DDOS Attack Identification using Machine Learning Techniques

Peneti, Subhashini; Hemalatha, E

doi:10.1109/iccci50826.2021.9402441

Cited by 19 publications

(4 citation statements)

References 9 publications

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“…The most effective methods for using alreadygathered data are those that use machine learning. With more data available, categorization accuracy improves [52] However, problems including a lack of datasets, hostile assaults, and model robustness continue to exist. Further investigation is required due to the requirement for interpretable ML models for security experts and regulatory compliance.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques

Zerin Hasan Sahosh,

Azraf Faheem,

Marzana Bintay Tuba

et al. 2024

Malaysian J. Sci. Adv. Tech.

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The rapid growth of the internet and the increasing reliance on digital infrastructures have posed significant challenges to cybersecurity. Among the other variants of attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most destructive and common threats. These attacks disrupt or slow down network services by overwhelming the network infrastructure with a massive volume of malicious traffic. To effectively identify and mitigate DDoS attacks, machine learning techniques have been extensively employed in intrusion detection systems. Machine learning approaches offer the advantage of automating the detection process by learning patterns and characteristics of DDoS attacks from historical data. Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to classify and detect DDoS attacks. These algorithms leverage features extracted from network traffic data, including packet size, packet delay patterns, and traffic behaviour, to differentiate between normal and malicious traffic.

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Section: Discussionmentioning

confidence: 99%

A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques

Zerin Hasan Sahosh,

Azraf Faheem,

Marzana Bintay Tuba

et al. 2024

Malaysian J. Sci. Adv. Tech.

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“…The classification of its speed as bits or bytes is discussed above in Table 1. It is [62] REVIEW 4 of 25 are 40 Gigabit Ethernet, 100 Gigabit Ethernet, and InfiniBand [22]. A Cisco Report predicted that the internet protocol may cross 4.3 zettabytes in 2023, which is 1879 exabytes higher than 2018 [3].…”

Section: Architecture Of High-speed Networkmentioning

confidence: 99%

High-Speed Network DDoS Attack Detection: A Survey

Haseeb-ur-rehman,

Aman,

Hasan

et al. 2023

Sensors

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Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

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“…Machine-Learning-Based Smart Detection System 6.9.1. General Information This smart detection system [88] works on the identification of DDOS attacks using Machine-Learning techniques such as XGBoost (Section 4.11.2), AdaBoost, Random Forests (Section 4.11.1), and multilayer perceptron. They focused on fewer features and used less memory to reduce complexity and improve the system's efficiency.…”

Section: Workingmentioning

confidence: 99%

“…The authors [88] built their classification model using XGBoost (Section 4.11.2), Ad-aBoost, Random Forests (Section 4.11.1), and multilayer perceptron, which uses the CI-CIDS2017 dataset for training. After training, their system can classify the test data into either DDoS attacks or benign ones.…”

Section: Ddos Detection Using a Machine-learning-based Smart Detectio...mentioning

confidence: 99%

A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

Khan

Namin

2022

Electronics

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The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

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DDOS Attack Identification using Machine Learning Techniques

Cited by 19 publications

References 9 publications

A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques

A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques

High-Speed Network DDoS Attack Detection: A Survey

A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

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