RETRACTED: Detection of attacks in IoT sensors networks using machine learning algorithm

Bedi, Pradeep; Mewada, Shivlal; Vatti, Rasmbabu Arjunarao; Singh, Chaitanya; Dhindsa, Kanwalvir Singh; Ponnusamy, Muruganantham; Sikarwar, Ranjana

doi:10.1016/j.micpro.2020.103814

Cited by 35 publications

(8 citation statements)

References 7 publications

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“…It was demonstrated that RF outperforms other technologies. This conclusion was comparable to that of Bedi et al [25], who investigated irregularities on an IoT network by comparing ANN, LR, RF, SVM, and DT. They also found RF performed better than the others.…”

Section: Relevant Studiessupporting

confidence: 88%

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

Syamsuddin¹,

Barukab

2022

Electronics

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The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi.

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Section: Relevant Studiessupporting

confidence: 88%

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

Syamsuddin¹,

Barukab

2022

Electronics

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“…In addition, spyware like Skeeyah and worms like Omni and Themoon also perform industrial Spying in IoT areas. Spying, along with different attacks like data type probing, is detected in [ 45 ], using a three-stage machine learning-based detection model. Stage one includes the dataset preparation and assortment.…”

Section: Iot Malware Categories and Familiesmentioning

confidence: 99%

“…Spying, along with different attacks like data type probing, is detected in [ 45 ], using a three-stage machine learning-based detection model. Stage one includes the dataset preparation and assortment.…”

Section: Iot Malware Categories and Familiesmentioning

confidence: 99%

“…At times, VPNFilter is spyware that targets Ukrainian hosts and slows down the industry activities using a C&C server that is specific for this specific country [ 59 ]. [ 45 ] discussed a machine learning detection model for detecting different IoT malware attacks, including Spying which is the most common target-specific attack. Mapping of IoT Malware to Access mechanisms is illustrated in the Table 7 .…”

Section: Iot Malware Categories and Familiesmentioning

confidence: 99%

“…[ 45 ] discussed a machine learning detection model for detecting different IoT malware attacks, including Spying which is the most common target-specific attack.…”

Section: Iot Malware Categories and Familiesmentioning

confidence: 99%

See 2 more Smart Citations

IoT malware: An attribute-based taxonomy, detection mechanisms and challenges

Victor,

Lashkari,

et al. 2023

Peer-to-Peer Netw. Appl.

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During the past decade, the Internet of Things (IoT) has paved the way for the ongoing digitization of society in unique ways. Its penetration into enterprise and day-to-day lives improved the supply chain in numerous ways. Unfortunately, the profuse diversity of IoT devices has become an attractive target for malware authors who take advantage of its vulnerabilities. Accordingly, enhancing the security of IoT devices has become the primary objective of industrialists and researchers. However, most present studies lack a deep understanding of IoT malware and its various aspects. As understanding IoT malware is the preliminary base of research, in this work, we present an IoT malware taxonomy with 100 attributes based on the IoT malware categories, attack types, attack surfaces, malware distribution architecture, victim devices, victim device architecture, IoT malware characteristics, access mechanisms, programming languages, and protocols. In addition, we have mapped these categories into 77 IoT Malwares identified between 2008 and 2022. Furthermore, To provide insight into the challenges in IoT malware research for future researchers, our study also reviews the existing IoT malware detection works.

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Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey

Manokaran

Vairavel

2022

Lecture Notes in Electrical Engineering

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RETRACTED: Detection of attacks in IoT sensors networks using machine learning algorithm

Cited by 35 publications

References 7 publications

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

IoT malware: An attribute-based taxonomy, detection mechanisms and challenges

Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey

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