IoT botnet detection via power consumption modeling

Jung, Woosub; Zhao, Hongyang; Sun, Minglong; Zhou, Gang

doi:10.1016/j.smhl.2019.100103

Cited by 53 publications

(42 citation statements)

References 17 publications

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“…[9] NB, BN, & DT 98.00 [10] Not ML method 99.00 [11] ANN, SVM, k-NN, NB, & GBM 97.00 [12] k-NN, DT, & RF 70.00 [13] ANN, SVM, NB, DT, RF, LR. & BNet 99.47 [14] NB & DT 97.00 [15] DT 99.00 [16] K-means 82.10 [17] DT 97.00 [18] RF 86.41 [19] ANN, SVM, & NB 93.90 [44] Not ML method 99.82 [20] k-NN & RF 91.10 [21] ANN 97.87 [22] DT 90.40 [23] DT 99.46 [24] ANN, SVM, & k-NN 99.00 [45] Not ML method 95.50 [46] Not ML method 99.68 [25] SVM & ANN 94.00 [47] Not ML method 99.70 [26] DT & ANN 99.20 [48] Not ML method 99.00 [27] KNN, SVM, DT, RF, & ANN 99.00 [28] SVM 99.15 [29] k-NN 94.00 [49] Not ML method 96.20 [50] Not ML method 99.35 [51] Not ML method 92.92 [52] Not ML method 98.70 [53] Not ML method 97.00 [54] Not ML method 98.70 [55] Not ML method *100 [56] Not ML method 99.94 [57] Not ML method 99.60 [58] Not ML method 98.60 [59] Not ML method 97.20 [30] k-NN, NB, DT, RF, & SVM 91.80 [31] ANN 99.60 This research LR, LR, DT, NB, k-NN, RF, GBM, SVM, K-means, K-medians, mini batch, HC, ANN, DBSCAN, GMM, LAC, AP, and ensemble learning…”

Section: Resultsmentioning

confidence: 99%

Using Ensemble Learning Technique for Detecting Botnet on IoT

Rezaei

2021

SN COMPUT. SCI.

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Despite the growing reputation and the ubiquitous nature of the IoT, it poses significant challenges as it is also considered a convenient platform for cyberattacks. The connection of various devices without fixed security help attackers in allowing botnet to run high crash DDoS attacks against a range of internet services. The botnet is one of the main security challenges that have the most impact on IoT for several reasons. It allows the private network devices to be infected by malicious software and controlled as a group without the owners' awareness. Botnets are often utilized for sending spam, stealing data, and performing DDoS attacks. As there are many areas of security that utilize machine learning (ML) technology, utilizing ML to detect botnet appears to be realistic and workable. While several studies have been conducted using ML for detecting botnet, they are either not highly accurate or only compatible with specific types of botnet or devices. This study aims to create an ensemble learning model using the best ML methods among supervised learning, unsupervised learning, and regression learning to optimize the accuracy of botnet detection on IoT and minimize the number of features requested. After several examinations in different combinations of ML algorithms, this study managed to achieve 100% accuracy of botnet detection as well as minimizing the number of features to only 20.

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Section: Resultsmentioning

confidence: 99%

Using Ensemble Learning Technique for Detecting Botnet on IoT

Rezaei

2021

SN COMPUT. SCI.

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“…In [42], by using a test-bed, the range of attacks included flood attacks and SQL injection attacks, SYN Flood, TCP Flood, UDP Flood Detection, ICMP Flood Detection, and HTTP Flood Detection. In [35], the authors used a test-bed with a set of attacks against IoT, such as botnets attack, Mirai, Hajime, Bricker, BotIoT Reaper, Masuta, Sora. In [30], by using a test-bed, the range of the attacks included malicious scan, DoS attack, malicious control spying, malicious operation, wrong setting categories, and data probing.…”

Section: Analysis Of Type Of Attacks Detectedmentioning

confidence: 99%

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

et al. 2021

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The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.

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“…Reference [95] 99.98% accuracy for DenseNet and 83.15% for SVM. Reference [96] achieves up to 98.6% botnet detection accuracy on the self-tests and about 90% on the cross-evaluation test.…”

Section: Neural Network Detection Mechanismsmentioning

confidence: 99%

“…While this method requires too high computational costs to run in real time, they propose implementing a two-step algorithm in order to achieve fast, memory-efficient factorisation. More nontraditional methods like [96] seek to identify botnets through the usage of power consumption as the parameter for their CNN model. In [29] a similar lightweight solution is also mentioned for use in small memory capacity devices with low-power processors, since these are not able to have reliable anti-malware systems.…”

Section: Neural Network Detection Mechanismsmentioning

confidence: 99%

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

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IoT botnet detection via power consumption modeling

Cited by 53 publications

References 17 publications

Using Ensemble Learning Technique for Detecting Botnet on IoT

Using Ensemble Learning Technique for Detecting Botnet on IoT

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

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