AI AND MACHINE LEARNING FOR NETWORK SECURITY: APPLICATIONS AND CASE STUDIES (original) (raw)

AI and Cyber-Security: Enhancing threat detection and response with machine learning

2024

As cyber threats continue to evolve and become more sophisticated, traditional security measures are no longer sufficient to protect networks and sensitive data. Artificial intelligence (AI) and machine learning (ML) techniques offer powerful tools to enhance cyber security by enabling more effective and efficient threat detection and response. This paper provides an overview of the current state of AI and ML in cyber security, discussing key techniques, applications, challenges, and future directions. We review ML algorithms used for tasks such as anomaly detection, malware classification, and network intrusion detection. Case studies are presented showing the successful implementation of AI/ML in real-world cyber security systems. Limitations and challenges are also discussed, including the need for large labelled datasets, adversarial attacks on ML models, and the difficulty of interpreting black-box ML models. Finally, we highlight promising research directions, such as explainable AI for cyber security, unsupervised learning approaches, and the integration of ML with other security tools and frameworks. AI and ML will play an increasingly crucial role in cyber security going forward, and ongoing research will help unlock their full potential for safeguarding our digital infrastructure.

Research Paper 3 Application of Artificial Intelligence in Detecting and Mitigating Cyber Threats (1)

International Research Journal of Innovations in Engineering and Technology (IRJIET), 2025

The integration of Artificial Intelligence (AI) into cybersecurity has revolutionized the detection and mitigation of cyber threats, addressing the growing complexity and sophistication of attacks. This study explores AI's effectiveness in identifying threats such as malware, phishing, and zero-day vulnerabilities while automating threat responses and enhancing proactive defense mechanisms. It highlights key challenges, including adversarial attacks, data quality issues, algorithmic biases, and integration complexities with legacy systems. Emerging technologies such as federated learning, blockchain, and edge computing offer promising solutions to overcome these barriers. Ethical and regulatory considerations are also addressed, emphasizing the need for responsible AI adoption in cybersecurity. The findings underscore AI's transformative potential in cybersecurity and provide actionable recommendations for its effective implementation. The study concludes that while AI presents significant advantages, addressing its limitations through interdisciplinary collaboration and continuous innovation is critical to maximizing its impact.

Artificial Intelligence with Respect to Cyber Security

PrePrints, 2023

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats. Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy. AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

A Literature Review on Machine Learning for Cyber Security Issues

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Through the use of relevant data to build an algorithm, machine learning primarily aims to automate human help. A subset of artificial intelligence (AI), machine learning (ML) focuses on the development of systems that can learn from past data, recognize patterns, and reach logical conclusions with little to no human involvement. The concept of cyber security involves guarding against hostile attack on digital systems such computers, servers, mobile devices, networks, and the data they are connected to. Accounting for cyber security where machine learning is used and using machine learning to enable cyber security are the two main components of combining cyber security and ML. We may benefit from this union in a number of ways, including by giving machine learning models better security, enhancing the effectiveness of cyber security techniques, and supporting the efficient detection of zero day threats with minimal human involvement. In this review paper, we combine ML and cyber security to talk about two distinct notions. We also talk about the benefits, problems, and difficulties of combining ML and cyber security. In addition, we explore several attacks and present a thorough analysis of various tactics in two different categories. Finally, we offer a few suggestions for future research.

Redefining Cybersecurity with AI and Machine Learning

2019

In the age of digital transformation with adoption of Cloud and mobile computing and ever-increasing Internet of Things(IoT) devices, the cybersecurity risks and threat levels are increasing at a rapid pace. The data is spread across systems, devices and cloud leading to growing attack surface and increased frequency of the security attacks. IoT is extended to drones, driver-less cars, industrial equipment, smart buildings, consumer goods, home appliances leaving us with more vulnerable attack points. Organizations needs to have effective information security management system (ISMS) in place to proactively detect, react to security threats with reduced time to discover any potential breach. This paper highlights how Artificial Intelligence(AI) and Machine Learning(ML) can redefine cybersecurity to detect, prevent organizations from security threats and data breaches.

A Survey of Artificial Intelligence in Cyber Security

International Journal of Computer Applications Technology and Research, 2022

Artificial intelligence (AI) and cyber security are two emerging technologies in the modern world. Machine learning (ML) models serve as the cornerstone of AI. Access control, user authentication, user behaviour analysis, spam, malware, and botnet identification are all areas where AI is crucial. The security issues of today, however, are many. Users now face substantial security threats due to the increasing use of apps like WhatsApp and Viber, social media, mobile devices, cloud computing, and social media. We will explain how artificial intelligence (AI) can be used to handle cyber security concerns and cyber threats in this essay. Since the past decade, the field of cyber security has expanded significantly. Thus, both the number of applications and the number of risks are continuously increasing. Artificial intelligence's applications in cyber security are covered in this essay. With a primary focus on studies between 2018 and 2022, the study technique involved online desk research.

Handling Network Security Issues Using AI

With excessive exposure and use of internet, every system is prone to wide range of attacks. With this perspective, Network security is gathering every ones' concern. Network attackers are getting more and more innovative with designs. Exponential growth in attacker's intelligence require mitigation mechanism to be updated with same speed and level of innovation. This pans avenues for advanced technologies and methods to be employed for handling network security issues. Trending ones are artificial intelligence(AI), machine learning (ML) and deep learning(DL). AI is an umbrella covering ML and DL under it. Main features of AI making it big deal for network security are capacity to handle big data, faster detection, quick response, ability of unsupervised processing. Considering mentioned aspects, comprehensive study of AI approaches in handling network security issues is been carried out.

DEFENDING THE CLOUD: HOW AI AND ML ARE REVOLUTIONIZING CYBERSECURITY

Journal of Research Administration, 2019

This study explores how machine learning (ML) and artificial intelligence (AI) might strengthen cybersecurity defenses in cloud computing environments. The sophistication and reach of cyber threats are growing, making traditional security methods inadequate. However, proactive threat detection, quick response times, and flexible defenses are made possible by AI and ML, which present a viable answer. Through the examination of large datasets, these technologies are able to identify irregularities and trends that may point to future attacks, allowing for the mitigation of risks in advance. This research highlights how AI and ML are changing cybersecurity techniques and how they can help make cloud-based security infrastructures more resilient and effective. It also covers how AI and ML may improve cloud security by giving businesses the ability to proactively identify, counter, and respond to new and emerging cyberthreats. This allows them to take advantage of cloud computing's flexibility and scalability while maintaining strong security protocols.

Machine learning to identify Potential Cyber Security threats First author --Preeti Jain

isara solutions, 2020

Cyber crime is proliferating everywhere exploiting every kind of vulnerability to computing environment. Ethical Hackers pay more attention towards assessing vulnerabilities and recommending mitigation methodologies. The development of effective techniques has been an urgent demand in the field of the cybersecurity community. Machine Learning for cybersecurity has become an issue of great importance recently due to th e effectiveness of machine learning and deep learning in cybersecurity issues. Machine learning techniques have been applied for ma jor challenges in cybersecurity issues like intrusion detection, malware classification and detection, spam detection and phishing detection. Although machin e learning cannot automate a complete cybersecurity system, it helps to identify cyber-security threats more efficiently than other softwareoriented methodologies, and thus reduces the burden on security analysts. Ever evolving nature of cyber threats throws challenges continuously on the researchers to e xplore with the ideal combination of deep expertise in cybersecurity and in data science. In this paper, we present the current state of art machine learning applications and their potential for cybersecurity. An analysis of machine learning algorithms for most common types of cybersecurity threats is presented. In a computing context, cybersecurity is going through gigantic movements in innovation and its tasks as of late, and information science is driving the change. Extricating security occurrence examples or experiences from cybersecurity information and building relating information driven model, is the way to make a security framework mechanized and smart. To comprehend and examine the genuine marvels with information, different logical strategies, AI methods, cycles, and frameworks are utilized, which is ordinarily known as information science. In this paper, we zero in and quickly talk about on cybersecurity information science, where the information is being assembled from important cybersecurity sources, and the investigation supplement the most recent information driven examples for giving more powerful security arrangements. The idea of cybersecurity information science permits making the computing cycle more significant and shrewd when contrasted with conventional ones in the area of cybersecurity. We at that point examine and sum up various related examination issues and future bearings. Moreover, we give an AI based multi-layered structure with the end goal of cybersecurity demonstrating. Generally speaking, our objective isn't just to talk about cybersecurity information science and important techniques yet in addition to center the relevance towards information driven savvy dynamic for shielding the frameworks from digital assaults.

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision...