IoT Botnets

Beltrán-García, Pamela; Aguirre-Anaya, Eleazar; Escamilla-Ambrosio, Ponciano Jorge; Acosta-Bermejo, R.

doi:10.1007/978-3-030-33229-7_21

Cited by 5 publications

(9 citation statements)

References 15 publications

(21 reference statements)

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“…Malware botnets are known with being high severity attacks that cost the victims huge managerial, and financial losses. Malware developers were able to start the first botnet family that is able to launch a UDP Flood DDoS attack in 2008; this botnet is called Linux/Hydra [18]. Psybot, Chuck Norris, and Tsunami followed in 2009, 2010 respectively [19].…”

Section: A Botnet Types Families and Implementationsmentioning

confidence: 99%

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

et al. 2022

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The fast expansion of the Internet of Things (IoT) in the technology and communication industries necessitates a continuously updated cyber-security mechanism to keep protecting the systems' users from any possible attack that might target their data and privacy. Botnets pose a severe risk to the IoT, they use malicious nodes in order to compromise other nodes inside the network to launch several types of attacks causing service disruption. Examples of these attacks are Denial of Service (DoS), Distributed Denial of Service (DDoS), Service Scan, and OS Fingerprint. DoS and DDoS attacks are the most severe attacks in IoT launched from Botnets. Where the Botnet commands previously compromised single or multiple nodes in the network to launch network traffic towards a specific node or service. This leads to computational, power, or network bandwidth draining, which causes specific services to shutdown or behave unexpectedly. In this paper, we aim to verify the detection approach reliability when it encounters an attack that it was not trained on before. Therefore, we evaluate the performance of Convolutional Neural Networks (CNN) classifier in order to detect the malicious attack traffic especially the attacks that never reported before in the network i.e. Zero-Day attacks. Different regularization techniques i.e. L1 and L2 have been used to address the problem of overfitting and to control the complexity of the classifier. The experimental results show that using the regularization methods gives a higher performance in all the evaluation metrics compared to the standard CNN model. In addition, the enhanced CNN technique improves the capability of IDSs in detection of unseen intrusion events.

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Section: A Botnet Types Families and Implementationsmentioning

confidence: 99%

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

et al. 2022

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“…Generally, the botnet can be defined as a collection of compromised devices known as bots running malicious code and controlled by an administrator called the botmaster [21][22][23].…”

Section: Iot Botnetmentioning

confidence: 99%

“…Many studies [21,22,27,37] have agreed that IoT botnets carry out their actions in at least the three main phases (see Figure 3), as described below:…”

Section: Iot Botnet Life Cycle Phasesmentioning

confidence: 99%

“…Then, it was followed by many IoT botnets that became the building blocks of the IoT botnets seen today. The Hydra IoT botnet, which appeared in 2008 [21,22,24,25], infected routers and had DDoS and spreading competences. Mirai, in 2016, was the largest IoT botnet, which infected millions of devices and dominated them to perform the biggest DDoS ever [22,26,27].…”

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confidence: 99%

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Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

et al. 2021

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Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. Thus, this study aimed to identify, assess and provide a thoroughly review of experimental works on the research relevant to the detection of IoT botnets. To accomplish this goal, a systematic literature review (SLR), an effective method, was applied for gathering and critically reviewing research papers. This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios. The authors analyzed the nominated research and the key methods related to them. The detection methods have been classified based on the techniques used, and the authors investigated the botnet phases during which detection is accomplished. This research procedure was used to create a source of foundational knowledge of IoT botnet detection methods. As a result of this study, the authors analyzed the current research gaps and suggest future research directions.

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“…Therefore, exploiting IoT devices to form a IoT botnet poses a threat that may affect precious resources. Commonly, a botnet can be identified as a collection of compromised devices recognised as bots operating malicious code and managed by an administrator called the botmaster [ 2 , 3 , 4 ]. These bots can propagate throughout networks by scanning for vulnerable devices and exploiting them in a process that aims to extend the botnets.…”

Section: Introductionmentioning

confidence: 99%

Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet

Wazzan

Alghazzawi

Albeshri

et al. 2022

Sensors

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In recent times, organisations in a variety of businesses, such as healthcare, education, and others, have been using the Internet of Things (IoT) to produce more competent and improved services. The widespread use of IoT devices makes our lives easier. On the other hand, the IoT devices that we use suffer vulnerabilities that may impact our lives. These unsafe devices accelerate and ease cybersecurity attacks, specifically when using a botnet. Moreover, restrictions on IoT device resources, such as limitations in power consumption and the central processing unit and memory, intensify this issue because they limit the security techniques that can be used to protect IoT devices. Fortunately, botnets go through different stages before they can start attacks, and they can be detected in the early stage. This research paper proposes a framework focusing on detecting an IoT botnet in the early stage. An empirical experiment was conducted to investigate the behaviour of the early stage of the botnet, and then a baseline machine learning model was implemented for early detection. Furthermore, the authors developed an effective detection method, namely, Cross CNN_LSTM, to detect the IoT botnet based on using fusion deep learning models of a convolutional neural network (CNN) and long short-term memory (LSTM). According to the conducted experiments, the results show that the suggested model is accurate and outperforms some of the state-of-the-art methods, and it achieves 99.7 accuracy. Finally, the authors developed a kill chain model to prevent IoT botnet attacks in the early stage.

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IoT Botnets

Cited by 5 publications

References 15 publications

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet

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