Statistical Surveillance for Host-Based Intrusion Detection System (HIDS): An Intelligent System for Automation

The provided text appears to be an academic or technical manuscript discussing intrusion detection using machine learning algorithms and the KDD Cup 99 dataset. Below is a paraphrased version with some modifications to enhance clarity and style: This chapter explores the analysis of the KDD Cup 1999 dataset, focusing on the extraction of features and identification of anomalies and signature-based attacks. The primary objective is to develop a robust system for detecting both anomaly- and signature-based attacks within an organization. The methodology involves preprocessing the KDD dataset by extracting and cleaning data from 42 frameworks and 58 features. Functional characteristics are further refined, and attacks in the dataset are categorized into four groups: refusal of administration, network test, or remote to neighborhood. To assess the exactness of the proposed system, various machine learning algorithms are employed. The assessment includes measuring true positives and false positives for each algorithm in comparison with others. The emphasis of this work is on enhancing sensitivity to improve the system’s reliability in detecting and resisting attacks. While substantial progress has been made in the field of intrusion detection, achieving 100% reliability remains a challenge. Existing structures exhibit reliability flaws, and certain attack scenarios are yet to be thoroughly explored. The constant evolution of viruses and malware introduces daily challenges to system integrity. To address these challenges, a combination of dissimilar machine learning and deep neural network (DNN) algorithms is implemented. This integration, along with the use of a corporate workstation structure, aims to minimize inaccuracies. The feasibility of the proposed approach is demonstrated, recognizing and addressing the inaccuracies associated with various technologies to enhance correctness and overall system performance.