Intrusion Detection using Nature‐Inspired Algorithms and Automated Machine Learning
Vasudev Awatramani, Pooja Gupta · 2021
With the surge of awareness and strategies against threats in space of the modern digital environment, machine learning is rapidly coming up as a fitting solution to many of the malign threats. Such systems are proficient to detect whether an application is under attack from a malicious entity. In this work, the methodology focuses on building an Intelligent Intrusion Detection System utilizing a blend of Nature Inspired Heuristics and Automated Machine Learning. The study applies Evolutionary Algorithms for feature selection as well as Hyperparameter Optimization. Moreover, the research explores Bayesian Search for Neural Architecture Search to estimate the ideal architecture of an artificial neural network (ANN). By employing the following techniques, the work achieves a near state of the detection rate of 98.5%.