Artificial Intelligence In Cybersecurity Research Articles

Role of AI in enhancing cybersecurity measures to protect sensitive financial data

In the digital age, the protection of sensitive financial data has become paramount due to the increasing sophistication of cyber threats. Artificial Intelligence (AI) has emerged as a critical tool in enhancing cybersecurity measures within the financial sector. This paper explores the multifaceted role of AI in safeguarding financial information from breaches and unauthorized access. AI-driven systems offer real-time fraud detection, advanced encryption techniques, and proactive threat identification, significantly reducing the risk of data compromise. By leveraging machine learning algorithms, AI can identify anomalous patterns and predict potential security breaches, enabling financial institutions to respond swiftly and effectively. Furthermore, AI enhances regulatory compliance by automating audits and ensuring adherence to security protocols. The integration of AI in cybersecurity not only fortifies the defense mechanisms of financial institutions but also fosters trust and reliability in digital financial transactions. This study underscores the transformative impact of AI on financial data security and highlights the ongoing advancements in AI technologies that continue to shape the future of cybersecurity.

The application of artificial intelligence in internet security

The global integration of the internet has led to a significant increase in the importance of cybersecurity. Artificial Intelligence (AI) has emerged as a viable solution for enhancing cybersecurity measures. AI has the potential to improve the speed and effectiveness of detecting and responding to cyber threats. This study explores the intersection of network security and AI, with a focus on the various ways in which AI can be used to enhance network security, such as intrusion detection, malware detection, and behavioural analytics. The study also examines the potential risks associated with AI in cybersecurity, including the possibility of AI being utilized for cyber-attacks. Additionally, the study discusses the challenges associated with implementing AI for network security, such as the lack of available large datasets for AI training, network infrastructure complexity, and the requirement for skilled AI professionals in cybersecurity. Ethical considerations arising from the use of AI in network security are also addressed. The study emphasizes the need for a balanced approach towards integrating AI into cybersecurity measures, taking into account potential benefits and challenges.

AI in Cybersecurity: Threat Detection and Response with Machine Learning

Cybersecurity threats are malicious activities or events that pose risks to the confidentiality, integrity, and availability of digital information systems, networks, and data. These threats can encompass a wide range of actions conducted by cybercriminals, hackers, or even insiders with malicious intent. Understanding these threats is crucial in safeguarding digital assets and maintaining the trust and reliability of modern information technology. In the rapidly evolving landscape of cybersecurity, the relentless growth of cyber threats poses a formidable challenge to organizations worldwide. To combat these threats effectively, there is an increasing reliance on Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper explores the integration of AI and ML into cybersecurity for threat detection and response, shedding light on the transformative impact of these technologies. AI (Artificial Intelligence) and ML (Machine Learning) have the potential to both bolster cybersecurity defences and, paradoxically, facilitate cyberattacks. On the defensive side, AI and ML technologies enhance threat detection and response, allowing organizations to identify and mitigate threats more efficiently. They can analyse vast amounts of data in real-time, spot anomalies, and recognize patterns indicative of potential cyberattacks. However, cybercriminals are also harnessing the power of AI and ML to perpetrate more sophisticated and targeted attacks. Ethical considerations surrounding AI in cybersecurity, including privacy concerns and responsible AI implementation, are also discussed to ensure a balanced and secure approach. The paper underscores the transformative impact of AI and ML in bolstering cybersecurity practices. It advocates for the integration of AI as an indispensable tool to fortify organizations against the ever-evolving landscape of cyber threats, ultimately enhancing their ability to detect, respond to, and mitigate potential breaches.

Artificial Intelligence

As we navigate the digital era of the 21st century, cyber security has grown into a pressing societal issue that requires innovative, cutting-edge solutions. In response to this pressing need, Artificial Intelligence (AI) has emerged as a revolutionary instrument, causing a paradigm shift in cyber security. AI's prowess resides in its capacity to process and analyze immense quantities of heterogeneous cyber security data, thereby facilitating the efficient completion of crucial tasks. These duties, which include threat detection, asset prioritization, and vulnerability management, are performed with a level of speed and accuracy that far exceeds human capabilities, thereby transforming our approach to cyber security. This document provides a comprehensive dissection of AI's profound impact on cyber security, as well as an in-depth analysis of how AI tools not only augment, but in many cases transcend human-mediated processes. By delving into the complexities of AI implementation within the realm of cyber security, we demonstrate the potential for AI to effectively anticipate, identify, and preempt cyber threats, empowering organizations to take a proactive stance towards digital safety. Despite these advancements, it is essential to consider the inherent limitations of AI. We emphasize the need for sustained human oversight and intervention to ensure that cyber security measures are proportionate and effective. Importantly, we address potential ethical concerns and emphasize the significance of robust governance structures for the responsible and transparent use of artificial intelligence in cyber security. This paper clarifies the transformative role of AI in reshaping cyber security strategies, thereby contributing to a safer, more secure digital future. In doing so, it sets the groundwork for further exploration and discussion on the use of AI in cyber security, a discussion that is becoming increasingly important as we continue to move deeper into the digital age.

Keystroke Biometric Data for Identity Verification: Performance Analysis of Machine Learning Algorithms

Cyber-attacks are on the rise in today's environment, where traditional security measures are ineffective. As a result, the adoption of cutting-edge tools such as artificial intelligence technology is critical in the fight against cyber threats. User behaviors, such as keyboard dynamics, provide potential data that can be used for protection against cyber-attacks. Keystroke dynamics is one of the fastest and most cost-effective methods that can be used to detect user behaviors, as it can be captured using standard user keyboards. The analysis of this data and protection against cyber-attacks is made possible through machine learning algorithms. Based on keyboard dynamics, this study analyzes the performance of k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Neural Network (NN) methods for user behavior analysis and anomaly detection. The findings shed light on the significance of artificial intelligence in cyber security by examining the accomplishments of several machine learning algorithms. The study's findings may serve as a foundation for future research and novel solutions in the realm of cyber security.

Artificial Intelligence In Cyber Security: Rescue Or Challenge

Objective: The objective of this research paper is to explore the potential of artificial intelligence (AI) in enhancing and fortifying cybersecurity measures. In light of the increasing cyber threats and the limitations of traditional computer security systems, the paper aims to shed light on the concept of AI and its components. The central goal is to demonstrate how AI can be leveraged to improve cybersecurity, focusing on key areas such as machine learning, data mining, in-depth learning, and expert programs.
 Method: This research paper utilizes a literature review approach to investigate the intersection of artificial intelligence and cybersecurity. It involves analyzing existing scholarly works and studies to understand the challenges posed by evolving cyber threats and the limitations of conventional security systems. The paper identifies various components of AI that have the potential to enhance cybersecurity measures and delineates their applicability in the digital landscape.
 Results: The analysis of past literature emphasizes the growing significance of adopting artificial intelligence techniques to bolster cybersecurity practices. The results underscore the inadequacies of traditional security systems in predicting and countering the ever-evolving tactics of cyber attackers and terrorists. Through an examination of various AI components, including machine learning, data mining, in-depth learning, and expert programs, the paper demonstrates the potential benefits that these technologies can offer to the field of cybersecurity.
 Conclusions: In conclusion, this research paper highlights the escalating threats to cybersecurity in the face of rapid technological advancement. The imperative of addressing these challenges through AI becomes evident as traditional security systems prove insufficient. By harnessing the power of artificial intelligence techniques such as machine learning, data mining, in-depth learning, and expert programs, institutions can significantly enhance their cybersecurity capabilities. The paper underscores the need for a paradigm shift towards AI-driven cybersecurity strategies to effectively predict, prevent, and counteract cyber threats in the digital age.

Smart Energy Meets Smart Security: A Comprehensive Review of AI Applications in Cybersecurity for Renewable Energy Systems

The rapid adoption of renewable energy systems has brought forth a new set of cybersecurity challenges that require innovative solutions. In this context, artificial intelligence (AI) has emerged as a promising approach to tackle these challenges. This paper provides a comprehensive review of more than 19 studies that investigate the applications of AI in cybersecurity for renewable energy systems. By analyzing these studies, a range of opportunities and challenges associated with the integration of AI in this domain are identified. Notably, the findings indicate that over 75% of the studies acknowledge the significant potential of AI in enhancing the security of renewable energy systems. Among the various AI techniques employed, machine learning emerges as the most extensively utilized method, demonstrating an impressive detection rate of 85% and a false positive rate below 5%. However, certain challenges persist, including the limited availability of relevant data and concerns regarding the interpretability of AI models. To address these challenges, this paper concludes by providing recommendations for future research directions in this field, aiming to drive advancements in the intersection of smart energy and smart security.

Cyber Kill Chain Analysis Using Artificial Intelligence

Artificial Intelligence (AI) tools are promising multifaceted techniques for addressing the most mundane tasks for greater efficiency and high productivity. Cyber security space is one of the areas that AI is promising to revolutionize. This study will develop a conceptual and theoretical framework to support a research design that can simulate research in understanding how AI can be applied to Cyber Kill Chain phases. This study has reviewed 21 journal and conference articles mostly from IEEE Xplore database. An overview of the application of artificial intelligence (AI) in cybersecurity, particularly within the framework of the Cyber Kill Chain was provided in this study. It also emphasizes the limitations of traditional security approaches and the necessity for innovative and intelligent defense methodologies. The results of reviewing the relevant literatures discovered that the key components of cybersecurity, includes identity, asset management, automated configuration management, security control validation, governance, risk assessment, and vulnerability identification. A theoretical framework was developed which introduces the Cyber Kill Chain model with a Unified Kill Chain model to address its shortcomings. Application of AI in cybersecurity offers an optimistic solutions to address the evolving threat landscape. AI techniques, such as machine learning, anomaly detection, and behavioural analysis, have shown great potential in enhancing various aspects of cybersecurity. However, challenges related to data quality, adversarial attacks, and privacy concerns need to be addressed for successful implementation. Further research and development are crucial to fully harness the power of AI in cybersecurity and stay ahead of evolving cyber threats.

The role of Artificial Intelligence in Cyber Security and Incident Response

The escalating number and intricacy of cyber attacks have underscored the urgent requirement for innovative solutions to bolster the security of digital infrastructure. Among these solutions, Artificial Intelligence (AI) emerges as a promising technology with the potential to significantly enhance cyber security and incident response. It has the aptitude to improve the speed and accuracy of threat detection, response and mitigation while also reducing the workload on security professionals. This research paper focuses on the role of AI in key areas of cyber security and incident response, specifically vulnerability assessment, intrusion detection and prevention, and digital forensics analysis. It elucidates how AI, with its innate capabilities, can be a game-changer by empowering organizations to detect, respond to, and mitigate threats more effectively. However, AI is not a silver bullet for Cyber Security. Like any technology, it possesses its limitations and potential vulnerabilities. Consequently, this paper also addresses the need for ongoing development in the field of AI to overcome these limitations and challenges. By recognizing the need for continuous advancements, the research paper emphasizes the importance of future research and development efforts to maximize the potential benefits of AI in the realm of cyber security.

AI-based quantum-safe cybersecurity automation and orchestration for edge intelligence in future networks

The AIQUSEC (AI-based quantum secure cyber security automation and orchestration in the edge intelligence of future networks) brings measurable advances to the cyber security of access and edge networks and their services, as well as Operational Service Technologies (OT). The research aims for significant cybersecurity scalability, efficiency, and effectiveness of operations through improved and enhanced device and sensor securities, security assurance, quantum security, and Artificial Intelligence (AI) based automation solutions. The new application scenarios of near future, the multiple stakeholders within each scenario, and the higher data volumes raise the need for novel cybersecurity solutions. Recently, OT cybersecurity threat landscape has become wider, due to the increase digitalization of services, the increase in virtualization and slicing of networks, as well as the increase in advanced cyber-attacks. Because of recent advances in computing power, AI in cybersecurity analyzing and validations is now becoming a reality. A significant part of currently used encryption technologies which secures communications and infrastructures might become instantly penetrable when quantum computing becomes available. Enabling quantum-safety migration development is a clear goal to the project. The research develops a state-of-the-art information security verification and validation environment that supports the integration of cyber security systems as a reference model, focusing on architectural choices and network connection from different vertical use cases. With the help of the platform and the reference model, common cybersecurity capabilities and requirements can be built, tested, and validated, as well as their fulfillment. In addition to the environment mentioned above, the results of the research are demonstrated and utilized in critical communication systems, water utilities, industrial environments, in physical access solutions and remote work. The developed platform can also be used for auditing devices, systems, and software’s in the future. The research integrates new quantum-safe artificial intelligence-based, hardware-hardened, and scalable cybersecurity solutions that have been validated in a standardized way. In this research, we also deal with the requirements of the EU sustainable growth program - issues related to the green transition.

Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.

A Malware Detection and Extraction Method for the Related Information Using the ViT Attention Mechanism on Android Operating System

Artificial intelligence (AI) is increasingly being utilized in cybersecurity, particularly for detecting malicious applications. However, the black-box nature of AI models presents a significant challenge. This lack of transparency makes it difficult to understand and trust the results. In order to address this, it is necessary to incorporate explainability into the detection model. There is insufficient research to provide reasons why applications are detected as malicious or explain their behavior. In this paper, we propose a method of a Vision Transformer(ViT)-based malware detection model and malicious behavior extraction using an attention map to achieve high detection accuracy and high interpretability. Malware detection uses a ViT-based model, which takes an image as input. ViT offers a significant advantage for image detection tasks by leveraging attention mechanisms, enabling robust interpretation and understanding of the intricate patterns within the images. The image is converted from an application. An attention map is generated with attention values generated during the detection process. The attention map is used to identify factors that the model deems important. Class and method names are extracted and provided based on the identified factors. The performance of the detection was validated using real-world datasets. The malware detection accuracy was 80.27%, which is a high level of accuracy compared to other models used for image-based malware detection. The interpretability was measured in the same way as the F1-score, resulting in an interpretability score of 0.70. This score is superior to existing interpretable machine learning (ML)-based methods, such as Drebin, LIME, and XMal. By analyzing malicious applications, we also confirmed that the extracted classes and methods are related to malicious behavior. With the proposed method, security experts can understand the reason behind the model’s detection and the behavior of malicious applications. Given the growing importance of explainable artificial intelligence in cybersecurity, this method is expected to make a significant contribution to this field.

A REVIEW ON ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREVENTING CYBER THREATS

Artificial intelligence advancements in the present day are proving to be the most effective method for preventing cyberattacks. Artificial intelligence (AI) is being used by experts as a protection against cyberattacks. This technology is being used by security analysts to spot abnormalities, which reduces time and reduces total company expenditures. With the proliferation of IoT and linked devices in today's digital world, cyber security professionals are constantly confronted with new difficulties. The expert needs all available tools to stop attacks, discover security problems, and respond to attacks. The greater standard of technology in today's environment is strongly correlated with the rise in cybersecurity risks. The digitalization of organizations makes it essential to focus on cybersecurity issues and ways to make them better. Using artificial intelligence in cybersecurity is essential to improve data protection because traditional computer algorithms may occasionally be inadequate to manage all cyber threats. A descriptiveanalytical approach from previous research on the application of AI in cybersecurity is used. This paper aims to emphasise artificial intelligence and the concepts of cybersecurity that can be developed to improve the security of the data. Methods based on artificial intelligence can enhance the overall security operation of traditional security systems and give higher security against an increasing array of complicated cyber threats, when traditional security technologies may be incompetent or ineffective. In this study, we look at how human reasoning and artificial intelligence (AI) can be leveraged to improve cyber security. This study's primary goal is to showcase current developments in the use of AI approaches to defend against cyberattacks.

Artificial Intelligence in Cybersecurity: A Survey of National Research, Investment and Policy Implementation

Artificial Intelligence in Cybersecurity: A Survey of National Research, Investment and Policy Implementation

Artificial intelligence for cybersecurity: Literature review and future research directions

Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the security posture against various security issues and cyberattacks. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review resulted in 2395 studies, of which 236 were identified as primary. This article classifies the identified AI use cases based on a NIST cybersecurity framework using a thematic analysis approach. This classification framework will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in different contexts. The review also identifies future research opportunities in emerging cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for the successful adoption of AI-based cybersecurity in today's era of digital transformation and polycrisis.

From Bombs to Bytes: Role of Cyber Security in National Security and Importance of Artificial Intelligence in Cyber Security

Bu çalışma, devletin siber ortamda depoladığı veri miktarının artması ve kamu hizmetlerinin giderek daha fazla siber ortam aracılığıyla sunulması nedeniyle siber güvenliğe; siber güvenliğin sağlanmasında ise gerekli hız, çeviklik ve esnekliği sağlayan bir araç olarak görülen yapan zekâya odaklanmaktadır. Çalışmanın amacı ise siber güvenliğin neden bir ulusal güvenlik bileşeni olduğunun ve yapay zekânın siber güvenlikteki öneminin araştırılması, siber güvenlik alanında ülke uygulamalarından örnekler sunarak bunlardan çıkarımlar yapılmasıdır. Literatür taraması yapılarak hazırlanan bu çalışma sonucunda kamu sektörünün (devletin) siber saldırılardan en çok etkilenen alan olduğu; siber saldırıların askeri gücün operasyon sahasına yansıtılmasını etkileyebileceği; siber saldırıların ve siber ortama artan bağımlılığın toplum ve devleti bir arada tutan “güven” duygusunu baltalayabileceği; günümüzde siber saldırılarda yapay zekâ kullanımının kısıtlı olduğu ancak gelecekte kullanımının artacağı öngörüsü nedeniyle geleceğin siber güvenliğinin yapay zekâ odaklı hale geleceği gibi hususların önem taşıdığı görülmüştür. Buradan hareketle siber saldırıların ulusal güvenliği tehdit edebilecek potansiyelinin bulunduğu ve siber güvenliğin bir ulusal güvenlik bileşeni olduğu anlaşılmaktadır. Çalışmanın sonuç bölümünde de ülkelerin siber güvenlik uygulamalarından hareketle siber güvenliğin sağlanmasına yönelik önerilere yer verilmiştir.

ARTIFICIAL INTELLIGENCE AND CYBERSECURITY

The article introduces the concept of artificial intelligence and identifies its main applications inthe field of cybersecurity. The main problems of scale, context, precision, accuracy, and speed aredescribed. The types of problems solved by machine learning are identified. Attention was paid tothe risks and limitations of using artificial intelligence in cybersecurity and the problems ofimplementing it in an organisation.

Survey on the role of artificial intelligence in cyber security

The survey focuses on the role of Artificial Intelligence (AI) in enhancing cyber security, as well as the challenges that come with it. AI has become a crucial component in addressing the increasing sophistication of cyber-attacks and malware. Implementing intelligent cyber security measures, especially in support, situation awareness, and data management domains, is necessary for optimal effectiveness. Professional system technology is a promising technology in areas where neural networks may not be the most suitable technology. While the timeline for universal computing advances is unclear, it is important to note that attackers can potentially use such technology as soon as it becomes available. Therefore, advancements in machine learning, knowledge understanding, illustration, and handling can significantly enhance a system's capacity for cyber security. This article provides a comprehensive review of AI and computer applications in cyber security and offers recommendations for improving security system intelligence.

AI in cyber security

Artificial Intelligence (AI) is revolutionizing the field of cyber security, enabling organizations to better detect and respond to threats in real-time. AI-powered cyber security solutions leverage machine learning and other advanced techniques to analyze vast amounts of data and identify anomalies and patterns that might indicate an attack. This paper explores the various ways in which AI is being used in cyber security, including threat detection, incident response, security analytics, and more. It also discusses the benefits and challenges of using AI in cyber security and highlights some of the latest trends and developments in this rapidly evolving field.

Preventing cyber attacks using artificial intelligence

This paper explores the utilization of artificial intelligence (AI) in preventing cyber attacks across various domains of cybersecurity. It discusses different applications of AI in cybersecurity, including intrusion detection and prevention, threat hunting, vulnerability assessment, and fraud detection. Various AI techniques, such as anomaly detection, machine learning, natural language processing, and predictive analysis, are highlighted within each application area. The paper examines specific AI-driven cyber-attack methods such as phishing attacks, voice impersonation, and the use of chatbots. The study concludes with a comparative analysis of intrusion detection methods, highlighting the effectiveness of AI-driven approaches in identifying intruders and instances of attacks, especially those with unique characteristics. The paper underscores the importance of using AI in cybersecurity defense strategies to combat evolving cyber threats effectively.