Biblio

To ensure dynamic and transient angle and load stability in order to maintain the power system security is a major task of the power Engineer. FACTS Controllers are most effective devices to ensure system security by enhancing the stability margins with reactive power support all over the power system network. The major shunt compensation devices of FACTS are SVC and STATCOM. This article dispenses the modelling and simulation of both the shunt devices viz. Oneis the Static Synchronous Compensator (STATCOM) and the other is Static Var Compensator (SVC). The small signal models of these devices have been derived from the first principles and obtained the transfer function models of weak and strong power systems. The weak power system has the Short Circuit Ratio (SCR) is about less than 3 and that of the strong power system has the SCR of more than 5. The performance of the both weak and strong power systems has been evaluated with time and frequency responses. The dynamic response is obtained with the exact models for both weak and strong systems, subsequently the root locus plots as well as bode plots have been obtained with MATLAB Programs and evaluated the performance of these devices and comparison is made. The Stability margins of both the systems with SVC and STATCOM have been obtained from the bode plots. The dynamic behaviour of the both kinds of power systems have been assessed with time responses of SVC and STATCOM models. All of these results viz. dynamic response, root locus and bode plots proves the superiority of the STATCOM over SVC with indices, viz. peak overshoot, settling time, gain margin and phase margins. The dynamic, steady state performance indices obtained from time response and bode plots proves the superior performance of STATCOM.

Being a part of today’s technical world, we are connected through a vast network. More we are addicted to these modernization techniques we need security. There must be reliability in a network security system so that it is capable of doing perfect monitoring of the whole network of an organization so that any unauthorized users or intruders wouldn’t be able to halt our security breaches. Firewalls are there for securing our internal network from unauthorized outsiders but still some time possibility of attacks is there as according to a survey 60% of attacks were internal to the network. So, the internal system needs the same higher level of security just like external. So, understanding the value of security measures with accuracy, efficiency, and speed we got to focus on implementing and comparing an improved intrusion detection system. A comprehensive literature review has been done and found that some feature selection techniques with standard scaling combined with Machine Learning Techniques can give better results over normal existing ML Techniques. In this survey paper with the help of the Uni-variate Feature selection method, the selection of 14 essential features out of 41 is performed which are used in comparative analysis. We implemented and compared both binary class classification and multi-class classification-based Intrusion Detection Systems (IDS) for two Supervised Machine Learning Techniques Support Vector Machine and Classification and Regression Techniques.

The Internet of Things is a developing technology that converts physical objects into virtual objects connected to the internet using wired and wireless network architecture. Use of cross-layer techniques in the internet of things is primarily driven by the high heterogeneity of hardware and software capabilities. Although traditional layered architecture has been effective for a while, cross-layer protocols have the potential to greatly improve a number of wireless network characteristics, including bandwidth and energy usage. Also, one of the main concerns with the internet of things is security, and machine learning (ML) techniques are thought to be the most cuttingedge and viable approach. This has led to a plethora of new research directions for tackling IoT's growing security issues. In the proposed study, a number of cross-layer approaches based on machine learning techniques that have been offered in the past to address issues and challenges brought on by the variety of IoT are in-depth examined. Additionally, the main issues are mentioned and analyzed, including those related to scalability, interoperability, security, privacy, mobility, and energy utilization.

A weather radar is expected to provide information about weather conditions in real time and valid. To obtain these results, weather radar takes a lot of data samples, so a large amount of data is obtained. Therefore, the weather radar equipment must provide bandwidth for a large capacity for transmission and storage media. To reduce the burden of data volume by performing compression techniques at the time of data acquisition. Compressive Sampling (CS) is a new data acquisition method that allows the sampling and compression processes to be carried out simultaneously to speed up computing time, reduce bandwidth when passed on transmission media, and save storage media. There are three stages in the CS method, namely: sparsity transformation using the Discrete Cosine Transform (DCT) algorithm, sampling using a measurement matrix, and reconstruction using the Orthogonal Matching Pursuit (OMP) algorithm. The sparsity transformation aims to convert the representation of the radar signal into a sparse form. Sampling is used to extract important information from the radar signal, and reconstruction is used to get the radar signal back. The data used in this study is the real data of the IDRA beat signal. Based on the CS simulation that has been done, the best PSNR and RMSE values are obtained when using a CR value of two times, while the shortest computation time is obtained when using a CR value of 32 times. CS simulation in a sector via DCT using the CR value two times produces a PSNR value of 20.838 dB and an RMSE value of 0.091. CS simulation in a sector via DCT using the CR value 32 times requires a computation time of 10.574 seconds.

Communication systems across a variety of applications are increasingly using the angular domain to improve spectrum management. They require new sensing architectures to perform energy-efficient measurements of the electromagnetic environment that can be deployed in a variety of use cases. This paper presents the Directional Spectrum Sensor (DSS), a compressive sampling (CS) based analog-to-information converter (CS-AIC) that performs spectrum scanning in a focused beam. The DSS offers increased spectrum sensing sensitivity and interferer tolerance compared to omnidirectional sensors. The DSS implementation uses a multi-antenna beamforming architecture with local oscillators that are modulated with pseudo random waveforms to obtain CS measurements. The overall operation, limitations, and the influence of wideband angular effects on the spectrum scanning performance are discussed. Measurements on an experimental prototype are presented and highlight improvements over single antenna, omnidirectional sensing systems.

Due to the rapid development of cyber-physical systems, there are more and more security problems. The purpose of this work is to develop the concept of a knowledge base in the field of security of cyber-physical systems based on an ontological approach. To create the concept of a knowledge base, it was necessary to consider the system of a cyber-physical system and highlight its structural parts. As a result, the main concepts of the security of a cyber-physical system were identified and the concept of a knowledge base was drawn up, which in the future will help to analyze potential threats to cyber-physical systems.

Cyber-attacks against Industrial Control Systems (ICS) can lead to catastrophic events which can be prevented by the use of security measures such as the Intrusion Prevention Systems (IPS). In this work we experimentally demonstrate how to exploit the configuration vulnerabilities of SNORT one of the most adopted IPSs to significantly degrade the effectiveness of the IPS and consequently allowing successful cyber-attacks. We illustrate how to design a batch script able to retrieve and modify the configuration files of SNORT in order to disable its ability to detect and block Denial of Service (DoS) and ARP poisoning-based Man-In-The-Middle (MITM) attacks against a Programmable Logic Controller (PLC) in an ICS network. Experimental tests performed on a water distribution testbed show that, despite the presence of IPS, the DoS and ARP spoofed packets reach the destination causing respectively the disconnection of the PLC from the ICS network and the modification of packets payload.

Frame detection is an important part of the reconnaissance satellite receiver to identify the terrestrial application specific messages (ASM) / VHF data exchange (VDE) signal, and has been challenged by Doppler shift and message collision. A constant false alarm rate (CFAR) frame detection strategy insensitive to Doppler shift has been proposed in this paper. Based on the double Barker sequence, a periodical sequence has been constructed, and differential operations have been adopted to eliminate the Doppler shift. Moreover, amplitude normalization is helpful for suppressing the interference introduced by message collision. Simulations prove that the proposed CFAR frame detection strategy is very attractive for the reconnaissance satellite to identify the terrestrial ASM/VDE signal.

Metaverse is becoming the new standard for social networks and 3D virtual worlds when Facebook officially rebranded to Metaverse in October 2021. Many relevant technologies are used in the metaverse to offer 3D immersive and customized experiences at the user’s fingertips. Despite the fact that the metaverse receives a lot of attention and advantages, one of the most pressing concerns for its users is the safety of their digital material and data. As a result of its decentralization, immutability, and transparency, blockchain is a possible alternative. Our goal is to conduct a comprehensive assessment of blockchain systems in the metaverse to properly appreciate its function in the metaverse. To begin with, the paper introduces blockchain and the metaverse and explains why it’s necessary for the metaverse to adopt blockchain technology. Aside from these technological considerations, this article focuses on how blockchain-based approaches for the metaverse may be used from a privacy and security standpoint. There are several technological challenegs that need to be addressed for making the metaverse a reality. The influence of blockchain on important key technologies with in metaverse, such as Artifical Intelligence, big data and the Internet-of-Things (IoT) is also examined. Several prominent initiatives are also shown to demonstrate the importance of blockchain technology in the development of metaverse apps and services. There are many possible possibilities for future development and research in the application of blockchain technology in the metaverse.

This paper proposes a control flow integrity checking method based on the LBR register: through an analysis of the static target program loaded binary modules, gain function attributes such as borders and build the initial transfer of legal control flow boundary, real-time maintenance when combined with the dynamic execution of the program flow of control transfer record, build a complete profile control flow transfer security; Get the call location of /bin/sh or system() in the program to build an internal monitor for control-flow integrity checks. In the process of program execution, on the one hand, the control flow transfer outside the outline is judged as the abnormal control flow transfer with attack threat; On the other hand, abnormal transitions across the contour are picked up by an internal detector. In this method, by identifying abnormal control flow transitions, attacks are initially detected before the attack code is executed, while some attacks that bypass the coarse-grained verification of security profile are captured by the refined internal detector of control flow integrity. This method reduces the cost of control flow integrity check by using the safety profile analysis of coarse-grained check. In addition, a fine-grained shell internal detector is inserted into the contour to improve the safety performance of the system and achieve a good balance between performance and efficiency.

In Production System Engineering (PSE), domain experts from different disciplines reuse assets such as products, production processes, and resources. Therefore, PSE organizations aim at establishing reuse across engineering disciplines. However, the coordination of multi-disciplinary reuse tasks, e.g., the re-validation of related assets after changes, is hampered by the coarse-grained representation of tasks and by scattered, heterogeneous domain knowledge. This paper introduces the Multi-disciplinary Reuse Coordination (MRC) artifact to improve task management for multi-disciplinary reuse. For assets and their properties, the MRC artifact describes sub-tasks with progress and result states to provide references for detailed reuse task management across engineering disciplines. In a feasibility study on a typical robot cell in automotive manufacturing, we investigate the effectiveness of task management with the MRC artifact compared to traditional approaches. Results indicate that the MRC artifact is feasible and provides effective capabilities for coordinating multi-disciplinary re-validation after changes.

In this decade, digital transactions have risen exponentially demanding more reliable and secure authentication systems. CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) system plays a major role in these systems. These CAPTCHAs are available in character sequence, picture-based, and audio-based formats. It is very essential that these CAPTCHAs should be able to differentiate a computer program from a human precisely. This work tests the strength of text-based CAPTCHAs by breaking them using an algorithm built on CNN (Convolution Neural Network) and RNN (Recurrent Neural Network). The algorithm is designed in such a way as an attempt to break the security features designers have included in the CAPTCHAs to make them hard to be cracked by machines. This algorithm is tested against the synthetic dataset generated in accordance with the schemes used in popular websites. The experiment results exhibit that the model has shown a considerable performance against both the synthetic and real-world CAPTCHAs.

In this paper, we proposed a data security model of a big data analytical environment in the financial sector. Big Data can be seen as a trend in the advancement of technology that has opened the door to a new approach to understanding and decision making that is used to describe the vast amount of data (structured, unstructured and semi-structured) that is too time consuming and costly to load a relational database for analysis. The increase in cybercriminal attacks on an organization’s assets results in organizations beginning to invest in and care more about their cybersecurity points and controls. The management of business-critical data is an important point for which robust cybersecurity controls should be considered. The proposed model is applied in a datalake and allows the identification of security gaps on an analytical repository, a cybersecurity risk analysis, design of security components and an assessment of inherent risks on high criticality data in a repository of a regulated financial institution. The proposal was validated in financial entities in Lima, Peru. Proofs of concept of the model were carried out to measure the level of maturity focused on: leadership and commitment, risk management, protection control, event detection and risk management. Preliminary results allowed placing the entities in level 3 of the model, knowing their greatest weaknesses, strengths and how these can affect the fulfillment of business objectives.

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.

With the intelligent development of power system, due to the double-layer structure of smart grid and the characteristics of failure propagation across layers, the attack path also changes significantly: from single-layer to multi-layer and from static to dynamic. In response to the shortcomings of the single-layer attack path of traditional attack path identification methods, this paper proposes the idea of cross-layer attack, which integrates the threat propagation mechanism of the information layer and the failure propagation mechanism of the physical layer to establish a forward-backward bi-directional detection model. The model is mainly used to predict possible cross-layer attack paths and evaluate their path generation probabilities to provide theoretical guidance and technical support for defenders. The experimental results show that the method proposed in this paper can well identify the dynamic cross-layer attacks in the smart grid.

Unmanned Aerial Vehicle (UAV)-based streaming media transmission may become unstable when the bit rate generated by the source load exceeds the channel capacity owing to the UAV location and speed change. The change of the location can affect the network connection, leading to reduced transmission rate; the change of the flying speed can increase the video payload due to more I-frames. To improve the transmission reliability, in this paper we design a Client-Server-Ground&User (C-S-G&U) framework, and propose an algorithm of splitting-merging stream (SMS) for multi-link concurrent transmission. We also establish multiple transport links and configure the routing rules for the cross-layer design. The multi-link transmission can achieve higher throughput and significantly smaller end-to-end delay than a single-link especially in a heavy load situation. The audio and video data are packaged into the payload by the Real-time Transport Protocol (RTP) before being transmitted over the User Datagram Protocol (UDP). The forward error correction (FEC) algorithm is implemented to promote the reliability of the UDP transmission, and an encryption algorithm to enhance security. In addition, we propose a Quality of Service (QoS) strategy so that the server and the user can control the UAV to adapt its transmission mode dynamically, according to the load, delay, and packet loss. Our design has been implemented on an engineering platform, whose efficacy has been verified through comprehensive experiments.

Security of Internet of Things (IoT) is one of the most prevalent crucial challenges ever since. The diversified devices and their specification along with resource constrained protocols made it more complex to address over all security need of IoT. Denial of Service attacks, being the most powerful and frequent attacks on IoT have been considered so forth. However, the attack happens on multiple layers and thus a single detection technique for each layer is not sufficient and effective to combat these attacks. Current study focuses on cross layer intrusion detection system (IDS) for detection of multiple Denial of Service (DoS) attacks. Presently, two attacks at Transmission Control Protocol (TCP) and Routing Protocol are considered for Low power and Lossy Networks (RPL) and a neural network-based IDS approach has been proposed for the detection of such attacks. The attacks are simulated on NetSim and detection and the performance shows up to 80% detection probabilities.

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning-based processing is a promising solution for detecting cyber-threats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

To prevent all sorts of attacks, the technology of security service function chains (SFC) is proposed in recent years, it becomes an attractive research highlights. Dynamic orchestration algorithm can create SFC according to the resource usage of network security functions. The current research on creating SFC focuses on a single domain. However in reality the large and complex networks are divided into security domains according to different security levels and managed separately. Therefore, we propose a cross-security domain dynamic orchestration algorithm to create SFC for network security functions based on ant colony algorithm(ACO) and consider load balancing, shortest path and minimum delay as optimization objectives. We establish a network security architecture based on the proposed algorithm, which is suitable for the industrial vertical scenarios, solves the deployment problem of the dynamic orchestration algorithm. Simulation results verify that our algorithm achieves the goal of creating SFC across security domains and demonstrate its performance in creating service function chains to resolve abnormal traffic flows.

Side-channel attacks have been a constant threat to computing systems. In recent times, vulnerabilities in the architecture were discovered and exploited to mount and execute a state-of-the-art attack such as Spectre. The Spectre attack exploits a vulnerability in the Intel-based processors to leak confidential data through the covert channel. There exist some defenses to mitigate the Spectre attack. Among multiple defenses, hardware-assisted attack/intrusion detection (HID) systems have received overwhelming response due to its low overhead and efficient attack detection. The HID systems deploy machine learning (ML) classifiers to perform anomaly detection to determine whether the system is under attack. For this purpose, a performance monitoring tool profiles the applications to record hardware performance counters (HPC), utilized for anomaly detection. Previous HID systems assume that the Spectre is executed as a standalone application. In contrast, we propose an attack that dynamically generates variations in the injected code to evade detection. The attack is injected into a benign application. In this manner, the attack conceals itself as a benign application and gen-erates perturbations to avoid detection. For the attack injection, we exploit a return-oriented programming (ROP)-based code-injection technique that reuses the code, called gadgets, present in the exploited victim's (host) memory to execute the attack, which, in our case, is the CR-Spectre attack to steal sensitive data from a target victim (target) application. Our work focuses on proposing a dynamic attack that can evade HID detection by injecting perturbations, and its dynamically generated variations thereof, under the cloak of a benign application. We evaluate the proposed attack on the MiBench suite as the host. From our experiments, the HID performance degrades from 90% to 16%, indicating our Spectre-CR attack avoids detection successfully.

We propose a methodology for the simulation of electrostatic confinement wells in transistors at cryogenic temperatures. This is considered in the context of 22-nm fully depleted silicon-on-insulator transistors due to their potential for imple-menting quantum bits in scalable quantum computing systems. To overcome thermal fluctuations and improve decoherence times in most quantum bit implementations, they must be operated at cryogenic temperatures. We review the dominant sources of electric field at these low temperatures, including material interface work function differences and trapped interface charges. Intrinsic generation and dopant ionisation are shown to be negligible at cryogenic temperatures when using a mode of operation suitable for confinement. We propose studying cryogenic electrostatic confinement wells in transistors using a finite-element model simulation, and decoupling carrier transport generated fields.

While 5G Edge Computing along with IoT technology has transformed the future of healthcare data transmission, it presents security vulnerabilities and risks when transmitting patients' confidential information. Currently, there are very few reliable security solutions available for healthcare data that routes through SDN routers in 5G Edge Computing. These solutions do not provide cryptographic security from IoT sensor devices. In this paper, we studied how 5G edge computing integrated with IoT network helps healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, and finally proposed a cryptographic end-to-end security solution initiated at IoT sensor devices and routed through SDN routers. Our proposed solution with cryptographic security initiated at IoT sensor goes through SDN control plane and data plane in 5G edge computing and provides an end-to-end secured communication from IoT device to doctor's office. A prototype built with two-layer encrypted communication has been lab tested with promising results. This analysis will help future security implementation for eHealth in 5G and beyond networks.

There is a stark contrast between the state of cyber security of national infrastructure in Ireland and the efforts underway to support cyber security technologists to work in the country. Notable attacks have recently occurred against the national health service, universities, and various other state bodies, prompting an interest in changing the current situation. This paper presents an overview of the security projects, commercial establishments, and policy in Ireland.

Still in many countries COVID19 virus is changing its structure and creating damages in terms of economy and education. In India during the period of January 2022 third wave is on its high peak. Many colleges and schools are still forced to teach online. This paper describes how cyber security actionable or practical fundamental were taught by school or college teachers. Various cyber security tools are used to explain the actionable insight of the subject. Main Topics or concepts covered are MITM (Man In the Middle Attack) using ethercap tool in Kali Linux, spoofing methods like ARP (Address Resolution Protocol) spoofing and DNS (Domain Name System) spoofing, network intrusion detection using snort , finding information about packets using wireshark tool and other tools like nmap and netcat for finding the vulnerability. Even brief details were given about how to crack password using wireshark.

Artificial intelligence (AI) and machine learning (ML) have been used in transforming our environment and the way people think, behave, and make decisions during the last few decades [1]. In the last two decades everyone connected to the Internet either an enterprise or individuals has become concerned about the security of his/their computational resources. Cybersecurity is responsible for protecting hardware and software resources from cyber attacks e.g. viruses, malware, intrusion, eavesdropping. Cyber attacks either come from black hackers or cyber warfare units. Artificial intelligence (AI) and machine learning (ML) have played an important role in developing efficient cyber security tools. This paper presents Latest Cyber Security Tools Based on Machine Learning which are: Windows defender ATP, DarckTrace, Cisco Network Analytic, IBM QRader, StringSifter, Sophos intercept X, SIME, NPL, and Symantec Targeted Attack Analytic.