solve the feedback Nash equilibrium solution of the two players in the proposed model, and the experimental environment verifies the usability of the risk assessment method.

Cyber-attacks in electrical power system causes serious damages causing breakdown of few equipment to shutdown of the complete power system. Game theory is used as a tool to detect the cyber-attack in the power system recently. Interaction between the attackers and the defenders which is the inherent nature of the game theory is exploited to detect the cyber-attack in the power system. This paper implements the cyber-attack detection on a two-area power system controlled using the Load Frequency controller. Ant Lion Optimization is used to tune the integral controller applied in the Load Frequency Controller. Cyber-attacks that include constant injection, bias injection, overcompensation, and negative compensation are tested on the Game theory-based attack detection algorithm proposed. It is considered that the smart meters are attacked with the attacks by manipulating the original data in the power system. MATLAB based implementation is developed and observed that the defender action is satisfactory in the two-area system considered. Tuning of integral controller in the Load Frequency controller in the two-area system is also observed to be effective.

Making use of spectrally diverse communications links to re-route traffic in response to dynamic environments to manage network bottlenecks has become essential in order to guarantee message delivery across heterogeneous networks. We propose an innovative, proactive Congestion Aware Intent-Based Routing (CONAIR) architecture that can select among available communication link resources based on quality of service (QoS) metrics to support continuous information exchange between networked participants. The CONAIR architecture utilizes a Network Controller (NC) and artificial intelligence (AI) to re-route traffic based on traffic priority, fundamental to increasing end user quality of experience (QoE) and mission effectiveness. The CONAIR architecture provides network behavior prediction, and can mitigate congestion prior to its occurrence unlike traditional static routing techniques, e.g. Open Shortest Path First (OSPF), which are prone to congestion due to infrequent routing table updates. Modeling and simulation (M&S) was performed on a multi-hop network in order to characterize the resiliency and scalability benefits of CONAIR over OSPF routing-based frameworks. Results demonstrate that for varying traffic profiles, packet loss and end-to-end latency is minimized.

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting.

As Edge deployments move closer towards the end devices, low latency communication among Edge aware applications is one of the key tenants of Edge service offerings. In order to simplify application development, service mesh architectures have emerged as the evolutionary architectural paradigms for taking care of bulk of application communication logic such as health checks, circuit breaking, secure communication, resiliency (among others), thereby decoupling application logic with communication infrastructure. The latency to throughput ratio needs to be measurable for high performant deployments at the Edge. Providing benchmark data for various edge deployments with Bare Metal and virtual machine-based scenarios, this paper digs into architectural complexities of deploying service mesh at edge environment, performance impact across north-south and east-west communications in and out of a service mesh leveraging popular open-source service mesh Istio/Envoy using a simple on-prem Kubernetes cluster. The performance results shared indicate performance impact of Kubernetes network stack with Envoy data plane. Microarchitecture analyses indicate bottlenecks in Linux based stacks from a CPU micro-architecture perspective and quantify the high impact of Linux's Iptables rule matching at scale. We conclude with the challenges in multiple areas of profiling and benchmarking requirement and a call to action for deploying a service mesh, in latency sensitive environments at Edge.

In many distributed learning setups such as federated learning, client nodes at the edge use individually collected data to compute the local gradients and send them to a central master server, and the master aggregates the received gradients and broadcasts the aggregation to all clients with which the clients can update the global model. As straggling communication links could severely affect the performance of distributed learning system, Prakash et al. proposed to utilize helper nodes and coding strategy to achieve resiliency against straggling client-to-helpers links. In this paper, we propose two coding schemes: repetition coding (RC) and MDS coding both of which enable the clients to update the global model in the presence of only helpers but without the master. Moreover, we characterize the uplink and downlink communication loads, and prove the tightness of uplink communication load. Theoretical tradeoff between uplink and downlink communication loads is established indicating that larger uplink communication load could reduce downlink communication load. Compared to Prakash's schemes which require a master to connect with helpers though noiseless links, our scheme can even reduce the communication load in the absence of master when the number of clients and helpers is relatively large compared to the number of straggling links.

Approximate computing emerged as a key alternative for trading off accuracy against energy efficiency and area reduction. Error-tolerant applications, such as multimedia processing, machine learning, and signal processing, can process the information with lower-than-standard accuracy at the circuit level while still fulfilling a good and acceptable service quality at the application level. Adaptive filtering-based systems have been demonstrating high resiliency against hardware errors due to their intrinsic self-healing characteristic. This paper investigates the design space exploration of arithmetic approximations in a Very Large-Scale Integration (VLSI) harmonic elimination (HE) hardware architecture based on Least Mean Square (LMS) adaptive filters. We evaluate the Pareto front of the area- and power versus quality curves by relaxing the arithmetic precision and by adopting both approximate multipliers (AxMs) in combination with approximate adders (AxAs). This paper explores the benefits and impacts of the Dynamic Range Unbiased (DRUM), Rounding-based Approximate (RoBA), and Leading one Bit-based Approximate (LoBA) multipliers in the power dissipation, circuit area, and quality of the VLSI HE architectures. Our results highlight the LoBA 0 as the most efficient AxM applied in the HE architecture. We combine the LoBA 0 with Copy and LOA AxAs with variations in the approximation level (L). Notably, LoBA 0 and LOA with \$L=6\$ resulted in savings of 43.7% in circuit area and 45.2% in power dissipation, compared to the exact HE, which uses multiplier and adder automatically selected by the logic synthesis tool. Finally, we demonstrate that the best hardware architecture found in our investigation successfully eliminates the contaminating spurious noise (i.e., 60 Hz and its harmonics) from the signal.

Maintaining critical data and process availability is an important challenge of Industry 4.0. Given the variety of smart nodes, data and the access latency that can be tolerated by consumers in modern IoT-based industry, we propose a method for analyzing the resiliency of an IoT network. Due to the complexity of modern system structures, different components in the system can affect the system’s resiliency. Therefore, a fundamental problem is to propose methods to quantify the value of resilience contribution of a node in each system effectively. This paper aims to identify the most critical vertices of the network with respect to the latency constraint resiliency metric. Using important centrality metrics, we identify critical nodes in industrial IoT networks to analyze the degree of resiliency in the IoT environments. The results show that when nodes with the highest value of Closeness Centrality (CC) were disrupted Resiliency of Latency (RL) would have the lowest value. In other words, the results indicate the nodes with the high values for CC are most critical in an IoT network.

This paper presents a new framework for SATCOM jamming resiliency in the presence of a smart adversary jammer that can prioritize specific channels to attack with a non-uniform probability of distribution. We first develop a model and a defense action strategy based on a Markov decision process (MDP). We propose a greedy algorithm for the MDP-based defense algorithm's policy to optimize the expected user's immediate and future discounted rewards. Next, we remove the assumption that the user has specific information about the attacker's pattern and model. We develop a Q-learning algorithm-a reinforcement learning (RL) approach-to optimize the user's policy. We show that the Q-learning method provides an attractive defense strategy solution without explicit knowledge of the jammer's strategy. Computer simulation results show that the MDP-based defense strategies are very efficient; they offer a significant data rate advantage over the simple random hopping approach. Also, the proposed Q-learning performance can achieve close to the MDP approach without explicit knowledge of the jammer's strategy or attacking model.

Due to the shortage of resources and services, agents are often in competition with each other. Excessive competition will lead to a social dilemma. Under the viewpoint of breaking social dilemma, we present a novel trust-based logic framework called Trust Computation Logic (TCL) for measure method to find the best partners to collaborate and automatically verifying trust in Multi-Agent Systems (MASs). TCL starts from defining trust state in Multi-Agent Systems, which is based on contradistinction between behavior in trust behavior library and in observation. In particular, a set of reasoning postulates along with formal proofs were put forward to support our measure process. Moreover, we introduce symbolic model checking algorithms to formally and automatically verify the system. Finally, the trust measure method and reported experimental results were evaluated by using DeepMind’s Sequential Social Dilemma (SSD) multi-agent game-theoretic environments.

Privacy and data security are critical aspects in databases, mainly when the latter are publically accessed such in social networks. Furthermore, for advanced databases, such as NoSQL ones, security models and security meta-data must be integrated to the business specification and data. In the literature, the proposed models for NoSQL databases can be considered as static, in the sense where the privileges for a given user are predefined and remain unchanged during job sessions. In this paper, we propose a novel model for NoSQL database access control that we aim that it will be dynamic. To be able to design such model, we have considered the Trust concept to compute the reputation degree for a given user that plays a given role.

The main technology of traditional information security is firewall, intrusion detection and anti-virus software, which is used in the first anti-outer defence, the first anti-service terminal defence terminal passive defence ideas, the complexity and complexity of these security technologies not only increase the complexity of the autonomous system, reduce the efficiency of the system, but also cannot solve the security problem of the information system, and cannot satisfy the security demand of the information system. After a significant stretch of innovative work, individuals utilize the secret word innovation, network security innovation, set forward the idea “confided in figuring” in view of the equipment security module support, Trusted processing from changing the customary protection thoughts, center around the safety efforts taken from the terminal to forestall framework assaults, from the foundation of the stage, the acknowledgment of the security of data frameworks. Believed figuring is chiefly worried about the security of the framework terminal, utilizing a progression of safety efforts to ensure the protection of clients to work on the security of independent frameworks. Its principle plan thought is implanted in a typical machine to oppose altering the equipment gadget - confided in stage module as the base of the trust, the utilization of equipment and programming innovation to join the trust of the base of trust through the trust bind level to the entire independent framework, joined with the security of information stockpiling insurance, client validation and stage respectability of the three significant safety efforts guarantee that the terminal framework security and unwavering quality, to guarantee that the terminal framework is consistently in a condition of conduct anticipated.