Development of Anomaly Infused Deep Learning Model for Cyber Threat

Abstract

The rapid growth of the internet and digital communication, network security has become a critical concern. Traditional Intrusion Detection Systems (IDS), especially rule-based or signature-based systems, are effective only against known attacks [1], [2]. However, they fail to detect novel or zero-day threats because they rely on predefined signatures [5], [7]. To overcome this limitation, anomaly detection techniques have gained importance. Anomaly-based systems monitor network traffic and identify deviations from normal behavior, which could indicate potential cyber-attacks [6]. This project focuses on Network Anomaly Detection using Deep Learning techniques [3]. The proposed system leverages models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), or Long Short-Term Memory (LSTM) networks to learn patterns of normal network traffic and accurately identify abnormal or malicious activities [4], [5]. Deep learning models are capable of handling large-scale, high-dimensional data, and can automatically extract complex features without manual intervention [3], [6]. The objective of this work is to design a robust, intelligent intrusion detection framework that can not only detect known threats but also adapt to new and evolving attack patterns. The system will be trained and validated using benchmark datasets like NSL-KDD or CICIDS2017 [1], [2]. Performance will be measured using metrics such as accuracy, precision, recall, and F1-score. By implementing deep learning-based anomaly detection, this project aims to enhance the efficiency of network security, minimize false positives, and provide a scalable solution for real-time intrusion detection in modern networks [5], [6], [7].