Biblio

The development of industrial automation tools and the integration of industrial and corporate networks in order to improve the quality of production management have led to an increase in the risks of successful cyberattacks and, as a result, to the necessity to solve the problems of practical information security of industrial control systems (ICS). Detection of cyberattacks of both known and unknown types is could be implemented as anomaly detection in dynamic information processes recorded during the operation of ICS. Anomaly detection methods do not require preliminary analysis and labeling of the training sample. In the context of detecting attacks on ICS, cluster analysis is used as one of the methods that implement anomaly detection. The application of hierarchical cluster analysis for clustering data of ICS information processes exposed to various cyberattacks is studied, the problem of choosing the level of the cluster hierarchy corresponding to the minimum set of clusters aggregating separately normal and abnormal data is solved. It is shown that the Ward method of hierarchical cluster division produces the best division into clusters. The next stage of the study involves solving the problem of classifying the formed minimum set of clusters, that is, determining which cluster is normal and which cluster is abnormal.

Micro grid is a small-scale power supply network designed to provide electricity to small community with integrated renewable energy sources. A micro grid can be integrated to the utility grid. Due to lack of computerized analysis, mechanical switches causing slow response time, poor visibility and situational awareness blackouts are caused due to cascading of faults. This paper presents a brief survey on communication technologies used in smart grid and its extension to micro grid. By integration of communication network, device control, information collection and remote management an intelligent power management system can be achieved

Wireless security and privacy is gaining a significant interest due to the burgeoning growth of communication devices across the electromagnetic spectrum. In this article, we introduce the concept of the space-time modulated millimeter-wave wireless links enabling physical layer security in highspeed communication links. Such an approach does not require cryptographic key exchanges and enables security in a seamless fashion with no overhead on latency. We show both the design and implementation of such a secure system using custom integrated chips at 71-76 GHz with off-chip packaged antenna array. We also demonstrate the security metric of such a system and analyze the efficacy through distributed eavesdropper attack.

The expanding streaming culture of large amounts of data, as well as the requirement for faster and more reliable data transport systems, necessitates the development of innovative communication technologies such as Visible Light Communication (VLC). Nonetheless, incorporating VLC into next-generation networks is challenging due to technological restrictions such as air absorption, shadowing, and beam dispersion. One technique for addressing some of the challenges is to use the multiple input multiple output (MIMO) technique, which involves the simultaneous transmission of data from several sources, hence increasing data rate. In this work, the data transmission performance of the MIMO-VLC system is evaluated using a variety of factors such as distance from the source, data bit rate, and modulation method.

Propagation delay in blockchain networks is a major impairment of message transmission and validation in the bitcoin network. The transaction delay caused by message propagation across long network chains can cause significant threats to the bitcoin network integrity by allowing miners to find blocks during the message consensus process. Potential threats of slow transaction dissemination include double-spending, partitions, and eclipse attacks. In this paper, we propose a method for minimizing propagation delay by reducing non-compulsory message broadcasts during transaction dissemination in the underlying blockchain network. Our method will decrease the propagation delay in the bitcoin network and consequently mitigate the security threats based on message dissemination delay. Our results show improvement in the delay time with more effect on networks with a large number of nodes.

The use of rare-earth elements in permanent magnets rises economic, environmental and supply-chain related concerns. Instead, ferrite magnets have been researched as an alternative. The magnetic flux concentration capacity of the Spoke Type Permanent Magnet Synchronous Motor (PMSM) and the low magnetic remanence of the ferrite magnet make them complementary strategies towards the desirable performance. However, if restricted to conventional manufacturing processes and materials, the mechanical design is a challenging step of the development of these machines. This paper explores how mechanical constraints impact electromagnetic performance. To access the interdependency of the performance and the mechanical constraints, finite element analyses are done both in the mechanical and electromagnetic domain. The results show that the mechanical constraints have an impact on the performance, although it is possible to reduce it by adapting the design to the electromagnetic and mechanical properties of the electrical steel.

We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. AdaMix reduces the error increase from the non-private upper bound from the 167–311% of the baseline, on average across 6 datasets, to 68-92% depending on the desired privacy level selected by the user. AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis.

State of the art Artificial Intelligence Assurance (AIA) methods validate AI systems based on predefined goals and standards, are applied within a given domain, and are designed for a specific AI algorithm. Existing works do not provide information on assuring subjective AI goals such as fairness and trustworthiness. Other assurance goals are frequently required in an intelligent deployment, including explainability, safety, and security. Accordingly, issues such as value loading, generalization, context, and scalability arise; however, achieving multiple assurance goals without major trade-offs is generally deemed an unattainable task. In this manuscript, we present two AIA pipelines that are model-agnostic, independent of the domain (such as: healthcare, energy, banking), and provide scores for AIA goals including explainability, safety, and security. The two pipelines: Adversarial Logging Scoring Pipeline (ALSP) and Requirements Feedback Scoring Pipeline (RFSP) are scalable and tested with multiple use cases, such as a water distribution network and a telecommunications network, to illustrate their benefits. ALSP optimizes models using a game theory approach and it also logs and scores the actions of an AI model to detect adversarial inputs, and assures the datasets used for training. RFSP identifies the best hyper-parameters using a Bayesian approach and provides assurance scores for subjective goals such as ethical AI using user inputs and statistical assurance measures. Each pipeline has three algorithms that enforce the final assurance scores and other outcomes. Unlike ALSP (which is a parallel process), RFSP is user-driven and its actions are sequential. Data are collected for experimentation; the results of both pipelines are presented and contrasted.

In this study, the parameters of the Jiles-Atherton (JA) model are investigated to determine suitable solution candidates for hysteresis models of a piezoelectric actuator (PEA). The methodology of this study is to perform Monte Carlo experiments on the JA model by randomly selecting parameters that generate hysteresis curves. The solution space is then restrained such that their normalized area and remnant displacements are comparable to those of the PEA. The data resulting from these Monte Carlo simulations show trends in the parameter space that can be used to further restrain parameter selection windows to find suitable JA parameters to model PEAs. In particular, the results show that selection of the reversibility coefficient and the pinning factor strongly affect both of the hysteresis characteristics studied. A large density of solutions is found in certain parameter distributions for both the area and the remnant displacement, but the remnant displacement generates the densest distributions. These results can be used to more effectively find suitable hysteresis models for modeling purposes.

The development of cloud apps enables people to exchange resources, goods, and expertise online with other clients. The material is more vulnerable to numerous security dangers from outsiders due to the fact that millions of users exchange data through the same system. How to maintain the security of this data is now the main concern. The current data protection system functions best when it places a greater priority on safeguarding data maintained in online storage than it does on cybersecurity during transportation. The data becomes open to intrusion attacks while being transferred. Additionally, the present craze states that an outside auditor may view data as it is being transmitted. Additionally, by allowing the hacker to assume a third-person identity while obtaining the information, this makes the data more susceptible to exploitation. The proposed system focuses on using encryption to safeguard information flow since cybersecurity is seen as a major issue. The approach also takes into account the fourth auditing issue, which is that under the recommended manner, the inspector is not allowed to see the user information. Tests have shown that the recommended technique improves security overall by making it harder for hackers to decode the supplied data.

As cyber threats become highly damaging and complex, a new cybersecurity compliance certification model has been developed by the Department of Defense (DoD) to secure its Defense Industrial Base (DIB), and communication with its private partners. These partners or contractors are obligated by the Defense Federal Acquisition Regulations (DFARS) to be compliant with the latest standards in computer and data security. The Cybersecurity Maturity Model Certification (CMMC), and it is built upon existing DFARS 252.204-7012 and the NIST SP 800–171 controls. As of 2020, the DoD has incorporated DFARS and the National Institute of Standards and Technology (NIST) recommended security practices into what is now the CMMC. This paper presents the most commonly identified Security-Control-Deficiencies (SCD) faced, the attacks mitigated by addressing these SCD, and remediations applied to 127 DoD contractors in order to bring them into compliance with the CMMC guidelines. An analysis is done on what vulnerabilities are most prominent in the companies, and remediations applied to ensure these vulnerabilities are better avoided and the DoD supply-chain is more secure from attacks.

A multi-designated receiver authentication code (MDRA-code) with information-theoretic security is proposed as an extension of the traditional multi-receiver authentication code. The purpose of the MDRA-code is to securely transmit a message via a broadcast channel from a single sender to an arbitrary subset of multiple receivers that have been designated by the sender, and only the receivers in the subset (i.e., not all receivers) should accept the message if an adversary is absent. This paper proposes a model and security formalization of MDRA-codes, and provides constructions of MDRA-codes.

With the recent advancements in automated communication technology, many traditional businesses that rely on face-to-face communication have shifted to online portals. However, these online platforms often lack the personal touch essential for customer service. Research has shown that face-to- face communication is essential for building trust and empathy with customers. A multimodal embodied conversation agent (ECA) can fill this void in commercial applications. Such a platform provides tools to understand the user’s mental state by analyzing their verbal and non-verbal behaviour and allows a human-like avatar to take necessary action based on the context of the conversation and as per social norms. However, the literature to understand the impact of ECA agents on commercial applications is limited because of the issues related to platform and scalability. In our work, we discuss some existing work that tries to solve the issues related to scalability and infrastructure. We also provide an overview of the components required for developing ECAs and their deployment in various applications.

Multi robot systems are defined as a collection of two or more robots that are capable of working autonomously while coordinating with each other. Three challenges emerge while designing any multi robot system. The robots have to coordinate their path planning or trajectory planning in order to avoid collision during the course of navigation, while collaborating tasks with other robots to achieve a specific end goal for the system. The other challenge, which is the focus of this paper, is the security of the entire multi robot system. Since robots have to coordinate with each other, any one of them being malicious due to any kind of security threat, can lead to a chain reaction that may compromise the entire system. Such security threats can be fatal if not dealt with immediately. This paper proposes the use of a Hybridized Blockchain Model (HBM) to identify such security threats and take necessary actions in real time so that the system does not encounter any catastrophic failure. The proposed security architecture uses ROS (Robot operating system) to decentralize the information collected by robot clients and HBM to monitor the clients and take necessary real time actions.

This paper is a conceptual paper that explores how the sensemaking process by intelligence analysts completed within a cognitive immersive environment might be impacted by the inclusion of a progressive dialog system. The tools enabled in the sensemaking room (a specific instance within the cognitive immersive environment) were informed by tools from the intelligence analysis domain. We explore how a progressive dialog system would impact the use of tools such as the collaborative brainstorming exercise [1]. These structured analytic techniques are well established in intelligence analysis training literature, and act as ways to access the intended users' cognitive schema as they use the cognitive immersive room and move through the sensemaking process. A prior user study determined that the sensemaking room encouraged users to be more concise and representative with information while using the digital brainstorming tool. We anticipate that addition of the progressive dialog function will enable a more cohesive link between information foraging and sensemaking behaviors for analysts.

In this study, the effect of surface treatment on the boding strength between Quad flat package (QFP) and quartz was investigated for establishing a QFP/quartz glass bonding technique. This bonding technique is necessary to prevent bond failure at the nano-artifact metrics (NAM) chip and adhesive interface against physical attacks such as counterfeiting and tampering of edge AI devices that use NAM chips. Therefore, we investigated the relationship between surface roughness and tensile strength by applying surface treatments such as vacuum ultraviolet (VUV) and Ar/O2 plasma. All QFP/quartz glass with surface treatments such as VUV and Ar/O2 plasma showed increased bond strength. Surface treatment and bonding technology for QFP and quartz glass were established to realize NAM chip mounting.

With the proliferation of malware, the detection and classification of malware have been hot topics in the academic and industrial circles of cyber security, and the generation of malware signatures is one of the important research directions. In this paper, we propose NBP-MS, a method of signature generation that is based on network traffic generated by malware. Specifically, we utilize the network traffic generated by malware to perform fine-grained profiling of its network behaviors first, and then cluster all the profiles to generate network behavior signatures to classify malware, providing support for subsequent analysis and defense.

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.

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.

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.

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.

Higher education management has problems producing 100% of graduates capable of responding to the needs of industry while industry also is struggling to find qualified graduates that responded to their needs in part because of the inefficient way of evaluating problems, as well as because of weaknesses in the evaluation of problem-solving capabilities. The objective of this paper is to propose an appropriate classification model to be used for predicting and evaluating the attributes of the data set of the student in order to meet the selection criteria required by the industries in the academic field. The dataset required for this analysis was obtained from a private firm and the execution was carried out using Chimp Optimization Algorithm (COA) based Deep Belief Neural Network (COA-DBNN) and the obtained results are compared with various classifiers such as Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The proposed model outperforms other classifiers in terms of various performance metrics. This critical analysis will help the college management to make a better long-term plan for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

We demonstrated hardware emulations to evaluate the error-correction performance for a FEC scheme with multilevel coding. It has enabled the measurement of BER to reach the order of 10−14 for the decoded signal.