STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Data Structure Studies Review Article

Efficient Malware Detection in Cybersecurity: Leveraging Advanced Data Structures for Enhanced Threat Identification

  1. P. Devi Sravanthi Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem
  2. Manas Kumar Yogi Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem

Abstract

The cybersecurity landscape is constantly changing with more advanced malware creating major challenges for detection systems. To address these challenges effectively, advanced data structures have become essential in optimizing how data is managed, processed, and analyzed for malware detection. This review paper delves into the role of several cutting-edge data structures—bloom filters, tries, hash tables, graphs, decision trees, and suffix trees—in enhancing the efficiency and accuracy of malware detection mechanisms. Bloom filters offer rapid membership testing, crucial for initial checks of malware signatures with minimal memory usage. Tries facilitating efficient string searches, making them ideal for managing and matching malware signatures. Hash tables enable quick lookups of malware behavior profiles, supporting real-time detection. Graphs model malware propagation and network activity, aiding in identifying and mitigating threats. Decision trees classify files and activities based on learned features, enhancing classification accuracy. Suffix trees detect variants of known malware by analyzing common patterns. The paper provides a comprehensive examination of these structures, their applications, benefits, limitations, and potential advancements in improving malware detection systems.

Keywords

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