Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

Abstract

Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious network traffic, several IDS based on machine learning (ML) methods have been developed in the literature. Machine learning models, on the other hand, have recently been proved to be effective, since they are vulnerable to adversarial perturbations, which allows the opponent to crash the system while performing network queries. This motivated us to present a defensive model that uses adversarial training based on generative adversarial networks (GANs) as a defense strategy to offer better protection for the system against adversarial perturbations. The experiment was carried out using random forest as a classifier. In addition, both principal component analysis (PCA) and recursive features elimination (Rfe) techniques were leveraged as a feature selection to diminish the dimensionality of the dataset, and this led to enhancing the performance of the model significantly. The proposal was tested on a realistic and recent public network dataset: CSE-CICIDS2018. The simulation results showed that GAN-based adversarial training enhanced the resilience of the IDS model and mitigated the severity of the black-box attack.

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

“…In Table 1, the attacks are listed. To more accurately represent the attacks, a network infrastructure with machine diversity similar to real-world networks was developed (five departments making up the victim organisation, with fifty attacker machines, four hundred victim machines, and thirty servers) [3,2]. The dataset includes 80 features that CICFlowMeter-V3 retrieved from the traffic, together with forward and backward collected system logs and network traffic for each machine [33].…”

“…The output gate manages the distribution of the most recent data to other networks. It is denoted by the symbol OG and the output gate's value at time instance t is expressed in Equation (3).…”

“…2023, Brno, Czech RepublicX Institute for Cybersecurity, both of which have their headquarters in Fredericton, Canada[?]. The most recent intrusion dataset, acquired to conduct real attacks, is the CSE-CIC-IDS2018[3]. It is available to the public.…”

Hybrid Chaotic Zebra Optimization Algorithm and Long Short-Term Memory for Cyber Threats Detection

“…The CSE-CIC-IDS2018 intrusion detection dataset was created in 2018 by the Communications Security Establishment and the Canadian Institute for Cybersecurity, both of which have their headquarters in Fredericton, Canada ( [52]; [53]). It is the most recent intrusion dataset, acquired to conduct real attacks [54]. It is available to the public.…”

“…Common attacks are listed in Table 5. To more accurately represent the attacks, a network infrastruc- ture with machine diversity similar to real-world networks was developed (five departments making up the victim organization, with fifty attacker machines, four hundred victim machines, and thirty servers) ( [54]; [3]).…”