Autonomous Cyber Defence Agent for Intelligent Threat Detection and Adaptive Response in Network Security

Abstract

As attacks evolve in both frequency and sophistication, traditional security systems have proven ineffective in handling the threats because they are very heavily signature and manual dependent. In today's network environment, intelligent defense systems that can detect threats that are changing and reacting quickly in real time are necessary. This paper introduces an Autonomous Cyber Defense Agent (ACDA) which combines a deep learning network for threat detection with automated response mechanisms to improve the security of a network. The proposed framework is based on hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) that is used to analyze the network traffic pattern and detect malicious activities. The CNN module learns spatial features from the network traffic and the LSTM module learns the temporal attack behaviors. If an attack is detected, the autonomous agent assesses the threat and performs preprogrammed mitigation operations like generation of alerts, host isolation, and IP blocking. The experimental results on the benchmark intrusion datasets show that the proposed framework has high accuracy in detecting intrusions, while significantly saving in incident response time. The findings show that the combination of intelligent detection and autonomous response enhances cyber resilience and helps to proactively defend against new cyber threats