DDoS Attack Detection in IoT Using Machine Learning Based Intrusion Detection System (IDS)

ElKashlan, Mohamed; Aslan, Heba K.; Azer, Marianne A.

doi:10.1109/icenco55801.2022.10032515

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…. These atributtes can store [19] each of the related features in IoT and IIoT cyberattack scenarios according to industry functions based on these aspects [53], as opposed to the early detection [11,54] of an IDS when faced with an imminent cyberattack against the infrastructure. The following are the methodologies' paths:…”

Section: Ooda-diamond-ckc and Ttps Methodologymentioning

confidence: 99%

“…First, the attackers use networks to perform denial of service (DoS) attacks and distributed denial of service (DDoS) attacks as a way to attack network availability. DoS and DDoS attacks [17,53] include ping of death and others, synchronize (SYN) flooding, and Hypertext Transfer Protocol (HTTP). In this case, by obtaining detection information, e.g., APT alert output from different attack detection sensors in APTALCM [26]: timestamp, alert type, Src Ip, Dest_Ip, Src_Port, Dest Port, and Victim_HostIp, they make a vector dimensional A(I(alert)m) = (a1, a2, a3, a4, a5, a6, a7).…”

Section: Ooda-diamond-ckc and Ttps Methodologymentioning

confidence: 99%

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Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia

Ortiz-Ruiz,

Bermejo,

Sicilia

et al. 2024

Electronics

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This study investigates the application of machine learning techniques for cyberattack prevention in Internet of Things (IoT) systems, focusing on the specific context of cyberattacks in Colombia. The research presents a comparative perspective on cyberattacks in Colombia, aiming to identify the most effective machine learning methods for mitigating and preventing such threats. The study evaluates the performance of logistic regression, naïve Bayes, perceptron, and k-nearest neighbors algorithms in the context of cyberattack prevention. Results reveal the strengths and weaknesses of these techniques in addressing the unique challenges posed by cyberattackers in Colombia’s IoT infrastructure. The findings provide valuable insights for enhancing cybersecurity measures in the region and contribute to the broader field of IoT security.

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Section: Ooda-diamond-ckc and Ttps Methodologymentioning

confidence: 99%

Section: Ooda-diamond-ckc and Ttps Methodologymentioning

confidence: 99%

Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia

Ortiz-Ruiz,

Bermejo,

Sicilia

et al. 2024

Electronics

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“…The study uses a DL approach, like multi-layer perceptron (MLP), for efficient and effective identification of DDoS attacks. Conventional intrusion detection systems (IDS) cannot identify complicated attacks with abnormal patterns [9]. Attackers acquire detected signatures by imitating normal users.…”

Section: Introductionmentioning

confidence: 99%

Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment

Aljebreen,

Mengash,

Arasi

et al. 2023

IEEE Access

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A more widespread Internet of Things (IoT) device is performing a surge in cyber-attacks, with Distributed Denial of Service (DDoS) attacks posing a major risk to the reliability and availability of IoT services. DDoS attacks overwhelm target methods by flooding them with a huge volume of malicious traffic from several sources. Mitigating and identifying these attacks in IoT platforms are vital to maintaining the seamless function of IoT services and the maintenance of secret information. Feature selection (FS) is a key stage in the machine learning (ML) pipeline as it supports decreasing the data size, enhancing model outcomes, and speeding up training and inference. As part of IoT with DDoS attack detection, FS proposes to recognize a subset of IoT-related features that is optimum to represent the traffic features and distinguish between malicious and benign activities. This study designs a new DDoS attack detection using a snake optimizer with ensemble learning (DDAD-SOEL) technique on the IoT platform. The purpose of the DDAD-SOEL approach lies in the effectual and automated identification of DDoS attacks. To attain this, the DDAD-SOEL technique utilizes the SO algorithm for feature subset selection. Besides, an ensemble of three DL approaches namely long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN) approach. Finally, the Adadelta optimizer can be applied for the parameter tuning of the DL algorithms. The simulation value of the DDAD-SOEL methodology was tested on the benchmark database and the outcome indicates the improvements of the DDAD-SOEL methodology over other recent models in terms of distinct measures.

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Aquila Optimization Algorithm based Feature Selection with Optimal Machine Learning for Security Internet of Things Environment

Khairnar,

Sesha Vidhya,

Keerthana

et al. 2024

2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)

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DDoS Attack Detection in IoT Using Machine Learning Based Intrusion Detection System (IDS)

Cited by 5 publications

References 24 publications

Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia

Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia

Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment

Aquila Optimization Algorithm based Feature Selection with Optimal Machine Learning for Security Internet of Things Environment

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