NETWORK-BASED INTRUSION DETECTION USING NEURAL NETWORKS

TLDR

The growing prevalence of computer networking, e‑commerce, and web services has made network security critical, as hacking can cripple companies and has spurred dedicated intrusion detection research. The study demonstrates that careful analysis of network data reveals attack evidence and that neural networks can efficiently detect such activity, exploring intrusion detection with SOM clustering and MLP neural networks. The authors evaluate their system on denial‑of‑service, distributed denial‑of‑service, and port‑scan attacks using self‑organizing maps for clustering and multilayer perceptrons for detection. Neural networks efficiently detect intrusion activity in network traffic.

Abstract

With the growth of computer networking, electronic commerce, and web services, security of networking systems has become very important. Many companies now rely on web services as a major source of revenue. Computer hacking poses significant problems to these companies, as distributed attacks can render their cyber-storefront inoperable for long periods of time. This happens so often, that an entire area of research, called Intrusion Detection, is devoted to detecting this activity. We show that evidence of many of these attacks can be found by a careful analysis of network data. We also illustrate that neural networks can efficiently detect this activity. We test our systems against denial of service attacks, distributed denial of service attacks, and portscans. In this work, we explore network based intrusion detection using classifying, self-organizing maps for data clustering and MLP neural networks for detection.