Intrusion Detection System For Phising Detection Using Convolution Neural Network.

Abstract

Innovative security solutions utilizing cutting-edge machine learning techniques are essential to strengthen network defense as cyber threats become more sophisticated. This study proposes an intrusion detection system (IDS) that uses deep learning algorithms (DLAs), specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to automatically detect phishing attacks. Phishing circumvents traditional signature-based intrusion detection systems by using cunning social engineering techniques. CNNs enable the automatic extraction of sophisticated features from raw input data, including URLs and webpage content. Low-level patterns are recognized by their convolutional layers, and in later layers, these patterns are combined to create higher-level representations. Sequential data, such as user activities over time, is a great fit for RNN modeling. CNNs and RNNs work together to acquire the intricate multi-modal patterns that are characteristic of phishing. Back propagation-based model optimization enables real-time adaptation to identify emerging phishing variants. DLA integration with an IDS offers a strong defense against sophisticated user-targeted phishing attacks. Using the KDD-CUP99 dataset, which has 175,341 training and 82,332 testing instances, the DLA model is trained. Proactive incident response is made possible by automated feature learning by DLAs, which dramatically increases detection accuracy over manual rule-based techniques. This DLA-driven intrusion detection system research strengthens the overall security posture by improving resistance to changing social engineering threats. By utilizing machine learning, networks and users can be protected from sneaky phishing tactics through constant model refining for intelligent, adaptive threat identification as attack vectors change.