# Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers

arXiv: https://arxiv.org/abs/2409.00667 · HTML: https://mlsanity.com/paper/2409.00667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors](https://mlsanity.com/paper/2005.07519/) (2020)
- [Adversarial Machine Learning in Network Intrusion Detection Systems](https://mlsanity.com/paper/2004.11898/) (2020)
- [Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.06664/) (2023)
- [Adversarial Network Traffic: Towards Evaluating the Robustness of Deep Learning-Based Network Traffic Classification](https://mlsanity.com/paper/2003.01261/) (2020)
- [Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System](https://mlsanity.com/paper/1710.04843/) (2017)
- [Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System](https://mlsanity.com/paper/2310.03334/) (2023)
- [DI-NIDS: Domain Invariant Network Intrusion Detection System](https://mlsanity.com/paper/2210.08252/) (2022)
- [TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2210.15700/) (2022)
- [Business Model of a Botnet](https://mlsanity.com/paper/1804.10848/) (2018)
- [Efficient Detection of Botnet Traffic by features selection and Decision Trees](https://mlsanity.com/paper/2107.02896/) (2021)
- [A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks](https://mlsanity.com/paper/2308.00077/) (2023)

## Built on by (most cited first, 2 of 2)

- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [Reliable Hierarchical Operating System Fingerprinting via Conformal Prediction](https://mlsanity.com/paper/2602.12825/) (2026)

## Abstract (arXiv)

Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. They autonomously infect, propagate, and coordinate to conduct cybercrimes, necessitating robust detection methods. This research addresses the sophisticated adversarial manipulations posed by attackers, aiming to undermine machine learning-based botnet detection systems. We introduce a flow-based detection approach, leveraging machine learning and deep learning algorithms trained on the ISCX and ISOT datasets. The detection algorithms are optimized using the Genetic Algorithm and Particle Swarm Optimization to obtain a baseline detection method. The Carlini & Wagner (C&W) attack and Generative Adversarial Network (GAN) generate deceptive data with subtle perturbations, targeting each feature used for classification while preserving their semantic and syntactic relationships, which ensures that the adversarial samples retain meaningfulness and realism. An in-depth analysis of the required L2 distance from the original sample for the malware sample to misclassify is performed across various iteration checkpoints, showing different levels of misclassification at different L2 distances of the Pertrub sample from the original sample. Our work delves into the vulnerability of various models, examining the transferability of adversarial examples from a Neural Network surrogate model to Tree-based algorithms. Subsequently, models that initially misclassified the perturbed samples are retrained, enhancing their resilience and detection capabilities. In the final phase, a conformal prediction layer is integrated, significantly rejecting incorrect predictions, of 58.20 % in the ISCX dataset and 98.94 % in the ISOT dataset.