Detection of DDoS attacks in D2D communications using machine learning approach

Rani, S.V. Jansi; Ioannou, Iacovos; Nagaradjane, Prabagarane; Christophorou, Chrıstophoros; Vassiliou, Vasos; Charan, Sai; Prakash, Sai S.; Parekh, Niel; Pitsillides, Andreas

doi:10.1016/j.comcom.2022.11.013

Cited by 19 publications

(8 citation statements)

References 28 publications

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“…Attack [62][63][64] Slowloris is an application layer distributed denial-of-service (DDoS) attack that overloads and eventually shuts down a target Web server by repeatedly delivering unclear Hypertext Transfer Protocol (HTTP) requests from a single IoT device. This specific DDoS attack can be launched with minimal bandwidth while leaving other applications and ports unaffected.…”

Section: Ddos (Slowloris)mentioning

confidence: 99%

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

Tariq

Ahmed

Bashir

et al. 2023

Sensors

114

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The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT’s layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.

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Section: Ddos (Slowloris)mentioning

confidence: 99%

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

Tariq

Ahmed

Bashir

et al. 2023

Sensors

114

View full text Add to dashboard Cite

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“…In [58], authors use a variety of Machine Learning (ML) techniques, such as random forest, light GBM, XGBoost, and AdaBoost, to quantify detection accuracy in the context of DDoS and DoS attacks and examine their performance through extensive simulation. According to the gathered results, random forest improves the accuracy of both the Slowloris and CIC-DDoS2019 datasets.…”

Section: ) Existing Methods For Identifying Numerous (More Than Two) ...mentioning

confidence: 99%

“…Only some utilise deep neural networks from the other approaches: the [20] and [51]. Also, regarding the dataset, this paper and [58] used a well-known dataset with an emulating dataset. Moreover, this paper and [58] support D2D communication.…”

Section: Comparison Of Related Work That Utilised ML Used At Idsmentioning

confidence: 99%

A Novel Deep Hierarchical Machine Learning Approach for Identification of Known and Unknown Multiple Security Attacks in a D2D Communications Network

Rani,

Ioannou,

Nagaradjane

et al. 2023

IEEE Access

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Intrusion Detection Systems (IDSs) have played a crucial role in identifying cyber threats for a very long time. Still, their significance has increased significantly with the advent of 5G/6G technologies, particularly Device-to-Device (D2D) communication. Multiple cyberattacks, such as Man in the Middle (MITM) attacks, Structured Query Language (SQL) injection attacks, Dictionary attacks, Distributed Denial of Service (DDoS) attacks, and others by using specific attack tools such as HULK, RUDY, and GoldenEye, that can cause rapid battery drain, rendering D2D network devices more prone to hardware failure or even to the dissolution of the D2D communication network affecting the operation and the performance of the mobile network. Using a Deep Hierarchical Machine Learning Model/Deep Hierarchical Neural Network (DHMLM/DHNN) technique, we develop an Intrusion Detection System (IDS) for D2D communication that, due to its hierarchical structure, is distinct from other comparable approaches. (i.e., Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), Long short-term memory (LSTM)), has several advantages, including i) reduced training time (training time can be reduced by 56%.); ii) the ability to identify multiple types of attacks; iii) the ability to identify Zero-day/Unknown attacks (i.e., attacks that it has not seen before); iv) a more straightforward model design due to the low number of connections and neurons compared to other approaches (excluding RNN and LSTM), and; v) overall outstanding performance in terms of accuracy (i.e., 99.07%). The custom/unified data set used to train and evaluate the model was partially manually emulated and partially sampled from a large set (>95%) from the commonly used CIC-DDoS-2019 data set. The after-comparison final proposed model's 99.07% accuracy on this unified data set demonstrates the efficacy of our method. The model was also tested and demonstrated an astounding 99.63% accuracy for zero-day/unknown attacks.

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“…The study [51] employed ML to detect DDoS attacks in device-to-device (D2D) connections. Solutions include using ML techniques such as Random Forest, XG Boost, Ada Boost, and Light Gradient Boosting Machine (LGBM) to detect and prevent DDoS attacks in D2D communication systems.…”

Section: A Ddos Anomaly Based On ML -Existing Research Workmentioning

confidence: 99%

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abdallah,

Saif Rashed Obaid Alkaabi,

Bark Nasser Douman Alameri

et al. 2024

IEEE Access

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In the rapidly evolving landscape of computing and networking, the concepts of cloud networks have gained significant prominence. Although the cloud network offers on-demand access to shared resources, anomalies pose potential risks to the integrity and security of cloud networks. However, protecting the cloud network against anomalies remains a challenge. Unlike traditional detection techniques, machine learning (ML) and deep learning (DL) offer new and adaptable methods for detecting anomalies in cloud networks. The objective of this study is to comprehensively explore existing ML /DL methods for detecting different anomalies based on distributed denial of service anomaly (DDoS) and intrusion detection systems (IDS) in cloud networks. The study seeks to address the gaps in anomaly detection for cloud networks, proposing potential solutions for anomaly detection in these cloud environments. The ultimate goal is to contribute valuable insights and practical solutions to enhance the security and reliability of cloud networks through effective anomaly detection by ML/ DL techniques. Methodologies for ML/DL are explained, along with their advantages, disadvantages, and respective approaches. In addition, a summary of the comparison between different ML/ DL models is also included.

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Detection of DDoS attacks in D2D communications using machine learning approach

Cited by 19 publications

References 28 publications

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

A Novel Deep Hierarchical Machine Learning Approach for Identification of Known and Unknown Multiple Security Attacks in a D2D Communications Network

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

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