MALICIOUS WEB TRAFFIC DETECTION USING SPATIAL PYRAMID POOLING WITH DEEP LEARNING

Abstract

The widespread use of the internet has brought immense convenience, but it has also led to a rise in cyber crimes. Attackers are using various tactics and techniques to compromise the security of information systems. One of the major threats in this landscape is web attacks, which pose a serious threat to web applications. Extensive work has been done for web security through multiple detection and prevention tools at each layer of security. Tools like IDS, IPS, and SIEM solutions have been proposed to detect and prevent these attacks. These security solutions mainly rely on network traffic stats (flows), signatures, cyber threat intelligence (CTI), and static threat detection rules. These methods have protected web security, but there are some limitations observed toward advanced attack payloads that use sophisticated techniques, a limited number of attempts, and zero-day exploits. This research aims to identify malicious web traffic using an innovative approach that combines deep learning with spatial pyramid pooling (SPP) to detect attacks on the base of payloads in network traffic. Deep learning is a powerful tool for recognizing patterns and extracting features from images. The proposed method involves using image classification techniques to dynamically spot different types of web attacks on the fly. By converting both malicious and clean payloads into image formats, the model has been trained to classify these data into either malicious or clean categories. Additionally, SPP techniques have been used to adapt the model to varying sizes of images. This method will help to improve the efficiency of the model by avoiding information loss due to resizing and cropping images to a fixed size. This work automates the process of extracting meaningful features, eliminating the need for manual feature selection commonly used in traditional machine learning approaches. The proposed approach aims to provide a more effective defense against evolving web attacks.