Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks before they enter the system and classifying them as malicious activities. However, the IDS approaches have shortcomings in misclassifying novel attacks or adapting to emerging environments, affecting their accuracy and increasing false alarms. To solve this problem, researchers have recommended using machine learning approaches as engines for IDSs to increase their efficacy. Machine-learning techniques are supposed to automatically detect the main distinctions between normal and malicious data, even novel attacks, with high accuracy. However, carefully designed adversarial input perturbations during the training or testing phases can significantly affect their predictions and classifications. Adversarial machine learning (AML) poses many cybersecurity threats in numerous sectors that use machine-learning-based classification systems, such as deceiving IDS to misclassify network packets. Thus, this paper presents a survey of adversarial machine-learning strategies and defenses. It starts by highlighting various types of adversarial attacks that can affect the IDS and then presents the defense strategies to decrease or eliminate the influence of these attacks. Finally, the gaps in the existing literature and future research directions are presented.

/pdf/adversarial-machine-learning-attacks-against-intrusion-21rh51kb.pdf

Adversarial Machine Learning Attacks against Intrusion Detection Systems: A Survey on Strategies and Defense

Cyber-physical systems (CPS) are increasingly relying on machine learning (ML) techniques to reduce labor costs and improve efficiency. However, the adoption of ML also exposes CPS to potential adversarial ML attacks witnessed in the literature. Specifically, the increased Internet connectivity in CPS has resulted in a surge in the volume of data generation and communication frequency among devices, thereby expanding the attack surface and attack opportunities for ML adversaries. Among various adversarial ML attacks, evasion attacks are one of the most well-known ones. Therefore, this survey focuses on summarizing the latest research on evasion attack and defense techniques, to understand state-of-the-art ML model security in CPS. To assess the attack effectiveness, this survey proposes an attack taxonomy by introducing quantitative measures such as perturbation level and the number of modified features. Similarly, a defense taxonomy is introduced based on four perspectives demonstrating the defensive techniques from models’ inputs to their outputs. Furthermore, the survey identifies gaps and promising directions that researchers and practitioners can explore to address potential challenges and threats caused by evasion attacks and lays the groundwork for understanding and mitigating the attacks in CPS.

Evasion Attack and Defense On Machine Learning Models in Cyber-Physical Systems: A Survey

An intrusion detection system (IDS) is an effective tool for securing networks and a dependable technique for improving a user’s internet security. It informs the administration whenever strange conduct occurs. An IDS fundamentally depends on the classification of network packets as benign or attack. Moreover, IDSs can achieve better results when built with machine learning (ML)/deep learning (DL) techniques, such as convolutional neural networks (CNNs). However, there is a limitation when building a reliable IDS using ML/DL techniques, which is their vulnerability to adversarial attacks. Such attacks are crafted by attackers to compromise the ML/DL models, which affects their accuracy. Thus, this paper describes the construction of a sustainable IDS based on the CNN technique, and it presents a method for defense against adversarial attacks that enhances the IDS’s accuracy and ensures it is more reliable in performing classification. To achieve this goal, first, two IDS models with a convolutional neural network (CNN) were built to enhance the IDS accuracy. Second, seven adversarial attack scenarios were designed against the aforementioned CNN-based IDS models to test their reliability and efficiency. The experimental results show that the CNN-based IDS models achieved significant increases in the intrusion detection system accuracy of 97.51% and 95.43% compared with the scores before the adversarial scenarios were applied. Furthermore, it was revealed that the adversarial attacks caused the models’ accuracy to significantly decrease from one attack scenario to another. The Auto-PGD and BIM attacks had the strongest effect against the CNN-based IDS models, with accuracy drops of 2.92% and 3.46%, respectively. Third, this research applied the adversarial perturbation elimination with generative adversarial nets (APE_GAN++) defense method to enhance the accuracy of the CNN-based IDS models after they were affected by adversarial attacks, which was shown to increase after the adversarial attacks in an intelligible way, with accuracy scores ranging between 78.12% and 89.40%.

/pdf/enhancing-the-sustainability-of-deep-learning-based-network-uj6rxhd8.pdf

Enhancing the Sustainability of Deep-Learning-Based Network Intrusion Detection Classifiers against Adversarial Attacks

Utilizing machine learning (ML)-based approaches for network intrusion detection systems (NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to various threats. Of particular concern are two significant threats associated with ML: adversarial attacks and distribution shifts. Although there has been a growing emphasis on researching the robustness of ML, current studies primarily concentrate on addressing specific challenges individually. These studies tend to target a particular aspect of robustness and propose innovative techniques to enhance that specific aspect. However, as a capability to respond to unexpected situations, the robustness of ML should be comprehensively built and maintained in every stage. In this paper, we aim to link the varying efforts throughout the whole ML workflow to guide the design of ML-based NIDSs with systematic robustness. Toward this goal, we conduct a methodical evaluation of the progress made thus far in enhancing the robustness of the targeted NIDS application task. Specifically, we delve into the robustness aspects of ML-based NIDSs against adversarial attacks and distribution shift scenarios. For each perspective, we organize the literature in robustness-related challenges and technical solutions based on the ML workflow. For instance, we introduce some advanced potential solutions that can improve robustness, such as data augmentation, contrastive learning, and robustness certification. According to our survey, we identify and discuss the ML robustness research gaps and future direction in the field of NIDS. Finally, we highlight that building and patching robustness throughout the life cycle of an ML-based NIDS is critical.

On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective

In recent years, cyber-attacks on modern industrial control systems (ICS) have become more common and it acts as a victim to various kind of attackers. The percentage of attacked ICS computers in the world in 2021 is 39.6%. To identify the anomaly in a large database system is a challenging task. Deep-learning model provides better solutions for handling the huge dataset with good accuracy. On the other hand, real time datasets are highly imbalanced with their sample proportions. In this research, GAN based model, a supervised learning method which generates new fake samples that is similar to real samples has been proposed. GAN based adversarial training would address the class imbalance problem in real time datasets. Adversarial samples are combined with legitimate samples and shuffled via proper proportion and given as input to the classifiers. The generated data samples along with the original ones are classified using various machine learning classifiers and their performances have been evaluated. Gradient boosting was found to classify with 98% accuracy when compared to other 

Anomaly-based Intrusion Detection using GAN for Industrial Control Systems

With the increasing scale of network, attacks against network emerge one after another, and security problems become increasingly prominent. Network intrusion detection system is a widely used and effective security means at present. In addition, with the development of machine learning technology, various intelligent intrusion detection algorithms also start to sprout. By flexibly combining these intelligent methods with intrusion detection technology, the comprehensive performance of intrusion detection can be improved, but the vulnerability of machine learning model in the adversarial environment can not be ignored. In this paper, we study the defense problem of network intrusion detection system against adversarial samples. More specifically, we design a defense algorithm for NIDS against adversarial samples by using bidirectional generative adversarial network. The generator learns the data distribution of normal samples during training, which is an implicit model reflecting the normal data distribution. After training, the adversarial sample detection module calculates the reconstruction error and the discriminator matching error of sample. Then, the adversarial samples are removed, which improves the robustness and accuracy of NIDS in the adversarial environment.

Detecting Adversarial Examples for Network Intrusion Detection System with GAN

Deep learning plays an increasingly important role in various fields due to its superior performance, and it also achieves advanced recognition performance in the field of image classification. However, the vulnerability of deep learning in the adversarial environment cannot be ignored, and the prediction result of the model is likely to be affected by the small perturbations added to the samples by the adversary. In this paper, we propose a two-layer dynamic defense method based on defensive techniques pool and retrained branch model pool. First, we randomly select defense methods from the defense pool to process the input. The perturbation ability of the adversarial samples preprocessed by different defense methods changed, which would produce different classification results. In addition, we conduct adversarial training based on the original model and dynamically generate multiple branch models. The classification results of these branch models for the same adversarial sample is inconsistent. We can detect the adversarial samples by using the inconsistencies in the output results of the two layers. The experimental results show that the two-layer dynamic defense method we designed achieves a good defense effect.

A Two-Layer Moving Target Defense for Image Classification in Adversarial Environment

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ye Peng

Author Tools

Chat about Author

Papers

Detecting Adversarial Examples for Network Intrusion Detection System with GAN

A Two-Layer Moving Target Defense for Image Classification in Adversarial Environment