Biblio

In this work different Meta-heuristic Techniques have been endeavored for addressing the Security Constrained Optimal Power Flow (SCOPF) and Optimal Power Flow (OPF)problem for minimizing the total fuel cost of the system. Here four meta-heuristics i.e. Genetic Algorithm (GA), Big Bang-Big Crunch Algorithm (BBBC), Shuffled Frog Leap Algorithm (SFLA) and Jaya Algorithms (JA) have been discussed. The problem was simulated on IEEE 30 bus standard test system under MATLAB environment. The simulation results show that JA outperforms GA, SFLA, and BBBC in terms of overall cost and computational time.

For the Internet of things (IoT) secure data aggregation issues, data privacy-preserving and limited computation ability and energy of nodes should be tradeoff. Based on analyzing the pros-and-cons of current works, a low energy- consuming secure data aggregation method (LCSDA) was proposed. This method uses shortest path principle to choose neighbor nodes and generates the data aggregation paths in the cluster based on prim minimum spanning tree algorithm. Simulation results show that this method could effectively cut down energy consumption and reduce the probability of cluster head node being captured, in the same time preserving data privacy.

In recent times, Network-on-Chip (NoC) has become state of the art for communication in Multiprocessor System-on-Chip due to the existing scalability issues in this area. However, these systems are exposed to security threats such as extraction of secret information. Therefore, the need for secure communication arises in such environments. In this work, we present a communication protocol based on authenticated encryption with recovery mechanisms to establish secure end-to-end communication between the NoC nodes. In addition, a selected key agreement approach required for secure communication is implemented. The security functionality is located in the network adapter of each processing element. If data is tampered with or deleted during transmission, recovery mechanisms ensure that the corrupted data is retransmitted by the network adapter without the need of interference from the processing element. We simulated and implemented the complete system with SystemC TLM using the NoC simulation platform PANACA. Our results show that we can keep a high rate of correctly transmitted information even when attackers infiltrated the NoC system.

Physical layer security is an emerging security area to tackle wireless security communications issues and complement conventional encryption-based techniques. Thus, we propose a novel scheme based on swarm intelligence optimization technique and a deep neural network (DNN) for maximizing the secrecy energy efficiency (SEE) in a cooperative relaying underlay cognitive radio- and non-orthogonal multiple access (NOMA) system with a non-linear energy harvesting user which is exposed to multiple eavesdroppers. Satisfactorily, simulation results show that the proposed particle swarm optimization (PSO)-DNN framework achieves close performance to that of the optimal solutions, with a meaningful reduction in computation complexity.

This paper introduces an application of machine learning algorithms. In fact, support vector machine and decision tree approaches are studied and applied to compare their performances in detecting, classifying, and locating faults in the transmission network. The IEEE 14-bus transmission network is considered in this work. Besides, 13 types of faults are tested. Particularly, the one fault and the multiple fault cases are investigated and tested separately. Fault simulations are performed using the SimPowerSystems toolbox in Matlab. Basing on the accuracy score, a comparison is made between the proposed approaches while testing simple faults, on the one hand, and when complicated faults are integrated, on the other hand. Simulation results prove that the support vector machine technique can achieve an accuracy of 87% compared to the decision tree which had an accuracy of 53% in complicated cases.

This paper addresses the issues in managing group key among clusters in Mobile Ad hoc Networks (MANETs). With the dynamic movement of the nodes, providing secure communication and managing secret keys in MANET is difficult to achieve. In this paper, we propose a distributed secure broadcast stateless groupkey management framework (DSBS-GKM) for efficient group key management. This scheme combines the benefits of hash function and Lagrange interpolation polynomial in managing MANET nodes. To provide a strong security mechanism, a revocation system that detects and revokes misbehaviour nodes is presented. The simulation results show that the proposed DSBS-GKM scheme attains betterments in terms of rekeying and revocation performance while comparing with other existing key management schemes.

This study addresses the coordination issue of multi-agent systems under complicated actuator faults and cyber attacks. Distributed fault-tolerant design is developed with the estimated and output neighboring information in decentralized estimation observer. Criteria of reaching the exponential coordination of multi-agent systems with cyber attacks is obtained with average dwelling time and chattering bound method. Simulations validate the efficiency of the anti-attack fault-tolerant design.

This publication deals with the robust attitude stabilization of a quadrotor subject to stealthy actuator attacks. Based first on the nonlinear model of the system, the sector non-linearity approach will be applied in order to deduce a polytopic Takagi-sugeno model. In parallel, a polytopic fuzzy T-S modeling of the data-deception malicious attacks (time-varying parameters) is presented. After some mathematical development, it will be shown that our original nonlinear system subject to stealthy actuator attacks can be represented as an uncertain polytopic T-S system. Based on this latest model, basic concepts for attitude stabilization will be used to implement the control law. The stabilization conditions will be given in terms of Linear Matrix Inequalities (LMIs) deduced from a classical Lyapunov approach. In order to highlight the efficiency of the proposed approach, simulation results will be given.

From the perspective of time domain, the propagation characteristics of sound waves in seawater can be seen more intuitively. In order to study the influence and characteristics of seamount on low frequency acoustic propagation, the research of this paper used the Finite Element Method (FEM) based on time domain to set up a full-waveguide low-frequency acoustic propagation simulation model, and discussed the influencing laws about acoustic propagation on seamount. The simulation results show that Seamounts can hinder the propagation of sound waves, weaken the energy of sound waves. The topographic changes of seamounts can cause the coupling and transformation of acoustic signals during the propagation which can stimulate the seabed interface wave.

Routing protocol for low power and lossy networks (RPL) is the underlying routing protocol of 6LoWPAN, a core communication standard for the Internet of Things. In terms of quality of service (QoS), device management, and energy efficiency, RPL beats competing wireless sensor and ad hoc routing protocols. However, several attacks could threaten the network due to the problem of unauthenticated or unencrypted control frames, centralized root controllers, compromised or unauthenticated devices. Thus, in this paper, we aim to investigate the effect of topology and Resources attacks on RPL.s efficiency. The Hello Flooding attack, Increase Number attack and Decrease Rank attack are the three forms of Resources attacks and Topology attacks respectively chosen to work on. The simulations were done to understand the impact of the three different attacks on RPL performances metrics including End-to-End Delay (E2ED), throughput, Packet Delivery Ratio (PDR) and average power consumption. The findings show that the three attacks increased the E2ED, decreased the PDR and the network throughput, and degrades the network’, which further raises the power consumption of the network nodes.

The most widely used protocol for routing across the 6LoWPAN stack is the Routing Protocol for Low Power and Lossy (RPL) Network. However, the RPL lacks adequate security solutions, resulting in numerous internal and external security vulnerabilities. There is still much research work left to uncover RPL's shortcomings. As a result, we first implement the worst parent selection (WPS) attack in this paper. Second, we offer an intrusion detection system (IDS) to identify the WPS attack. The WPS attack modifies the victim node's objective function, causing it to choose the worst node as its preferred parent. Consequently, the network does not achieve optimal convergence, and nodes form the loop; a lower rank node selects a higher rank node as a parent, effectively isolating many nodes from the network. In addition, we propose DWA-IDS as an IDS for detecting WPS attacks. We use the Contiki-cooja simulator for simulation purposes. According to the simulation results, the WPS attack reduces system performance by increasing packet transmission time. The DWA-IDS simulation results show that our IDS detects all malicious nodes that launch the WPS attack. The true positive rate of the proposed DWA-IDS is more than 95%, and the detection rate is 100%. We also deliberate the theoretical proof for the false-positive case as our DWA-IDS do not have any false-positive case. The overhead of DWA-IDS is modest enough to be set up with low-power and memory-constrained devices.

With the rapid development of the Internet of Things (IoT), a large amount of data is exchanged between various communicating devices. Since the data should be communicated securely between the communicating devices, the network security is one of the dominant research areas for the 6LoWPAN IoT applications. Meanwhile, 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. Thus intrusion detection systems have become more and more critical and play a noteworthy role in improving the 6LoWPAN IoT networks. However, most intrusion detection systems focus on the attacked areas in the IoT networks instead of precisely on certain IoT nodes. This may lead more resources to further detect the compromised nodes or waste resources when detaching the whole attacked area. In this paper, we therefore proposed a new precisional detection strategy for 6LoWPAN Networks, named as PDS-6LoWPAN. In order to validate the strategy, we evaluate the performance and applicability of our solution with a thorough simulation by taking into account the detection accuracy and the detection response time.

This work analyzed the coding gain that is provided in 6LoWPAN transceivers when channel-coding methods are used. There were made improvements at physical layer of 6LoWPAN technology in the system suggested. Performance analysis was performed using turbo, LDPC and convolutional codes on IEEE 802.15.4 standard that is used in the relevant physical layer. Code rate of convolutional and turbo codes are set to 1/3 and 1/4. For LDPC codes, the code rate is set as 3/4 and 5/6. According to simulation results obtained from the MATLAB environment, turbo codes give better results than LDPC and convolutional codes. It is seen that an average of 3 dB to 8 dB gain is achieved in turbo codes, in LDPC and convolutional coding, it is observed that the gain is between 2 dB and 6 dB depending on the modulation type and code rate.

This paper proposes a RSSI-based routing protocol for smart pole mesh networks equipped with multiple IEEE 802.11p/WAVE radios. In the IEEE 802.11p based multi-radio multi-channel environments, the performance of traditional mesh routing protocols is severely degraded because of metric measurement overhead. The periodic probe messages for measuring the quality of each channel incurs a large overhead due to the channel switching delay. To solve such an overhead problem, we introduce a routing metric that estimates expected transmission time and proposes a light-weight channel allocation algorithm based on RSSI value only. We evaluate the performance of the proposed solution through simulation experiments with NS-3. Simulation results show that it can improve the network performance in terms of latency and throughput, compared to the legacy WCETT routing scheme.

This paper proposes an improved version of the newly developed Honey Badger Algorithm (HBA), called Generalized opposition Based-Learning HBA (GOBL-HBA), for solving the mesh routers placement problem. The proposed GOBLHBA is based on the integration of the generalized opposition-based learning strategy into the original HBA. GOBL-HBA is validated in terms of three performance metrics such as user coverage, network connectivity, and fitness value. The evaluation is done using various scenarios with different number of mesh clients, number of mesh routers, and coverage radius values. The simulation results revealed the efficiency of GOBL-HBA when compared with the classical HBA, Genetic Algorithm (GA), and Particle Swarm optimization (PSO).

Although DC microgrids using a distributed cooperative control architecture can avoid the instability or shutdown issues caused by a single-point failure as compared to the centralized approach, limited global information in the former makes it difficult to detect cyber attacks. Here, we present a false data injection attack (FDIA)–-termed as a local control input attack–-targeting voltage observers in the secondary controllers and control loops in the primary controllers. Such an attack cannot be detected by only observing the performance of the estimated voltage of each agent, thereby posing a potential threat to the system operation. To address this, a detection method using the outputs of the voltage observers is developed to identify the exact location of an FDIA. The proposed approach is based on the characteristics of the distributed cooperative network and avoids heavy dependency on the system model parameters. Next, an event-driven mitigation approach is deployed to substitute the attacked element with a reconstructed signal upon the detection of an attack. Finally, the effectiveness of the proposed resilient scheme is validated using simulation results.

Non-orthogonal multiple access (NOMA) is emerging as a popular radio access technique to serve multiple users under the same resource block to improve spectral efficiency in 5G and 6G communication. But the resource sharing in NOMA causes concerns on data security. Since power domain NOMA exploits the difference in channel properties for bandwidth-efficient communication, it is feasible to ensure data confidentiality in NOMA communication through physical layer security techniques. In this work, we propose to ensure resistance against internal eavesdropping in NOMA communication through a secret key derived from channel randomness. A unique secret key is derived from the channel of each NOMA user; which is used to randomize the data of the respective user before superposition coding (SC) to prevent internal eavesdropping. The simulation results show that the proposed system provides very good security against internal eavesdropping in NOMA.

In this paper we use a seeded modular coding scheme for implementing physical layer security in a wiretap scenario. This modular scheme consists of a traditional coding layer and a security layer. For the traditional coding layer, we use a polar code. We evaluate the performance of the seeded modular coding scheme in an experimental setup with software defined radios and compare these results to simulation results. In order to assess the secrecy level of the scheme, we employ the distinguishing security metric. In our experiments, we compare the distinguishing error rate for different seeds and block lengths.

Cyber-Physical Systems(CPS) have been experiencing a fast-growing process in recent decades, and related security issues also have become more important than ever before. To design an efficient defensive policy for operators and controllers is the utmost task to be considered. In this paper, a stochastic game-theoretic model is developed to study a CPS security problem by considering the interdependence between cyber and physical spaces of a CPS. The game model is solved with Minimax Q-learning for finding the mixed strategies equilibria. The numerical simulation revealed that the defensive factors and attack cost can affect the policies adopted by the system. From the perspective of the operator of a CPS, increasing successful defense probability in the phrase of disruption will help to improve the probability of defense strategy when there is a correlation between the cyber layer and the physical layer in a CPS. On the contrary side, the system defense probability will decrease as the total cost of the physical layer increases.

SUMMARY & CONCLUSIONSA Multi-access Edge Computing (MEC) micro data center (MEDC) consists of multiple MEC hosts close to endpoint devices. MEC service is delivered by instantiating a virtualization system (e.g., Virtual Machines or Containers) on a MEC host. MEDC faces more new security risks due to various device connections in an open environment. When more and more IoT/CPS systems are connected to MEDC, it is necessary for MEC service providers to quantitatively analyze any security loss and then make defense-related decision. This paper develops a CTMC model for quantitatively analyzing the security and dependability of a vulnerable MEDC system under lateral movement attacks, from the adversary’s initial successful access until the MEDC becomes resistant to the attack. The proposed model captures the behavior of the system in a scenario where (i) the rate of vulnerable MEC servers being infected increases with the increasing number of infected MEC servers, (ii) each infected MEC server can perform its compromising activity independently and randomly, and (iii) any infected MEC may fail and then cannot provide service. We also introduce the formulas for computing metrics. The proposed model and formula are verified to be approximately accurate by comparing numerical results and simulation results.

The network attack such as Distributed Denial-of-Service (DDoS) attack could be critical to latency-critical systems such as Mobile Edge Computing (MEC) as such attacks significantly increase the response delay of the victim service. Intrusion prevention system (IPS) is a promising solution to defend against such attacks, but there will be a trade-off between IPS deployment and application resource reservation as the deployment of IPS will reduce the number of computation resources for MEC applications. In this paper, we proposed a game-theoretic framework to study the joint computation resource allocation and IPS deployment in the MEC architecture. We study the pricing strategy of the MEC platform operator and purchase strategy of the application service provider, given the expected attack strength and end user demands. The best responses of both MPO and ASPs are derived theoretically to identify the Stackelberg equilibrium. The simulation results confirm that the proposed solutions significantly increase the social welfare of the system.

Nowadays, source location privacy in underwater acoustic sensor networks (UASNs) has gained a lot of attention. The aim of source location privacy is to use specific technologies to protect the location of the source from being compromised. Among the many technologies available are fake packet technology, multi-path routing technology and so on. The fake packet technology uses a certain amount of fake packets to mask the transmission of the source packet, affecting the adversary's efficiency of hop-by-hop backtracking to the source. However, during the operation of the fake packet technology, the fake packet, and the source packet may interfere with each other. Focus on this, a fake packet time slot assignment-based source location privacy protection (FPTSA-SLP) scheme. The time slot assignment is adopted to avoid interference with the source packet. Also, a relay node selection method based on the handshake is further proposed to increase the diversity of the routing path to confuse the adversary. Compared with the comparison algorithm, the simulation results demonstrate that the proposed scheme has a better performance in safety time.

Specific emitter identification (SEI) is a physical-layer-based approach for enhancing wireless communication network security. A well-done SEI method can be widely applied in identifying the individual wireless communication device. In this paper, we propose a novel specific emitter identification method based on variational mode decomposition and histogram of oriented gradient (VMD-HOG). The signal is decomposed into specific temporal modes via VMD and HOG features are obtained from the time-frequency spectrum of temporal modes. The performance of the proposed method is evaluated both in single hop and relaying scenarios and under three channels with the number of emitters varying. Results depict that our proposed method provides great identification performance for both simulated signals and realistic data of Zigbee devices and outperforms the two existing methods in identification accuracy and computational complexity.

Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of consensus algorithms that underlie many multi-agent control and optimization techniques. However, sharing the value of algebraic connectivity can inadvertently reveal sensitive information about the topology of a graph, such as connections in social networks. Therefore, in this work we present a method to release a graph’s algebraic connectivity under a graph-theoretic form of differential privacy, called edge differential privacy. Edge differential privacy obfuscates differences among graphs’ edge sets and thus conceals the absence or presence of sensitive connections therein. We provide privacy with bounded Laplace noise, which improves accuracy relative to conventional unbounded noise. The private algebraic connectivity values are analytically shown to provide accurate estimates of consensus convergence rates, as well as accurate bounds on the diameter of a graph and the mean distance between its nodes. Simulation results confirm the utility of private algebraic connectivity in these contexts.

Traditional passive defense methods cannot resist the constantly updated and evolving cyber attacks. The concept of resilience is introducing to measure the ability of the system to maintain its function under attack. It matters in evaluating the security of modern industrial systems. This paper presents a 3D Resilience State Space method to assess Communication-based train control (CBTC) system resilience under malware attack. We model the spread of malware as two functions: the communicability function \$f\$(x) and the susceptibility function 9 (x). We describe the characteristics of these two function in the CBTC complex network by using the percolation theory. Then we use a perturbation formalism to analyze the impact of malware attack on information flow and use it as an indicator of the cyber layer state. The CBTC cyber-physical system resilience metric formalizes as the system state transitions in three-dimensional state space. The three dimensions respectively represent the cyber layer state, the physical layer state, and the transmission layer state. The simulation results reveal that the proposed framework can effectively assess the resilience of the CBTC system. And the anti-malware programs can prevent the spread of malware and improve CBTC system resilience.