International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-005
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Multiple Cyber Attack Detection using Machine Learning Prof. P.M. Kamde *1, Rakesh Patil *2, Ganesh Kadam *3, Vedant Pokale *4, Shriram Narkhede *5 *1 Prof. Department of Comp. Eng. SCOE, Pune, India. *2,3,4,5 Student, Department of Comp. Eng. SCOE, Pune, India.
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Abstract - In the current landscape of cybersecurity, the efficacy of traditional single-layer defences has significantly diminished due to the relentless evolution of cyber threats. Attackers continuously refine their tactics, techniques, and procedures, rendering organizations and individuals vulnerable to increasingly sophisticated attacks. In response, there is a growing recognition of the necessity to implement multiple layers of defence to mitigate these risks effectively. This paper proposes a proactive and comprehensive approach termed "multiple cyber-attack detection." This approach entails the strategic integration of diverse tools, techniques, and strategies aimed at enhancing the detection and response capabilities against cyber threats. By adopting a multifaceted approach, organizations and individuals can better safeguard their digital assets and sensitive information from a myriad of potential threats. However, the success of such initiatives hinges upon the clear definition of project scope, encompassing goals, resources, and timelines. Additionally, there is a crucial need for adaptability to adjust the scope in alignment with the evolving threat landscape and the changing needs of the organization. This paper underscores the imperative of adopting a dynamic and flexible cybersecurity strategy to ensure robust protection against the ever-evolving cyber threat landscape. Key Words: Cybersecurity, Multi-layered defence, Cyber threats, Attack tactics, Threat detection. 1. INTRODUCTION Cyberattacks have become a ubiquitous and persistent threat in our increasingly digitized world. As organizations and individuals rely more on technology and interconnected systems, the potential for malicious actors to exploit vulnerabilities and compromise data and infrastructure has grown substantially. The digitization of critical infrastructure, the widespread adoption of cloud computing, and the proliferation of Internet-connected devices have expanded the attack surface, providing attackers with a plethora of opportunities to infiltrate systems and perpetrate cybercrimes. To counter this everevolving threat landscape, effective cyber-attack detection mechanisms have become paramount. Traditional cybersecurity approaches primarily focus on detecting and mitigating single, isolated attacks, such as malware infections or network intrusions. While these methods have
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proven effective to some extent, they often struggle to keep pace with the rapidly evolving tactics and techniques employed by cybercriminals. Attackers are continuously developing new strategies to evade detection, exploit vulnerabilities, and bypass traditional security measures, posing a significant In this research paper, we aim to explore the application of machine learning for cyberattack detection and mitigation. We will investigate how ML algorithms can be effectively deployed to detect a wide range of cyber threats, including malware, phishing attacks, insider threats, and advanced persistent threats (APTs). Additionally, we will examine the challenges and limitations of ML-based detection systems and propose strategies to overcome them. Our research seeks to contribute to the advancement of cybersecurity by developing more robust and adaptive mechanisms for detecting and responding to cyber-attacks. By leveraging the power of machine learning and data-driven analytics, we aim to enhance the resilience of organizations and individuals against the threat of cybercrime. 2. LITERATURE SURVEY I)
Prediction of Cyber Attacks using Machine Learning Algorithms: In response to the escalating threat of cyberattacks, this paper highlights the pressing need for effective detection and prevention strategies. It underscores the significant economic harm inflicted by cybercrime on individuals and nations alike. The importance of understanding attack tactics and identifying cybercrime perpetrators to combat this growing menace is emphasized. While artificial intelligence techniques have been increasingly employed to address these challenges, predicting cybercrime strategies remains a formidable task. The paper suggests leveraging actual data to pinpoint specific attacks and their perpetrators as a potential solution to this ongoing challenge.
II) Cyber-attack Detection in Network Traffic using Machine Learning: This research addresses the rampant proliferation of cyber threats as a consequence of the rapid expansion of internet usage by individuals, government sectors, and companies. With the transition towards online provision of services and products, vulnerabilities in network security have become increasingly exploitable by
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