A Review on IoT Botnet

Zhao, Hao; Shu, Hui; Xing, Ying

doi:10.1145/3448734.3450911

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Command and Control This refers to the strategy attackers employ to sustain communication and exert control over compromised systems, with the manipulated hosts commonly referred to as bots or zombies [28]. This technique can be utilized to issue commands, receive updates, and more.…”

Section: Common Techniques In Aptsmentioning

confidence: 99%

“…This technique, often implicated in the spread of malware, ordinarily entails the substitution of legitimate updates or versions with malicious ones. It has been observed at various stages of cyber attacks, including the initial intrusion stage, the establishment of persistence step, and the evasion of detection procedure [28].…”

Section: Common Techniques In Aptsmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Persistent Threats based on Supply Chain Vulnerabilities: Challenges, Solutions and Future Directions

Tan,

Marnerides,

Anagnostopoulos

et al. 2024

Preprint

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Section: Common Techniques In Aptsmentioning

confidence: 99%

Section: Common Techniques In Aptsmentioning

confidence: 99%

Advanced Persistent Threats based on Supply Chain Vulnerabilities: Challenges, Solutions and Future Directions

Tan,

Marnerides,

Anagnostopoulos

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms (MLAs) are the current trend for the identification and detection of IoT botnets that are working based on the network traffic [7]. Distinct intrusion detection systems (IDS) are utilized to detect IoT botnets with misuse, anomaly, DNS, stateful, and hybrid techniques [8]. The misusebased method is used to detect a botnet with pre-defined signatures of the malware, while unknown malware is detectable by using an anomaly-based technique.…”

mentioning

confidence: 99%

“…∑ Total number of packets transmitted ∑ 𝑇 𝑇 (8) where 𝑇 and 𝑇 indicate the reception and sending time of the ith packet. The throughput can be measured in kb/sec for each successfully transmitted packet.…”

mentioning

confidence: 99%

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

Garg,

Kumar,

Mahanti

2024

Future Internet

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The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

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“…9 Since the Mirai source code is publicly available; developers are creating new sorts of malicious code for IoT devices based on the advantages of previous IoT botnets. 10 To analyze such kinds of IoT botnet traffic the datasets UNSW_NB15 and BoT_IoT were created by the Cyber Range Lab of UNSW Canberra, Australia in 2015 and 2019 respectively. Since UNSW_NB15 is a virtual dataset and has a limited number of attack vectors although BoT_IoT is a real-time dataset still it has few attack vectors that are unable to describe the detailed traffic analysis of IoT botnet for finding the root causes in the various possible attack scenarios.…”

mentioning

confidence: 99%

An Efficient Framework for Detection and Classification of IoT Botnet Traffic

et al. 2022

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The Internet of Things (IoT) has become an integral requirement to equip common life. According to IDC, the number of IoT devices may increase exponentially up to a trillion in near future. Thus, their cyberspace having inherent vulnerabilities leads to various possible serious cyber-attacks. So, the security of IoT systems becomes the prime concern for its consumers and businesses. Therefore, to enhance the reliability of IoT security systems, a better and real-time approach is required. For this purpose, the creation of a real-time dataset is essential for IoT traffic analysis. In this paper, the experimental testbed has been devised for the generation of a real-time dataset using the IoT botnet traffic in which each of the bots consists of several possible attacks. Besides, an extensive comparative study of the proposed dataset and existing datasets are done using popular Machine Learning (ML) techniques to show its relevance in the real-time scenario.

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A Review on IoT Botnet

Cited by 7 publications

References 5 publications

Advanced Persistent Threats based on Supply Chain Vulnerabilities: Challenges, Solutions and Future Directions

Advanced Persistent Threats based on Supply Chain Vulnerabilities: Challenges, Solutions and Future Directions

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

An Efficient Framework for Detection and Classification of IoT Botnet Traffic

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