Cybersecurity Domain Research Articles

Enhancing Cybersecurity in Financial Services using Single Value Neutrosophic Fuzzy Soft Expert Set

Cybersecurity has become a primary concern as the financial sectors generally handle increasing cyber-attacks and an increasing danger of financial crime. Recently, ransomware attacks have intensified, affecting enterprises, and crucial infrastructure worldwide. Ransomware employs sophisticated encryption techniques to encrypt data on the targeted device, then requests payment for decrypting the data. Artificial intelligence (AI) approaches involving ML were progressively employed in the domain of cybersecurity and significantly subsidized to preventing and detecting variety of threats. On the other hand, the several researchers that employed ML to identify ransomware are still constrained by the accuracy of models, the complication of malware, the high false-positive rate, and the lack of setting up the appropriate analysis environment. Therefore, there is a need to design efficient ransomware detection based on ML algorithms. This work introduces a modified Single Value Neutrosophic Fuzzy Soft Expert Set (M-SVNFSES) technique for cyberattack detection. The main purpose of the M-SVNFSES system is to detect and recognize the existence of cyberattacks in the financial sectors. In the M-SVNFSES technique, min-max normalization is used as an initial pre-processing stage. For the identification of cyberattacks in the financial sectors, the M-SVNFSES technique uses the SVNFSES model. To enhance its performance, the M-SVNFSES technique makes use of a bat optimization algorithm (BOA). The performance of the M-SVNFSES methodology was extensively studied using financial datasets. The experimental outcomes depicted that the M-SVNFSES method reaches optimal detection performance in attack detection process

A Comprehensive Approach to Cyberattack Detection in Edge Computing Environments

This research is concerned with the critical domain of cybersecurity in edge computing environments, which aims to strengthen defenses against increasing cyber threats that target interconnected Internet of Things (IoT) devices. The widespread adoption of edge computing introduces vulnerabilities that necessitate a strong framework for detecting cyberattacks. This study utilizes Long Short-Term Memory (LSTM) networks to present a comprehensive approach based on stacked LSTM layers for detecting and mitigating cyber threats in the dynamic landscape of edge networks. Using the NSL-KDD dataset and rigorous experimentation, this model demonstrates its ability to detect subtle anomalies in network traffic, which can be used to accurately classify malicious activities while minimizing false alarms. The findings highlight the potential of LSTM-based approaches to enhance security at the edge, providing promising avenues for strengthening IoT ecosystems’ integrity and resilience against emerging cyber threats.

Leveraging AI and Machine Learning for Cyber Threat Analysis

This paper explores how AI and Machine Learning is becoming a key participant in cybersecurity's emergence and constantly shifting nature. It disillusioned traditional forms of detection, such as signature-based detection and rule-based systems, with the idea that they are suitable for dealing with new complex threats like zero-day vulnerabilities, APTs, and social engineering attacks. AI and ML provide enhanced features for accurate time operation, real-time threat identification tools, anomaly identification tools, behavioral tools, and predictive analysis, and therefore, have become helpful in the modern context and central to the cybersecurity architecture of many organizations. The paper fleshes out the strengths of AI in intrusion detection, dynamic malware analysis, and phishing detection, among others. The following AI methods are covered in detail: Supervised and unsupervised learning algorithms are used in processing datasets, and autoencoders are used. Deep learning models are suitable for processing big data in real-time. The issues of AI incorporating false positive outcomes, adversarial AI, and data quality problems are also addressed, along with ethical and privacy considerations. The roles of automated incident reporting and self-learning security systems, as forecasted for AI's contributions in managing cyber threats, are designated as crucial. Finally, the paper concludes by stressing the need for organizations to adopt AI solutions for the preemptive approach in the cybersecurity domain.

Exploring and Acquiring Modern Human Resource Competencies in Cybersecurity Amidst State Digital Transformation

This paper validates the relevance of contemporary electronic communication systems, networks, and hardware and software suites and tools being particularly susceptible to cyber influences. The goal of the article is to concisely summarize international experience in modern human resource competencies within the cybersecurity domain and to identify their components according to specific directions. The paper examines current international trends regarding the formation of a minimal necessary package for creating, implementing, technical support, and enhancing an information security and cyber defense management system. This necessitates the development of a digitally aware society by the state, a crucial area of its internal policy. It emphasizes the undeniable fact that the demand for cybersecurity professionals will continually grow as high-technology society advances. In the context of the state’s total defense, the role of unified training for defense force personnel and the civilian sector in cybersecurity is beyond doubt. It is concluded that the main focus in forming Cybercom of partner countries is adequate funding, careful selection of personnel, quality professional training, and ensuring full interoperability of diverse structural units of subjects ensuring information protection and cybersecurity at the state level. The share of cyber threats is increasing, and this trend will intensify over the next decade as the internet technology sphere is developing rapidly, and digital solutions are being used in combination (e.g., artificial intelligence and blockchain). For instance, current Russia is one of the main threats to national and international cybersecurity, actively implementing the concept of information confrontation based on a combination of destructive actions in cyberspace. An increase in conflicts between states and the intensity of intelligence and subversive activities in cyberspace is expected. The number of states seeking to form their own cyber-intelligence, master the latest methods of destructive influence in cyberspace, and strengthen state control over the state segment of the Internet is growing. The technical level of cyber threats is increasing, and new tools and mechanisms for cyber attacks are constantly being improved and developed

Emerging Cyber Security Challenges: Implications for Iranian National Security

In the third decade of 21st century, cyber security has become a completely developed national security paradigm like other prominent security paradigms such as physical security, economic security, and food security. Unmatched pace of technological advancements in the fields of Information Technology, Artificial Intelligence, automation, and communication services have transformed key processes of governance, commerce, and trade from physical to cyber space. As human dependence on cyber technologies increased new questions about the prevailing security models has come to the fore during the same time. Concepts like network security, data security or infrastructure security are no longer isolated domains but have transformed as securitization of cyber domain has become an ongoing global phenomenon. This paper focuses on how this transformation of geopolitical conflicts, from physical to cyber arena, is taking place where traditional concepts of conventional war are changing. It also examines how cyber security threat matrix is diametrically different from all prevailing military doctrinal construct? These questions certainly demand a new theoretical framework that can put this rapidly emerging new security paradigm. This study attempts to address this important question as well while taking the international and regional developments with special focus on the emerging cyber security domain in Iran.

ARTIFICIAL INTELLIGENCE AND NATIONAL SECURITY: PERSPECTIVE OF THE GLOBAL SOUTH

More than six decades since its inception, Artificial Intelligence (AI) stands at the cusp of a transformative shift. The global perspective on AI has evolved optimistically, as it increasingly permeates every facet of human life. AI is revolutionizing national security strategies and capabilities worldwide, but its impact on the Global South remains a topic of growing significance and concern. Every nation actively seeks to bolster internal security through AI-driven initiatives, including surveillance, cyber security, and autonomous technologies. This review paper delves into AI's role in analyzing vast datasets, uncovering patterns, and identifying security threats and challenges focusing specifically on the Global South. It considers the potential advantages AI offers in enhancing national security capabilities while addressing concerns surrounding its integration. Drawing from existing literature, it presents a comprehensive analysis of AI's prospective future in the cyber and national security domains within these nations. Ultimately, this paper aims to answer whether AI serves as a facilitator in strengthening internal security or poses unforeseen challenges and raises the importance of capacity-building, technology transfer, and international cooperation. It provides valuable insights into the evolving landscape of AI in the context of national security in the Global South.

Neuroguard:Unveiling the Strength of Lightfooted Anomaly Detection with Swift-Net Neural Networks in Countering Network Threats

In the dominion of cybersecurity, the prime tasks revolve around recognizing and moderating network breaches. This research paper impacts the widely recognized CICIDS2017 dataset to conduct a complete evaluation and comparison of numerous deep learning and machine learning representations designed for Anomaly-detection by the analysis of a diverse array of algorithms, spanning from traditional methodologies like logistic regression to more modern advances such as K-Nearest Neighbors (KNN) and state-of-the-art Swift-Net neural networks. The research also delves into the realism of employing dimensionality reduction and feature selection procedures, remarkably Principal Component Analysis (PCA) in addition with Gaussian Mixture Models (GMM). The implications of this consideration are substantial for the enhancement of network security with an emphasis of the efficiency of PCA and GMM in facilitating data visualization, enabling a deeper understanding of network behavior. Moreover, the paper highlights the potential of Swift-Net for real-time threat detection, signifying its relevance in the evolving cybersecurity environment. As the cybersecurity domain undergoes constant transformation, this research serves as a valuable reserve, paving the way for more effective Anomaly detection techniques and the employment of efficient network security solutions. These outcomes offer acute insights to reinforce network safety.

Enhancing Security Frameworks with Artificial Intelligence in Cybersecurity

Cybersecurity, in the digital era we live in today, has become a major concern that demands innovation. Data Science and Artificial Intelligence (AI) have played a central role in changing the way we understand and address cyber threats. This research will review the important role of innovation in this technology in improving an organization's ability to detect, prevent, and respond to cyber attacks. Identifying patterns and gaining insights from security events in cyber data, while developing appropriate data-based models, is a key element in realizing automated and intelligent security systems. This research reviews needs in the cyber security domain that can be addressed through Artificial Intelligence (AI) techniques. In this study, we employed quantitative methods to assess the impact of artificial intelligence on enhancing cybersecurity by distributing questionnaires to 85 respondents, which included companies operating in the banking and IT sectors. In addition, this research will explore how data-based intelligent decision-making systems are able to protect systems from known and unknown cyber attacks. This research will conclude by considering the future potential of Artificial Intelligence and cybersecurity.

Con2Mix: A semi-supervised method for imbalanced tabular security data1

Con2Mix (Contrastive Double Mixup) is a new semi-supervised learning methodology that innovates a triplet mixup data augmentation approach for finding code vulnerabilities in imbalanced, tabular security data sets. Tabular data sets in cybersecurity domains are widely known to pose challenges for machine learning because of their heavily imbalanced data (e.g., a small number of labeled attack samples buried in a sea of mostly benign, unlabeled data). Semi-supervised learning leverages a small subset of labeled data and a large subset of unlabeled data to train a learning model. While semi-supervised methods have been well studied in image and language domains, in security domains they remain underutilized, especially on tabular security data sets which pose especially difficult contextual information loss and balance challenges for machine learning. Experiments applying Con2Mix to collected security data sets show promise for addressing these challenges, achieving state-of-the-art performance on two evaluated data sets compared with other methods.

Enhancing Competency of Cybersecurity Through Implementation of the “CAPTURE THE FLAG” On College in Indonesia

Academic institutions encounter several challenges when it comes to determining the most effective pedagogical approaches for enhancing their students' proficiency in the domain of cybersecurity. Conversely, cybersecurity gamification, exemplified by Capture the Flag (CTF) events in the industry, is widely considered an appropriate avenue for fostering cybersecurity learning. This research endeavors to assess the effectiveness of employing gamification as an instructional tool to enhance cybersecurity competency within the college context. The study employs a quantitative research method, adopting an experimental approach with a pre-experimental design. The research findings reveal the following key points 1) the implementation of gamification resulted in a significant enhancement of students' competency in cryptography, web vulnerability, and overall cybersecurity, 2) the gamification experiment demonstrated a stronger influence on enhancing practical skills as opposed to theoretical knowledge, 3) the evaluation of gamification's effectiveness in augmenting cybersecurity competency yielded a medium level (medium-g) of efficacy, reaching 65.91%. Although the medium level of effectiveness was observed in the context of gamification, it should be acknowledged that gamification holds substantial potential for advancing cybersecurity competency. For the attainment of optimal improvement in cybersecurity skills, a consistent and regular integration of gamification is recommended, while concurrently addressing the three pillars of cybersecurity. Furthermore, the utilization of gamification also presents opportunities for cultivating cybersecurity incident handling capabilities and the simulation of cyberwarfare scenarios

Unequal Culture of Women and Minorities in Cybersecurity Domain

Women and ethnic minorities have historically been underrepresented in the cybersecurity industry. Although there are an increasing number of open opportunities in the cybersecurity industry, this pattern has not changed. There is still an imbalance in the participation of women and ethnic minorities in the cybersecurity domain. Issues that impact both Africa and the global participation of women and ethnic minorities in cyber-security include inadequate representation and awareness as well as retention problems. Further, issues such as harassment, gender bias and the idea that cybersecurity is a “man’s world” are also contributing factors. This paper provides a framework for discussing some of the skills that are available to women and underrepresented groups and argues for the necessity to solve this problem. Programs striving to increase the participation of women and ethnic minorities in cybersecurity are discussed. Lastly, some suggestions to remediate this pervasive issue are also provided.

Digital innovation through cybersecurity learning factories

Globally, the cybersecurity workforce changes tremendous challenges. Shortages in specialised cybersecurity staff members essentially puts organisations at risk. New graduates can face difficulties in entering the cybersecurity domain due to a lack of experience and knowledge. However, with the advent of newer techniques for knowledge development, we find that learning factories offer a fresh perspective. Learning factories provide a mechanism to remove the barriers in the field of cybersecurity and cultivate a nurturing training environment. This paper looks at the modernisation of traditional training by covering the application of learning factories in the cybersecurity field. It aims to show how knowledge can be geared into more practical schemes to empower participants and expose them to critical cybersecurity skills. Through the paper, it will be demonstrated that learning factories can be used for real-world learning and information sharing. Learning factories embody the principles of knowledge sharing and promotes more efficient knowledge management. With the use of nominated tools and technologies cyber security learning factories can help measure the effectiveness of worker training as well provide for consistent facilitated training. Overall, learning factories can help to transform training and build knowledge application.
 Learning factories may be set up to tackle real industry challenges and are particularly useful in the field of cybersecurity. Using learning factories there is an opportunity to advance multi-dimensional cybersecurity skills and develop innovation in the field. Due to the added advantages of Information and Communication Technology (ICT) being virtually accessible, there is the added benefits of agility, responsiveness and increased engagement. Using a variety of modes, cybersecurity learning factories can combine the techniques of gamification, videos, multi-media and simulation. All of this provided an augmented and enriched experience for participants. Learning factories are a low-cost solution to replicating working environments thus assisting in skills development. Through the application of learning factories, a skilled workforce can be developed and cultivated.

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

AbstractThe rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to detect, and novel approaches utilize machine learning to identify compromised accounts. This paper aims to conduct a literature review on account takeover detection and user behavior analysis within the cybersecurity domain. By exploring these areas, the goal is to combat account takeovers and other fraudulent attempts effectively.

Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media

In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustainable strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research provides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.

Cybersecurity Strategies for Safeguarding Customer’s Data and Preventing Financial Fraud in the United States Financial Sectors

As the financial sectors in the United States deal with expanding cyberthreats and a rising danger of financial crime, cybersecurity has become a top priority. This paper examines the crucial cybersecurity techniques used by financial institutions to protect client information and counter the growing risk of financial fraud. It proves that understanding common fraud tactics used to defraud financial institutions and customers, putting fraud detection and prevention techniques like anomaly detection and machine learning into practice, and using transaction monitoring and anti-money laundering tactics to spot and stop fraudulent activity are all necessary for preventing financial fraud. The paper begins by reviewing the common cyber dangers affecting the financial industry and the strategies used by cybercriminals to circumvent security precautions and take advantage of weaknesses. After looking at potential risks, the paper highlights the importance of proactive cybersecurity measures and risk mitigation techniques. It highlights crucial components of cybersecurity frameworks, including strong data encryption, multifactor authentication, intrusion detection systems, and ongoing security monitoring. This paper also emphasizes the value of educating and training financial institution staff members to increase cybersecurity resilience. It underlines the significance of building a strong security culture, educating personnel about potential dangers, and encouraging responsible management of client data. The study also explores the advantages of financial organizations working together and exchanging threat knowledge. It examines industry alliances, information-sharing platforms, and public-private partnerships as crucial methods for group protection against cyber threats. This paper highlighted the significance of artificial intelligence and machine learning in cybersecurity domain. It demonstrates how these technologies improve cybersecurity systems' capabilities by spotting irregularities and potential attacks. It emphasizes the significance of taking a proactive and dynamic strategy to securing client information and maintaining faith in the United States’ financial sectors. Overall, this paper provides a thorough overview of cybersecurity tactics crucial for protecting consumer data and avoiding financial fraud in the financial sectors across the United States. By taking a vigilant, team-based, and technology-driven strategy, financial institutions may strengthen their cyber defenses, protect the data of their clients, and defend the integrity of the financial system.

Security and privacy problems in voice assistant applications: A survey

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.

Implementing Data Exfiltration Defense in Situ: A Survey of Countermeasures and Human Involvement

In this article we consider the problem of defending against increasing data exfiltration threats in the domain of cybersecurity. We review existing work on exfiltration threats and corresponding countermeasures. We consider current problems and challenges that need to be addressed to provide a qualitatively better level of protection against data exfiltration. After considering the magnitude of the data exfiltration threat, we outline the objectives of this article and the scope of the review. We then provide an extensive discussion of present methods of defending against data exfiltration. We note that current methodologies for defending against data exfiltration do not connect well with domain experts, both as sources of knowledge and as partners in decision-making. However, human interventions continue to be required in cybersecurity. Thus, cybersecurity applications are necessarily socio-technical systems that cannot be safely and efficiently operated without considering relevant human factor issues. We conclude with a call for approaches that can more effectively integrate human expertise into defense against data exfiltration.

Experiences From a Multi-National Course in Cybersecurity Awareness Raising

The European Union (EU), as well as the entire world, is facing emerging challenges in the cybersecurity domain. Two of the most prominent challenges are citizens’ cybersecurity awareness which is the first line of defense against cybersecurity incidents, and the cybersecurity skill gap expected to lead to a future shortage of cybersecurity professionals. This paper presents an effort to combat those issues through the implementation of an intra-European course on cybersecurity awareness. The course engages university students from four EU member states who learn about increasing cybersecurity awareness while practically developing cybersecurity awareness activities for preadolescents. The paper provides an overview of the course and lessons learned from implementing it in international cooperation. The intention is to provide a guide for the development of such courses and outline success factors others can adopt and pitfalls that should be avoided.

Research Areas in Cyber Security and Forensic and Centre of Excellence for Forensic Laboratory

This research work identifies the national and international related work to identify the cutting-edge areas of research problems in the domain of cloud computing , mobile software and multimedia data for its security and cyber forensics. The explanatory research identifies the statement of problems with its aim, objectives, and the scope of the work to be conducted into the proposed cyber forensic laboratory. The hardware and software requirements for the cybercrime digital forensic laboratory are identified to conduct cybercrime research in the domain of cyber security for the recommendation of security. The Centre of excellence for computer cybercrime digital forensics will be useful for reading, writing, research, and development to give practical and feasible solutions for building digital forensic tools in the cutting domain of computing systems such as Cloud Computing, Web Applications, Mobile communication, and Multimedia Systems. The hardware and software requirements for setting up the forensic laboratory are identified and the budget expected to set the centre of excellence for a cybercrime investigation laboratory is approximately Rs.7,79,83,434.39/- which may vary depending on versions and updates to hardware and software’s.

An application of DEMATEL and fuzzy DEMATEL to evaluate the interaction of safety management system and cybersecurity management system in automated vehicles

To ensure the safety and security of Automated Vehicles (Avs), the interaction between the Functional Safety (FuSa) and the Cybersecurity (CS) domains needs to be managed systematically. There is a demand to develop effective and structured management systems to support the homologation process. From this motivation, identifying the interaction between the Safety Management System (SMS) and the Cybersecurity Management System (CSMS) is a fundamental aspect and needs to be improved for HAD systems. Hence, the classical Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy DEMATEL are applied to evaluate the influential factors that can impact the safety and security of the HAD systems. This paper proposes a list of influencing factors focusing on the interaction between SMS and CSMS for HAD systems. Additionally, the results of an anonymously conducted survey among experts from industry and research are presented and used as inputs for the methods. This work helps to understand the relationship between influencing factors and provides a simplified, easy-to-visualized, and valuable guide for developing HAD systems. The result of this study shows that the most important influential factor is F13. Moreover, the cause and effect of the factors are illustrated numerically and graphically. The influential factors F1 to F7 are identified as the cause and F8 to F13 are reasoned to effect. Finally, a circular representation of the influential factors and their interaction is presented in this paper.