Biblio

With the development of industrial informatization, information security in the power production industry is becoming more and more important. In the power production industry, as the critical information egress of the industrial control system, the information security of the Networked Control System is particularly important. This paper proposes a construction method for an information security platform of Networked Control System, which is used for research, testing and training of Networked Control System information security.

The safety of the power system is inherently vital, due to the high risk of the electronic power system. In the wave of digitization in recent years, many power systems have been digitized to a certain extent. Under this circumstance, network security is particularly important, in order to ensure the normal operation of the power system. However, with the development of the Internet, network security issues are becoming more and more serious. Among all kinds of network attacks, the Distributed Denial of Service (DDoS) is a major threat. Once, attackers used huge volumes of traffic in short time to bring down the victim server. Now some attackers just use low volumes of traffic but for a long time to create trouble for attack detection. There are many methods for DDoS detection, but no one can fully detect it because of the huge volumes of traffic. In order to better detect DDoS and make sure the safety of electronic power system, we propose a novel detection method based on neural network. The proposed model and its service are deployed to the edge cloud, which can improve the real-time performance for detection. The experiment results show that our model can detect attacks well and has good real-time performance.

A power amplifier(PA) is inherently nonlinear device and is used in a communication system widely. Due to the nonlinearity of PA, the communication system is hard to work well. Digital predistortion (DPD) is the way to solve this problem. Using Volterra function to fit the PA is what most DPD solutions do. However, when it comes to wideband signal, there is a deduction on the performance of the Volterra function. In this paper, we replace the Volterra function with B-spline function which performs better on fitting PA at wideband signal. And the other benefit is that the orthogonality of coding matrix A could be improved, enhancing the stability of computation. Additionally, we use compressive sampling to reduce the complexity of the function model.

Without security, any network system loses its efficiency, reliability, and resilience. With the huge integration of the ICT capabilities, the Electric Vehicle (EV) as a transportation form in cities is becoming more and more affordable and able to reply to citizen and environmental expectations. However, the EV vulnerability to cyber-attacks is increasing which intensifies its negative impact on societies. This paper targets the cybersecurity issues for Connected Electric Vehicles (CEVs) in parking lots where a peer-to-peer(P2P) energy transaction system is launched. A False Data Injection Attack (FDIA) on the electricity price signal is considered and a Machine Learning/SVM classification protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness of this proposed model.

In the near future, the high data rate challenge would not be possible by using the radio frequency (RF) only. As the user will increase, the network traffic will increase proportionally. Visible light communication (VLC) is a good solution to support huge number of indoor users. VLC has high data rate over RF communication. The way internet users are increasing, we have to think over VLC technology. Not only the data rate is a concern but also its security, cost, and reliability have to be considered for a good communication network. Quantum technology makes a great impact on communication and computing in both areas. Quantum communication technology has the ability to support better channel capacity, higher security, and lower latency. This paper combines the quantum technology over the existing VLC and compares the performance between quantum visible light communication performance (QVLC) over the existing VLC system. Research findings clearly show that the performance of QVLC is better than the existing VLC system.

Most existing deep neural networks (DNNs) are inexplicable and fragile, which can be easily deceived by carefully designed adversarial example with tiny undetectable noise. This allows attackers to cause serious consequences in many DNN-assisted scenarios without human perception. In the field of speaker recognition, the attack for speaker recognition system has been relatively mature. Most works focus on white-box attacks that assume the information of the DNN is obtainable, and only a few works study gray-box attacks. In this paper, we study blackbox attacks on the speaker recognition system, which can be applied in the real world since we do not need to know the system information. By combining the idea of transferable attack and query attack, our proposed method NMI-FGSM-Tri can achieve the targeted goal by misleading the system to recognize any audio as a registered person. Specifically, our method combines the Nesterov accelerated gradient (NAG), the ensemble attack and the restart trigger to design an attack method that generates the adversarial audios with good performance to attack blackbox DNNs. The experimental results show that the effect of the proposed method is superior to the extant methods, and the attack success rate can reach as high as 94.8% even if only one query is allowed.

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.

Nowadays, cloud computing has become one of the most important and easily available storage options. This paper represents providing a platform where the data redundancy and the data security is maintained. Materials and Methods: This study contains two groups, the elliptic curve cryptography is developed in group 1 with 480 samples and advanced encryption is developed in group 2 with 960 samples. The accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Result: Advanced elliptic curve cryptography algorithm provides 1.2 times better performance compared to conventional elliptic curve cryptography algorithm for various datasets. The results were obtained with a significance value of 0.447 (p\textgreater0.05). Conclusion: From the obtained results the advanced elliptic curve cryptography algorithm seems to be better than the conventional algorithm.

We investigate the fuzzy adaptive compensation control problem for nonlinear cyber-physical system with false data injection attack over digital communication links. The fuzzy logic system is first introduced to approximate uncertain nonlinear functions. And the time-varying sliding mode surface is designed. Secondly, for the actual require-ment of data transmission, three uniform quantizers are designed to quantify system state and sliding mode surface and control input signal, respectively. Then, the adaptive fuzzy laws are designed, which can effectively compensate for FDI attack and the quantization errors. Furthermore, the system stability and the reachability of sliding surface are strictly guaranteed by using adaptive fuzzy laws. Finally, we use an example to verify the effectiveness of the method.

Metaverse technologies depend on various advanced human-computer interaction (HCI) devices to be supported by extended reality (XR) technology. Many new HCI devices are supported by wireless Internet of Things (IoT) networks, where a reliable routing scheme is essential for seamless data trans-mission. Routing Protocol for Low power and Lossy networks (RPL) is a key routing technology used in IPv6-based low power and lossy networks (LLNs). However, in the networks that are configured, such as small wireless devices applying the IEEE 802.15.4 standards, due to the lack of a system that manages the identity (ID) at the center, the maliciously compromised nodes can make fabricated IDs and pretend to be a legitimate node. This behavior is called Sybil attack, which is very difficult to respond to since attackers use multiple fabricated IDs which are legally disguised. In this paper, Sybil attack countermeasures on RPL-based networks published in recent studies are compared and limitations are analyzed through simulation performance analysis.

Securing optical edge devices across an optical network is a critical challenge for the technological capabilities of fog/cloud computing. Locating and blocking rogue devices from transmitting data frames in an optical network is a significant security problem due to their widespread distribution over the optical fog cloud. A malicious actor might simply compromise such a device and execute assaults that degrade the optical channel’s Quality. In this study, we advocate an innovative framework for the use of an optical network to facilitate cloud and fog computing in a safe environment. This framework is sustainable and able to detect hostile equipment in optical fog and cloud and redirect it to a honeypot, where the assault may be halted and analyzed. To do this, it employs a model based on a two-stage hidden Markov, a fog manager based on an intrusion detection system, and an optical virtual honeypot. An internal assault is mitigated by simulated testing of the suggested system. The findings validate the adaptable and affordable access for cloud computing and optical fog.

Frauds in supply chain finance not only result in substantial loss for financial institutions (e.g., banks, trust company, private funds), but also are detrimental to the reputation of the ecosystem. However, such frauds are hard to detect due to the complexity of the operating environment in supply chain finance such as involvement of multiple parties under different agreements. Traditional instruments of financial institutions are time-consuming yet insufficient in countering fraudulent supply chain financing. In this study, we propose a novel blockchain-driven framework for deterring fraud in supply chain finance. Specifically, we use inventory financing in jewelry supply chain as an illustrative scenario. The blockchain technology enables secure and trusted data sharing among multiple parties due to its characteristics of immutability and traceability. Consequently, information on manufacturing, brand license, and warehouse status are available to financial institutions in real time. Moreover, we develop a novel rule-based fraud check module to automatically detect suspicious fraud cases by auditing documents shared by multiple parties through a blockchain network. To validate the effectiveness of the proposed framework, we employ agent-based modeling and simulation. Experimental results show that our proposed framework can effectively deter fraudulent supply chain financing as well as improve operational efficiency.

A novel secure physical layer key generation method for Connected and Autonomous Vehicles (CAVs) against an attacker is proposed under fading and Additive White Gaussian Noise (AWGN). In the proposed method, a random sequence key is added to the demodulated sequence to generate a unique pre-shared key (PSK) to enhance security. Extensive computer simulation results proved that an attacker cannot extract the same legitimate PSK generated by the received vehicle even if identical fading and AWGN parameters are used both for the legitimate vehicle and attacker.

Middlebox is primarily used in Software-Defined Network (SDN) to enhance operational performance, policy compliance, and security operations. Therefore, security of the middlebox itself is essential because incorrect use of the middlebox can cause severe cybersecurity problems for SDN. Existing attacks against middleboxes in SDN (for instance, middleboxbypass attack) use methods such as cloned tags from the previous packets to justify that the middlebox has processed the injected packet. Flowcloak as the latest solution to defeat such an attack creates a defence using a tag by computing the hash of certain parts of the packet header. However, the security mechanisms proposed to mitigate these attacks are compromise-able since all parts of the packet header can be imitated, leaving the middleboxes insecure. To demonstrate our claim, we introduce a novel attack against SDN middleboxes by hijacking TCP/IP headers. The attack uses crafted TCP/IP headers to receive the tags and signatures and successfully bypasses the middleboxes.

The reigning U.S. paradigm for deterring malicious cyberspace activity carried out by or condoned by other countries is to levy penalties on them. The results have been disappointing. There is little evidence of the permanent reduction of such activity, and the narrative behind the paradigm presupposes a U.S./allied posture that assumes the morally superior role of judge upon whom also falls the burden of proof–-a posture not accepted but nevertheless exploited by other countries. In this paper, we explore an alternative paradigm, obnoxious deterrence, in which the United States itself carries out malicious cyberspace activity that is used as a bargaining chip to persuade others to abandon objectionable cyberspace activity. We then analyze the necessary characteristics of this malicious cyberspace activity, which is generated only to be traded off. It turns out that two fundamental criteria–that the activity be sufficiently obnoxious to induce bargaining but be insufficiently valuable to allow it to be traded away–may greatly reduce the feasibility of such a ploy. Even if symmetric agreements are easier to enforce than pseudo-symmetric agreements (e.g., the XiObama agreement of 2015) or asymmetric red lines (e.g., the Biden demand that Russia not condone its citizens hacking U.S. critical infrastructure), when violations occur, many of today’s problems recur. We then evaluate the practical consequences of this approach, one that is superficially attractive.

As the demand for effective information protection grows, security has become the primary concern in protecting such data from attackers. Cryptography is one of the methods for safeguarding such information. It is a method of storing and distributing data in a specific format that can only be read and processed by the intended recipient. It offers a variety of security services like integrity, authentication, confidentiality and non-repudiation, Malicious. Confidentiality service is required for preventing disclosure of information to unauthorized parties. In this paper, there are no ideal hash functions that dwell in digital signature concepts is proved.

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage.

The increased dissemination of open source software to a broader audience has led to a proportional increase in the dissemination of vulnerabilities. These vulnerabilities are introduced by developers, some intentionally or negligently. In this paper, we work to quantity the relative risk that a given developer represents to a software project. We propose using empirical software engineering based analysis on the vast data made available by GitHub to create a Developer Risk Score (DRS) for prolific contributors on GitHub. The DRS can then be aggregated across a project as a derived vulnerability assessment, we call this the Computational Vulnerability Assessment Score (CVAS). The CVAS represents the correlation between the Developer Risk score across projects and vulnerabilities attributed to those projects. We believe this to be a contribution in trying to quantity risk introduced by specific developers across open source projects. Both of the risk scores, those for contributors and projects, are derived from an amalgamation of data, both from GitHub and outside GitHub. We seek to provide this risk metric as a force multiplier for the project maintainers that are responsible for reviewing code contributions. We hope this will lead to a reduction in the number of introduced vulnerabilities for projects in the Open Source ecosystem.

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework.

TVM(tensor virtual machine) as a deep learning compiler which supports the conversion of machine learning models into TVM IR(intermediate representation) and to optimise the generation of high-performance machine code for various hardware platforms. While the traditional approach is to parallelise the cyclic transformations of operators, in this paper we partition the implementation of the operators in the deep learning compiler TVM with parallel scheduling to derive a faster running time solution for the operators. An optimisation algorithm for partitioning and parallel scheduling is designed for the deep learning compiler TVM, where operators such as two-dimensional convolutions are partitioned into multiple smaller implementations and several partitioned operators are run in parallel scheduling to derive the best operator partitioning and parallel scheduling decisions by means of performance estimation. To evaluate the effectiveness of the algorithm, multiple examples of the two-dimensional convolution operator, the average pooling operator, the maximum pooling operator, and the ReLU activation operator with different input sizes were tested on the CPU platform, and the performance of these operators was experimentally shown to be improved and the operators were run speedily.

Traditional risk assessment process based on knowledge of threat occurrence probability against every system’s asset. One should consider asset placement, applied security measures on asset and network levels, adversary capabilities and so on: all of that has significant influence on probability value. We can measure threat probability by modelling complex attack process. Such process requires creating an attack tree, which consist of elementary attacks against different assets and relations between elementary attacks and impact on influenced assets. However, different attack path may lead to targeted impact – so task of finding optimal attack chain on a given system topology emerges. In this paper method for complex attack graph creation presented, allowing automatic building various attack scenarios for a given system. Assuming that exploits of particular vulnerabilities represent by independent events, we can compute the overall success probability of a complex attack as the product of the success probabilities of exploiting individual vulnerabilities. This assumption makes it possible to use algorithms for finding the shortest paths on a directed graph to find the optimal chain of attacks for a given adversary’s target.

This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm returns an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a modelling approach for power networks subject to D-LAAs, suitable for the designed robust control protection scheme. The paper also proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases. The approach is evaluated by numerical simulations on large networks.

Energy trading in small groups or microgrids is interesting to study. The energy market may overgrow in the future, so accessing the energy market by small prosumers may not be difficult anymore. This paper has modeled a decentralized P2P energy trading and exchange system in a microgrid group. The Islanded microgrid system is simulated to create a small energy producer and consumer trading situation. The simulation results show the increasing energy transactions and profit when including V2G as an energy storage device. In addition, blockchain is used for system security because a peer-to-peer marketplace has no intermediary control.

Wrist-worn devices enable access to essential information and they are suitable for a wide range of applications, such as gesture and activity recognition. Wrist-worn devices require appropriate technologies when used in sensitive areas, overcoming vulnerabilities in regard to security and privacy. In this work, we propose an approach to recognize wrist rotation by utilizing Visible Light Communication (VLC) that is enabled by low-cost LEDs in an indoor environment. In this regard, we address the channel model of a VLC communicating wristband (VLCcw) in terms of the following factors. The directionality and the spectral composition of the light and the corresponding spectral sensitivity and the directional characteristics of the utilized photodiode (PD). We verify our VLCcw from the simulation environment by a small-scale experimental setup. Then, we analyze the system when white and RGBW LEDs are used. In addition, we optimized the VLCcw system by adding more receivers for the purpose of reducing the number of LEDs on VLCcw. Our results show that the proposed approach generates a feasible real-world simulation environment.

Tourism is one of the main sources of income in Australia. The number of tourists will affect airlines, hotels and other stakeholders. Predicting the arrival of tourists can make full preparations for welcoming tourists. This paper selects Queensland Tourism data as intelligent data. Carry out data visualization around the intelligent data, establish seasonal ARIMA model, find out the characteristics and predict. In order to improve the accuracy of prediction. Based on the tourism data around Queensland, build a 10 layer Back Propagation neural network model. It is proved that the network shows good performance for the data prediction of this paper.