Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks

Alabsi, Basim Ahmad; Anbar, Mohammed; Rihan, Shaza Dawood Ahmed

doi:10.3390/s23125644

Cited by 9 publications

(2 citation statements)

References 40 publications

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“…After the features are selected, the dataset is divided into two subsets: the training subset and the testing subset. By selecting the right testing and training data, classification accuracy can be improved [26]. The training data are the set of instances trained on the model, while the test data are used to determine the model's ability or execution.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Bukhowah,

Aljughaiman,

Rahman

2024

Electronics

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The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Bukhowah,

Aljughaiman,

Rahman

2024

Electronics

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“…In this paper, we propose some new techniques to protect systems against the ML-based DoS attacks. The authors of [10] presented a CGAN-based intrusion detection system against DDoS attacks on IoT networks. The core of the system is the mechanism which generates synthetic traffic mapping known patterns and completely new network discriminator network to detect anomalies.…”

Section: Of 15mentioning

confidence: 99%

Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network

Nędza,

Domżał

2024

Preprint

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With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

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Enhancing network intrusion detection: a dual-ensemble approach with CTGAN-balanced data and weak classifiers

Soflaei,

Salehpour,

Samadzamini

2024

J Supercomput

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Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks

Cited by 9 publications

References 40 publications

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network

Enhancing network intrusion detection: a dual-ensemble approach with CTGAN-balanced data and weak classifiers

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