VISUALISASI SERANGAN GERAKAN LATERAL (LATERAL MOVEMENT ATTACK) DENGAN MENERAPKAN METODE K-MEANS CLUSTERING

SEPTIANI, KUSUMA NINGRUM and Deris, Stiawan and NURUL, AFIFAH (2024) VISUALISASI SERANGAN GERAKAN LATERAL (LATERAL MOVEMENT ATTACK) DENGAN MENERAPKAN METODE K-MEANS CLUSTERING. Undergraduate thesis, Sriwijaya University.

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Abstract

The lateral movement is one of the most crucial phases in an Advanced Persistent Threat (APT) attack, aiming to penetrate other resources and gain greater privileges within the target network. Attackers typically exploit social engineering techniques (such as phishing, pretexting, baiting) to deceive individuals within the network into running malicious code or surrendering credentials. This enables the attacker to gain access to the victim's computer and gradually seek valuable information by exploiting vulnerabilities in other intranet entities. Utilizing the K-means method for clustering benign and malicious activities, combined with Principal Component Analysis (PCA), this approach delivers good performance in visualizing laterally moving attacks. Combining the four datasets, connection dataset, files dataset, DNS dataset, and HTTP dataset, can provide a clear visualization to illustrate differences based on user activities. Through this data integration, it fosters a better understanding of normal and malicious activities within the network. The use of validation methods like the Elbow method proves the existence of a clear elbow point, making it easy to identify the optimal clusters. On the other hand, graphs lacking a clear elbow point might provide unreliable assessments. Hence, this research indicates that using the silhouette method is an effective evaluation technique for measuring the quality of an ideal cluster.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Lateral Movement, K-Means Clustering, Principal Component Analysis
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.A25 Computer security. Systems and Data Security.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Septiani Kusuma Ningrum
Date Deposited:	12 Jan 2024 03:32
Last Modified:	12 Jan 2024 03:32
URI:	http://repository.unsri.ac.id/id/eprint/138013

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