Leveraging Spatiotemporal Patterns for Cyber Attack Detection in Distributed System Using Machine Learning
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Leveraging Spatiotemporal Patterns for Cyber Attack Detection in Distributed System Using Machine Learning
G.MANOJ KUMAR, KANDULA HEMAPRIYA
Assistant Professor, 2MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India
Abstract
Cyber-crime is proliferating everywhere exploiting every kind of vulnerability to the 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 cyber security community. Most techniques used in today’s IDS are not able to deal with the dynamic and complex nature of cyber-attacks on computer networks. Machine learning for cyber security has become an issue of great importance recently due to the effectiveness of machine learning in cyber security issues. Machine learning techniques have been applied for major challenges in cyber security issues like intrusion detection, malware classification and detection, spam detection and phishing detection. Although machine learning cannot automate a complete cyber security system, it helps to identify cyber security threats more efficiently than other software-oriented methodologies, and thus reduces the burden on security analysts. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. Our main goal is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.
IndexTerms: Cyber-crime,Machine Learning,Cyber-security,Intrusion DetectionSystem,SpatiotemporalPatterns,AnomalyDetection,Support Vector Machine (SVM),Distributed Systems.
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