Biblio

Large volumes of private data are gathered, processed, and stored on computers by governments, the military, organizations, financial institutions, colleges, and other enterprises. This data is then sent through networks to other computers. Urgent measures are required to safeguard sensitive personal and company data as well as national security due to the exponential development in number and complexity of cyber- attacks. The essay discusses the characteristics of the Internet and demonstrates how private and financial data can be transmitted over it while still being safeguarded. We show that robbery has spread throughout India and the rest of the world, endangering the global economy and security and giving rise to a variety of cyber-attacks.

In the prevailing situation, the sports like economic, industrial, cultural, social, and governmental activities are carried out in the online world. Today's international is particularly dependent on the wireless era and protective these statistics from cyber-assaults is a hard hassle. The reason for cyber-assaults is to damage thieve the credentials. In a few other cases, cyber-attacks ought to have a navy or political functions. The damages are PC viruses, facts break, DDS, and exceptional attack vectors. To this surrender, various companies use diverse answers to prevent harm because of cyberattacks. Cyber safety follows actual-time data at the modern-day-day IT data. So, far, numerous techniques have proposed with the resource of researchers around the area to prevent cyber-attacks or lessen the harm due to them. The cause of this has a look at is to survey and comprehensively evaluate the usual advances supplied around cyber safety and to analyse the traumatic situations, weaknesses, and strengths of the proposed techniques. Different sorts of attacks are taken into consideration in element. In addition, evaluation of various cyber-attacks had been finished through the platform called Kali Linux. It is predicted that the complete assessment has a have a study furnished for college students, teachers, IT, and cyber safety researchers might be beneficial.

The digital transformation brought on by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles necessary to enable 5G to achieve its promise. 5G trustworthiness highlights the importance of embedding security capabilities from the very beginning while the 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the different layers of 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture within a risk-management framework that takes into account the evolving security-threats landscape. 5G presents a typical use-case of wireless communication and computer networking convergence, where 5G fundamental building blocks include components such as Software Defined Networks (SDN), Network Functions Virtualization (NFV) and the edge cloud. This convergence extends many of the security challenges and opportunities applicable to SDN/NFV and cloud to 5G networks. Thus, 5G security needs to consider additional security requirements (compared to previous generations) such as SDN controller security, hypervisor security, orchestrator security, cloud security, edge security, etc. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability. The working group scope fundamentally addresses the following: •5G security considerations need to overlay and permeate through the different layers of the 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture including a risk management framework that takes into account the evolving security threats landscape. •5G exemplifies a use-case of heterogeneous access and computer networking convergence, which extends a unique set of security challenges and opportunities (e.g., related to SDN/NFV and edge cloud, etc.) to 5G networks. Similarly, 5G networks by design offer potential security benefits and opportunities through harnessing the architecture flexibility, programmability and complexity to improve its resilience and reliability. •The IEEE FNI security WG's roadmap framework follows a taxonomic structure, differentiating the 5G functional pillars and corresponding cybersecurity risks. As part of cross collaboration, the security working group will also look into the security issues associated with other roadmap working groups within the IEEE Future Network Initiative.

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim’s files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges.

Successful information and communication technology (ICT) may propel administrative procedures forward quickly. In order to achieve efficient usage of TCT in their businesses, ICT strategies and plans should be examined to ensure that they align with the organization's visions and missions. Efficient software and hardware work together to provide relevant data that aids in the improvement of how we do business, learn, communicate, entertain, and work. This exposes them to a risky environment that is prone to both internal and outside threats. The term “security” refers to a level of protection or resistance to damage. Security can also be thought of as a barrier between assets and threats. Important terms must be understood in order to have a comprehensive understanding of security. This research paper discusses key terms, concerns, and challenges related to information systems and security auditing. Exploratory research is utilised in this study to find an explanation for the observed occurrences, problems, or behaviour. The study's findings include a list of various security risks that must be seriously addressed in any Information System and Security Audit.

The risk of detecting incidents in the field of computer technology in Maritime transport is considered. The structure of the computer incident investigation system and its functions are given. The system of conducting investigations of computer incidents on sea transport is considered. A possible algorithm for investigating the incident using the tools of forensic science and an algorithm for transmitting the received data for further processing are presented.

This note addresses the problem of distributed control for a class of nonlinear multi-agent systems over a communication graph. In many real practical systems, owing to communication limits and the vulnerability of communication networks to be overheard and modified by the adversary, consideration of communication delays and cyber-attacks in designing of the controller is important. To consider these challenges, in the presented approach, a distributed controller for a group of one-link flexible joint manipulators is provided which are connected via data delaying communication network in the presence of cyber-attacks. Sufficient conditions are provided to guarantee that the closed-loop system is stable with prescribed disturbance attenuation, and the parameter of the control law can be obtained by solving a set of linear matrix inequities (LMIs). Eventually, simulations results of four single-link manipulators are provided to demonstrate the performance of the introduced method.

Cyber-Physical Systems (CPS) suffer from extendable vulnerabilities due to the convergence of the physical world with the cyber world, which makes it victim to a number of sophisticated cyber-attacks. The motives behind such attacks range from criminal enterprises to military, economic, espionage, political, and terrorism-related activities. Many governments are more concerned than ever with securing their critical infrastructure. One of the effective means of detecting threats and securing their infrastructure is the use of Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). A number of studies have been conducted and proposed to assess the efficacy and effectiveness of IDS through the use of self-learning techniques, especially in the Industrial Control Systems (ICS) era. This paper investigates and analyzes the utilization of IDS systems and their proposed solutions used to enhance the effectiveness of such systems for CPS. The targeted data extraction was from 2011 to 2021 from five selected sources: IEEE, ACM, Springer, Wiley, and ScienceDirect. After applying the inclusion and exclusion criteria, 20 primary studies were selected from a total of 51 studies in the field of threat detection in CPS, ICS, SCADA systems, and the IoT. The outcome revealed the trends in recent research in this area and identified essential techniques to improve detection performance, accuracy, reliability, and robustness. In addition, this study also identified the most vulnerable target layer for cyber-attacks in CPS. Various challenges, opportunities, and solutions were identified. The findings can help scholars in the field learn about how machine learning (ML) methods are used in intrusion detection systems. As a future direction, more research should explore the benefits of ML to safeguard cyber-physical systems.

Today's companies are increasingly relying on Internet of Everything (IoE) to modernize their operations. The very complexes characteristics of such system expose their applications and their exchanged data to multiples risks and security breaches that make them targets for cyber attacks. The aim of our work in this paper is to provide an cybersecurity strategy whose objective is to prevent and anticipate threats related to the IoE. An economic approach is used in order to help to take decisions according to the reduction of the risks generated by the non definition of the appropriate levels of security. The considered problem have been resolved by exploiting a combinatorial optimization approach with a practical case of knapsack. We opted for a bi-objective modeling under uncertainty with a constraint of cardinality and a given budget to be respected. To guarantee a robustness of our strategy, we have also considered the criterion of uncertainty by taking into account all the possible threats that can be generated by a cyber attacks over IoE. Our strategy have been implemented and simulated under MATLAB environement and its performance results have been compared to those obtained by NSGA-II metaheuristic. Our proposed cyber security strategy recorded a clear improvment of efficiency according to the optimization of the security level and cost parametrs.

Recent cyber attacks on the power grid have been of increasing complexity and sophistication. In order to understand the impact of cyber-attacks on the power system resiliency, it is important to consider an holistic cyber-physical system specially with increasing industrial automation. In this study, device-level resilience properties of the various controllers and their impact on the microgrid resiliency is studied. In addition, a cyber-physical resiliency metric considering vulnerabilities, system model, and device-level properties is proposed. Resiliency is defined as the system ability to provide energy to critical loads even in extreme contingencies and depends on system ability to withstand, predict, and recover. A use case is presented inspired by the recent Ukraine cyber-attack. A use case has been presented to demonstrate application of the developed cyber-physical resiliency metric to enhance situational awareness of the operator, and enable better proactive or remedial control actions to improve resiliency.

Recent cyber attacks on the power grid have been of increasing complexity and sophistication. In order to understand the impact of cyber-attacks on the power system resiliency, it is important to consider an holistic cyber-physical system specially with increasing industrial automation. In this work, device level resilience properties of the various controllers and their impact on the microgrid resiliency is studied. In addition, a cyber-physical resiliency metric considering vulnerabilities, system model, and device level properties is proposed. A use case is presented inspired by the recent Ukraine cyber-attack. A use case has been presented to demonstrate application of the developed cyber-physical resiliency metric to enhance situational awareness of the operator, and enable better control actions to improve resiliency.

Protecting Cyber-physical Systems (CPSs) is highly important for preserving sensitive information and detecting cyber threats. Developing a robust privacy-preserving anomaly detection method requires physical and network data about the systems, such as Supervisory Control and Data Acquisition (SCADA), for protecting original data and recognising cyber-attacks. In this paper, a new privacy-preserving anomaly detection framework, so-called PPAD-CPS, is proposed for protecting confidential information and discovering malicious observations in power systems and their network traffic. The framework involves two main modules. First, a data pre-processing module is suggested for filtering and transforming original data into a new format that achieves the target of privacy preservation. Second, an anomaly detection module is suggested using a Gaussian Mixture Model (GMM) and Kalman Filter (KF) for precisely estimating the posterior probabilities of legitimate and anomalous events. The performance of the PPAD-CPS framework is assessed using two public datasets, namely the Power System and UNSW-NB15 dataset. The experimental results show that the framework is more effective than four recent techniques for obtaining high privacy levels. Moreover, the framework outperforms seven peer anomaly detection techniques in terms of detection rate, false positive rate, and computational time.

The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis.

Cyber-attacks detection in modern power systems is undoubtedly indispensable to enhance their resilience and guarantee the continuous production of electricity. As the number of attacks is very small compared to normal events, and attacks are unpredictable, it is not obvious to build a model for attacks. Here, only anomaly-free measurements are utilized to build a reference model for intrusion detection. Specifically, this study presents an unsupervised intrusion detection approach using the k-nearest neighbor algorithm and exponential smoothing monitoring scheme for uncovering attacks in modern power systems. Essentially, the k-nearest neighbor algorithm is implemented to compute the deviation between actual measurements and the faultless (training) data. Then, the exponential smoothing method is used to set up a detection decision-based kNN metric for anomaly detection. The proposed procedure has been tested to detect cyber-attacks in a two-line three-bus power transmission system. The proposed approach has been shown good detection performance.

Due to the insufficient awareness of decision makers on the security risks of industrial cyber-physical systems(ICPS) under cyber-attacks, it is difficult to take effective defensive measures according to the characteristics of different cyber-attacks in advance. To solve the above problem, this paper gives a qualitative analysis method of ICPS security risk from the perspective of defenders. The ICPS being attacked is modeled as a dynamic closed-loop fusion model where the mathematical models of the physical plant and the feedback controller are established. Based on the fusion model, the disruption resources generated by attacks are mathematically described. Based on the designed Kalman filter, the detection of attacks is judged according to the residual value of the system. According to the disruption resources and detectability, a general security risk level model is further established to evaluate the security risk level of the system under attacks. The simulation experiments are conducted by using Matlab to analyze the destructiveness and detectability of attacks, where the results show that the proposed qualitative analysis method can effectively describe the security risk under the cyber-attacks.

Scam websites or illegitimate internet portals are widely used to mislead users into fraud or malicious attacks, which may involve compromise of vital information. Scammers misuse the secrecy and anonymity of the internet of facade their true identity and purposes behind numerous disguises. These can include false security alerts, information betrayal, and other misleading presentations to give the impression of legality and lawfulness. The proposed research is a web-based application - Scam Website Analyser- which enables checking whether a website is a scammed one.. The main aim of the research is to improve security and prevent scams of public websites. It ensures maintaining the details of scam websites in a database and also requests the websites of other databases using external APIs. The basic idea behind the research is the concept of user -orienteers where the user is able to get information about scam websites and prevent themselves from using those sites in future.

In this century, the demand for energy is increasing daily, and the need for energy resources has become urgent and inevitable. New ways of generating energy, such as renewable resources that depend on many sources, including the sun and wind energy will contribute to the future of humankind largely and effectively. These renewable sources are facing major challenges that cannot be ignored which also require more researches on appropriate solutions . This has led to the emergence of a new type of network user called prosumer, which causes new challenges such as the intermittent nature of renewable. Smart grids have emerged as a solution to integrate these distributed energy sources. It also provides a mechanism to maintain safety and security for power supply networks. The main idea of smart grids is to facilitate local production and consumption By customers and consumers.Distributed ledger technology (DLT) or Block-chain technology has evolved dramatically since 2008 that coincided with the birth of its first application Bitcoin, which is the first cryptocurrency. This innovation led to sparked in the digital revolution, which provides decentralization, security, and democratization of information storage and transfer systems across numerous sectors/industries. Block-chain can be applied for the sake of the durability and safety of energy systems. In this paper, we will propose a new distributed framework that provides protection based on block-chain technology for energy systems to enhance self-defense capability against those cyber-attacks.

With the great development of the information communication technology, power systems have been typical Cyber Physical Systems (CPSs). Although the control function of the grid side is becoming more intelligent, Grid Cyber Physical System (GCPS) brings the risk of potential cyberattacks. In this paper, the impacts of cyber-attacks against GCPS are analyzed based on confusion matrix model firstly, then a double-layer cyber physical collaboration control strategy adjustment methods is proposed considering the status of cyber modules and physical devices infected by cyber-attacks. Finally, the feasibility and effectiveness of the proposed method are verified on the IEEE standard system.

Cyber-power resiliency analysis of the distribution system is becoming critical with increase in adverse cyberevents. Distribution network operators need to assess and analyze the resiliency of the system utilizing the analytical tool with a carefully designed visualization and be driven by data and model-based analytics. This work introduces the Cyber-Physical Security Assessment Metric (CP-SAM) visualization tool to assist operators in ensuring the energy supply to critical loads during or after a cyber-attack. CP-SAM also provides decision support to operators utilizing measurement data and distribution power grid model and through well-designed visualization. The paper discusses the concepts of cyber-physical resiliency, software design considerations, open-source software components, and use cases for the tool to demonstrate the implementation and importance of the developed tool.

Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.

To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.

In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.

Computer networks and surging advancements of innovative information technology construct a critical infrastructure for network transactions of business entities. Information exchange and data access though such infrastructure is scrutinized by adversaries for vulnerabilities that lead to cyber-attacks. This paper presents an agent-based system modelling to conceptualize and extract explicit and latent structure of the complex enterprise systems as well as human interactions within the system to determine common vulnerabilities of the entity. The model captures emergent behavior resulting from interactions of multiple network agents including the number of workstations, regular, administrator and third-party users, external and internal attacks, defense mechanisms for the network setting, and many other parameters. A risk-based approach to modelling cybersecurity of a business entity is utilized to derive the rate of attacks. A neural network model will generalize the type of attack based on network traffic features allowing dynamic state changes. Rules of engagement to generate self-organizing behavior will be leveraged to appoint a defense mechanism suitable for the attack-state of the model. The effectiveness of the model will be depicted by time-state chart that shows the number of affected assets for the different types of attacks triggered by the entity risk and the time it takes to revert into normal state. The model will also associate a relevant cost per incident occurrence that derives the need for enhancement of security solutions.

Reliable and secure grid operations become more and more challenging in context of increasing IT/OT convergence and decreasing dynamic margins in today's power systems. To ensure the correct operation of monitoring and control functions in control centres, an intelligent assessment of the different information sources is necessary to provide a robust data source in case of critical physical events as well as cyber-attacks. Within this paper, a holistic data stream assessment methodology is proposed using an expert knowledge based cyber-physical situational awareness for different steady and transient system states. This approach goes beyond existing techniques by combining high-resolution PMU data with SCADA information as well as Digital Twin and AI based anomaly detection functionalities.

The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.