Prevent Cyber Attacks Research Articles

Assessing the Effectiveness of the Implementation of Cybercrimes Mitigation Strategies in Selected Commercial Banks in Tanzania

This study aimed to assess the effectiveness of implemented cybercrime mitigation strategies for commercial banks in Tanzania. Most financial sectors, like banks, are vulnerable to continuous attacks from external and internal cybercriminals such that the majority of banks spend their time updating and maintaining cybercrime mitigation strategies against cyber attacks. Despite the ongoing efforts to prevent cyber attacks the studies and experiences show that such attacks still occur regardless of the strong measures implemented against cyber attacks. It is articulated with different researchers that there is a gap to make a resilient and stronger systems against cybercrimes. This research assessed the effectiveness of cybercrime mitigation strategies by analyzing public awareness, budget allocation, support from management, and availability of skilled personnel. The study used a sample of 885 respondents from five biggest banks in Tanzania. The collected data were analyzed using descriptive statistical methods. The implications emanating from the study were discussed.

Cybersecurity, cyberspace and cyberthreats at the beginning of the 21st century: a Latin America typology and review

ABSTRACT Concepts of ‘cyberspace’, ‘cybersecurity’ and ‘cyberthreats’ and an analysis of policy documents are employed to explore the character and evolution of cybersecurity policies in Mexico, Colombia and the Dominican Republic, chosen as examples of ‘accelerated’, ‘transitional’ and ‘lagging’ Latin American development. Mexico focused on the stability of the Internet as a space that contributes to faster economic growth. Columbia’s ‘risk management’ path seeks a balance between plausible threat evaluation and the estimated cost of eliminating them and concentrates on the active prevention of cyberattacks, emphasising the importance of developing concrete mechanisms for international regulation in the field of cybersecurity. The approach to cybersecurity of the Dominican Republic is based on the idea of ensuring a rapid transition to an innovative development model with the widespread introduction of information and communication technology (ICT), while addressing domestic cybercrime to which ordinary citizens and businesses are constantly exposed to. Comparing these experiences, it is clear that these approaches are characterised by two trends: randomness and non-imitation.

A comparative evaluation of machine learning algorithms for network anomaly detection

In this era of digital transformation, the importance of network anomaly detection has been amplified to safeguard the security and integrity of various vital applications. These applications include the protection of critical infrastructures, prevention of cyber-attacks, and upkeep of network performance, among others. Our study presents a thorough evaluation of an array of machine learning algorithms for the proficient detection of network anomalies. These algorithms include, but are not limited to, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). We illustrate an in-depth comparison between these chosen algorithms, analyzing their performance metrics, strengths, and weaknesses, with a specific focus on their practical applicability and influence on network security methodologies. The investigation into these machine learning algorithms exhibits potential advantages and constraints of employing such methodologies for network anomaly detection. We further shed light on the determinants that affect their performance in diverse scenarios. Based on the exhaustive analysis, we provide suggestive guidelines for choosing the most suitable algorithm depending on specific use cases or requirements. This study thus serves as a comprehensive guide to understanding the role and impact of machine learning in the critical field of network anomaly detection.

Prosecuting Cybercrimes under International Legal Frameworks: Challenges and Innovations

Combating cybercrime is one of the most relevant areas of the legal community development since the widespread integration of digital technologies into all spheres of human activity demonstrates the need to regulate this sensitive area. The aims: are to study the international legal framework for countering cybercrime, as well as the system of challenges and innovations that exist in the modern world. Study design: 31 items of relevant scientific literature were analysed, which made it possible to comprehensively consider the issues of the article. Place and duration the study: The research took place during the second semester of the 2022-2023 academic years. Methodology: the main methods used in the article were the content analysis, forecasting method, and systematic method. Results: in the article modern interpretations of the term “cybercrime” were defined, certain legal international agreements that counteract its spread (primarily the Budapest Convention as an important current document of modern legal influence) and traces the mechanisms of counteraction and methods of their improvement were examined. Other important legal acts, such as the European Union Directive on the prevention of cyber-attacks on information systems and the European Commission Directive on combating fraud and other financial crimes on the Internet, were also considered. As a result, it was found that there were differences at the interstate level in the organisation of the cooperation, which included multilateral and bilateral agreements. The identified problems included insufficient interaction between the law enforcement agencies of different countries at the relevant stages, as well as the lack of an agreed mechanism for obtaining external data. The conclusions established that close ties at the level of international law enforcement systems aimed at addressing cybercrime issues could be effective in overcoming these difficulties.

Five ways to use AI to prevent cyber attacks

Artificial intelligence has the potential to plug the gaps in your website defences. At a time of limited budgets, constrained resources and a global skills shortage, AI is capable of taking over many previously labour-intensive tasks and of providing insight into threats and weaknesses. This article looks at five key ways in which AI offers a boost to your web security.

Highly efficient secure linear algebra for private machine learning classifications over malicious clients in the post-quantum world

Over the last decade there has a been widespread usage of Machine Learning (ML) classifiers in cases such accurate disease diagnosis at clinics, credit card fraud detection in banks, cyber-attacks prevention of computer systems in different industries, etc. However, privacy and security concerns and law regulations have been an obstacle to the usage of ML classifiers. To this end, this paper addresses the scenario where a server has a private trained ML model, and one or more clients have private queries that they wish to classify using the server's model. During the process, the server learns nothing, while the clients learn only their final classifications and nothing else. Several ML classification algorithms, such as Deep Neural Networks, Support Vector Machines, Logistic Regression, different flavors of Naïve Bayes, etc., can be expressed in terms of linear algebra operations. To this end, initially, as building blocks, several novel secure linear algebra operations are proposed. On top of them novel secure ML classification algorithms are proposed for the aforementioned classifiers under strict security, privacy and efficiency constraints and their security is proven under the semi-honest model. Since the used underlying cryptographic primitives are shown to be resilient to quantum computer attacks, the proposed algorithms are also suitable for the post-quantum world. Furthermore, the proposed algorithms are non-interactive and, based on where the bulk of the operations are done, they have the flexibility to be server or client centric. Theoretical analysis and extensive experimental evaluations over benchmark datasets show that the proposed secure linear algebra operations, hence the secure ML algorithms build on top of them, outperform the state-of-the-art schemes in terms of computation and communication costs as well as on security and privacy characteristics. Moreover, and to the best of the authors’ knowledge, for the first time in literature the security of the proposed algorithms is proven when dealing with multiple malicious clients during classifications.

Algorithmic means of ensuring network security and websites: trends, models, future cases

The purpose of the study is to establish probable trends in the development of algorithmic means of network security and the protection of web resources in the future. The research methods used in this publication are a bibliometric analysis of 500 relevant publications, which allowed us to establish probable trends in the future development of the subject field. The study found that currently the most likely algorithmic means of network security and website protection that will be intensively developed in the future are blockchain technologies (to protect inter-resource contact), deep and machine learning (to analyze and detect attacks and digital anomalies), artificial intelligence and neural networks (to develop complex security algorithms), and predictive analysis (to prevent possible attacks and malicious data injections). At the same time, technological development makes it possible to identify alternative security tools, including quantum and post-quantum cryptography (which is possible due to the development of quantum computing), augmented reality (which is the next iteration of the development of the interface between machine-human interaction), biometric identification (which is the next iteration of authentication and recognition systems) and DevSecOps (which is a promising technology for the production of digital tools and systems that have a relatively lower level of vulnerability to known digital threats). The correlative impact of Industry 4.0 technologies and solutions on the studied aspects of the security sector of the World Wide Web has been established. The growth of the network of devices requires the improvement of security algorithms in the paradigm of Industry 4.0 technologies, which will allow more effective detection and prevention of cyberattacks and protection of user data.

A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM

The Internet of Things (IoT) is one of the technologies used in many fields today. Cyber attacks against IoT/Industrial IoT (IIoT) networks, which are increasingly used thanks to the convenience it provides, are constantly increasing. Detection of attacks against IoT/IIoT networks is one of the popular topics recently. The development of a dataset for IoT applications is essential for the intrusion detection in IoT networks. In this context, the ToN_IoT dataset created in the laboratory of UNSW Canberra (Australia) is one of the most comprehensive datasets that can be used to detect cyber attacks on IoT networks. In this study, fridge, garage door, GPS tracker, modbus, motion light, weather, thermostat datasets related to IoT sensors from ToN_IoT datasets were used. The datasets used were subjected to multi-class classification with the Light Gradient Boosting Machine (LGBM) classifier proposed in the study. The obtained results were compared with the literature and it was seen that the proposed method provided the highest classification performance in the literature. It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.

Recommender Systems in Cybersecurity

With the growth of CyberTerrorism, enterprises worldwide have been struggling to stop intruders from obtaining private data. Despite the efforts made by Cybersecurity experts, the shortage of skillful security teams and the usage of intelligent attacks have slowed down the enhancement of defense mechanisms. Furthermore, the pandemic in 2020 forced organizations to work in remote environments with poor security, leading to increased cyberattacks. One possible solution for these problems is the implementation of Recommender Systems to assist Cybersecurity human operators. Our goal is to survey the application of Recommender Systems in Cybersecurity architectures. These decision-support tools deal with information overload through filtering and prioritization methods, allowing businesses to increase revenue, achieve better user satisfaction, and make faster and more efficient decisions in various domains (e-commerce, healthcare, finance, and other fields). Several reports demonstrate the potential of using these recommendation structures to enhance the detection and prevention of cyberattacks and aid Cybersecurity experts in treating client incidents. This survey discusses several studies where Recommender Systems are implemented in Cybersecurity with encouraging results. One promising direction explored by the community is using Recommender Systems as attack predictors and navigation assistance tools. As contributions, we show the recent efforts in this area and summarize them in a table. Furthermore, we provide an in-depth analysis of potential research lines. For example, the inclusion of Recommender Systems in security information event management systems and security orchestration, automation, and response applications could decrease their complexity and information overload.

Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.

ARTIFICIAL INTELLIGENCE IN SUSTAINABLE ENERGY INDUSTRY: STATUS QUO, CHALLENGES, AND OPPORTUNITIES

This study focuses on the future of AI in the energy sector, examining how AI can be used to improve efficiency and sustainability in the sector. The study aims to provide a realistic baseline of AI technology that can be used to compare efforts, ambitions, new applications, and challenges around the world. We covered three main topics: (i) how AI is being used in solar and hydrogen power generation; (ii) how AI is being used in supply and demand management control; and (iii) the latest advances in AI technology. In this research we explored how AI techniques outperform traditional models in controllability, energy efficiency optimization, cyber-attack prevention, IoT, big data handling, smart grid, robotics, predictive maintenance control, and computational efficiency. Our study found that AI is becoming an important tool for a new and data-intensive energy industry, which is providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. KEYWORDS: Artificial Intelligence; Renewable Energy; Energy Demand; Decision Making; Big Data; Energy Digitization

Libraries and cyber security: the importance of the human factor in preventing cyber attacks

PurposeThis paper aims to investigate cyber security awareness of the staff of Estonian libraries and gives an overview why libraries could be a target of cyber attacks and why librarians need cyber security at first place.Design/methodology/approachThe data used in this paper is based on a review of relevant literature to provide an overview of the concept of cyber security, and the results of the original online survey created by the paper’s author, conducted among Estonian librarians. The online questionnaire was developed using the world-recognised human aspects of information security questionnaire (HAIS-Q), which is based on the knowledge-attitudes-behaviour (KAB) methodology. A total of 388 completed questionnaires were returned from employees of academic, specialised, public and school libraries. The results are interpreted on the basis of descriptive statistics and Kruger and Kearney approach.FindingsThe final score of library employees is 86, which is classified as good, but based on the result, two focus areas need more attention than previously, which are the use of devices and prevention and handling of incidents.Originality/valueThe cyber behaviour of library employees has not been widely studied in the world using HAIS-Q and KAB models, and to the best of the authors’ knowledge, no research has been previously carried out in the Estonian library context into cyber security awareness.

Knowledge-Driven Cybersecurity Intelligence: Software Vulnerability Coexploitation Behavior Discovery

Coexploitation behavior, referring to multiple software vulnerabilities being exploited jointly by one or more exploits, brings enormous challenges to the prevention and remediation of cyberattacks. Leveraging the latest advances in graph-driven intelligence, this article formulates vulnerability coexploitation behavior discovery as a link prediction problem between vulnerability entities within a vulnerability knowledge graph. We propose a modality-aware graph convolutional network (MAGCN) module to embed multimodality entity attributes and topological graph connectivity features into a unified lower dimensional feature space to boost link prediction performance. We further design a graph knowledge transfer learning (GKTL) strategy to transfer knowledge between subgraphs extracted from the same knowledge graph. Experimental results on a real-world dataset containing coexploitation incidents between 1995 and 2021 show that MAGCN achieved 81.34% on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 score when applying the GKTL strategy, superior to other graph neural network modules, such as GCN, GraphSAGE, EdgeGCN, and GINGCN.

A Review on Major Cyber Threats and Recommended Counter Measures

Abstract: It was predicted that the number of cyber attacks will rise by a factor of two every year. There have been several highprofile cyber attacks on government organizations in India in recent years. Ransomware attacks, Advanced Persistent Threats, Internet of Things, Cloud security risks, Insider threats, Social engineering attacks, Supply chain attacks, Zero-day exploits in cyber threats are some of the significant types of cyber attacks affecting Indian government entities. Security measures, such as regularly backing up important data, installing antivirus software, training employees on how to identify and avoid phishing emails, and maintaining up-to-date software and operating systems are common countermeasures against significant cyber attacks. Preventing cyber attacks requires a multi-layered approach that addresses vulnerabilities in people, processes, and technology. Empowering, the agencies like CERT-In for cyber resilience, strengthening the penalty framework for noncompliance, advisories on information and data security practices are some of the strategies adopted by the government to secure cyberspace. It is necessary to stay informed about the latest threats and continually reassess and update security measures as needed.

Legal Status of Decentralized Finance: Towards the Articulation of Issue

The paper discusses the issues of legal regulation of decentralized financial services (DeFi). The purpose of the work is to form a systematic understanding of the direction and scope in which they can be regulated based on the DeFi technological features and the nature of the relationships that arise between digital platforms and their users.The methodological basis of the study is determined by the range of tasks and is focused on the analysis of decentralized finance simultaneously in technological, socio-cultural, political and legal aspects. Structural functional, taxonomic, constructive, situational, innovative, targeted and program-targeted methods form the complexity of the study. Taken together, they allowed us to study DeFi in their systemic unity and formulate the following conclusion. Decentralized finance is an independent model of finance organization both in terms of a set of technical characteristics (the use of distributed registry technology, smart contract protocols, oracles, etc.) and in terms of the nature of relations between the parties (horizontal P2P communications). The identified features allow us to consider them as a separate object of legal regulation. At the same time, it is important to take into account that the matrix of DeFi regulation at the national and international levels should be built in accordance with their taxonomy and assume simultaneous balanced consideration of technological features and product affiliation of services. The legal regulation of decentralized finance in the Russian Federation should primarily be focused on minimizing the risks associated with user identification, as well as determining the legal status of decentralized autonomous societies (DAOs), organizing centralized examination of protocols and preventing cyber attacks.

A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions

Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks.

Defending medical facilities from cyber attacks: critical issues with the principle of due diligence in international law

ABSTRACT I explore reasons why existing defense has failed to prevent cyber attacks on medical facilities. I look into one of the least studied notions of cyberspace behavior known as the principle of due diligence. The principle posits that states should do their best not to allow their territory to be used for cyber operations that produce adverse consequences for other states. I point out three reasons why the principle has failed to protect medical facilities. First, the principle of due diligence suffers from flaws with its enforcement mechanism and applicability to nonstate hackers. Second, the principle suffers from the lack of specificity in that it has never clarified what potential targets are supposed to be spared. Finally, the principle is prone to fail to mobilize states enmeshed with providers of network infrastructure – internet service providers (ISPs) – to practice due diligence. In sum, my analysis shows that factors that are external to the principle are as critical as internal ones in shaping the principle’s ineffectiveness. While the principle is not the only cause of failure to prevent attacks on medical facilities, it is one of the reasons for the failure.

Implications of false alarms in dynamic games on cyber-security

Cyber-threats including computer viruses, data breaches, and denial-of-service attacks have become increasingly prevalent and need to be handled effectively. Imperfect prevention of cyber-attacks inevitably results in false alarms from the defense system. We consider a dynamic game in which a defender operating an intrusion detection system is penalized for false alarms. By characterizing the equilibrium of the game, we search for the implications of false alarms under various circumstances. Specifically, we consider the cyber-insurance market, operational risk, and government intervention. Our model shows that both demand and supply of cyber-insurance can be low, which explains the underdevelopment of the cyber-insurance market. The analysis of an extended model with operational risk shows that reducing the operational risk decreases the cyber-risks but increases the false alarm rate. Regarding government intervention, we suggest a simple policy that achieves a socially optimal outcome.

V-SOC4AS: A Vehicle-SOC for Improving Automotive Security

Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of intra-vehicle communication, that is controller area network (CAN), local interconnect network (LIN), FlexRay, media oriented systems transport (MOST), and Ethernet. Therefore, to achieve this goal, security information and event management (SIEM) was used to monitor and detect malicious attacks in intra-vehicle and inter-vehicle communications: messages transmitted between vehicle ECUs; infotainment and telematics systems, which provide passengers with entertainment capabilities and information about the vehicle system; and vehicular ports, which allow vehicles to connect to diagnostic devices, upload content of various types. As a result, this allows the automation and improvement of threat detection and incident response processes. Furthermore, the V-SOC4AS allows the classification of the received message as malicious and non-malicious and acquisition of additional information about the type of attack. Thus, this reduces the detection time and provides more support for response activities. Experimental evaluation was conducted on two state-of-the-art attacks: denial of service (DoS) and fuzzing. An open-source dataset was used to simulate the vehicles. V-SOC4AS exploits security information and event management to analyze the packets sent by a vehicle using a rule-based mechanism. If the payload contains a CAN frame attack, it is notified to the SOC analysts.

The Problem of Defining a Cyber Attack

The author discusses problematic aspects in terms of developing a definition of a cyber attack. It is noted that such illegal actions capable of disabling nuclear centrifuges, air defense systems and electrical networks, etc., undoubtedly pose a serious threat to national security. In fact, in their destructive power, cyber attacks are approaching armed action. There are no treaties or other normative documents in international law that can regulate international cooperation in the prevention and limitation of cyber attacks. A new comprehensive legal framework, both at the domestic and international levels, is needed to counter cyberattacks more effectively. The importance of using theoretical and legal developments in the field of information security in law-making activities is fixed. International efforts to regulate cyberattacks should begin with an agreement on the definition of cyberattack, cybercrime and cyberwarfare. This would lay the foundation for expanding international cooperation in the field of information exchange, evidence collection and criminal prosecution of persons involved in cyber attacks, and more importantly, for a new international legal act on cyber attacks. The author presents some recommendations for further improvement of legal measures to counteract epistemological wars.