# Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks

arXiv: https://arxiv.org/abs/2209.12195 · HTML: https://mlsanity.com/paper/2209.12195/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 54 references that are in the map.
- First posted on arXiv 25 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Adversarial Examples: Opportunities and Challenges](https://mlsanity.com/paper/1809.04790/) (2018)
- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [A Survey of Machine Learning Techniques in Adversarial Image Forensics](https://mlsanity.com/paper/2010.09680/) (2020)
- [Autoencoder with recurrent neural networks for video forgery detection](https://mlsanity.com/paper/1708.08754/) (2017)
- [Demystifying the Transferability of Adversarial Attacks in Computer Networks](https://mlsanity.com/paper/2110.04488/) (2021)
- [CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing](https://mlsanity.com/paper/1805.11318/) (2018)
- [Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets](https://mlsanity.com/paper/1702.03681/) (2017)
- [An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework](https://mlsanity.com/paper/2204.13172/) (2022)
- [Improving the security of Image Manipulation Detection through One-and-a-half-class Multiple Classification](https://mlsanity.com/paper/1902.08446/) (2019)
- [Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples](https://mlsanity.com/paper/1910.12392/) (2019)

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

- [Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees](https://mlsanity.com/paper/2403.02995/) (2024)
- [Unscrambling the Rectification of Adversarial Attacks Transferability across Computer Networks](https://mlsanity.com/paper/2311.03373/) (2023)

## Abstract (arXiv)

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications including such computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks.Our architecture is referred to as the 1.5-Class (SPRITZ-1.5C) classifier and constructed using a final dense classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and SPRITZ-1.5C architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the SPRITZ-1.5C classifier.