A Novel Approach for Detecting IoT Botnet Using Balanced Network Traffic Attributes

Shobana, M.; Poonkuzhali, S.

doi:10.1007/978-3-030-76352-7_48

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The raw data used are from a dataset from the Bot-IoT dataset [50] as presented in Table 1, which is grouped into five classes, namely DDoS, Reconnaissance, DoS, Normal and Theft. The reason to choose this dataset is that it represents a realistic IoT environment [51,52] and also a balanced class between normal and abnormal classes [53,54]. The size of the dataset is approximately 0.78 GB and it has about three million records.…”

Section: Datasetmentioning

confidence: 99%

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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: Datasetmentioning

confidence: 99%

“…As the dataset is well prepared as mentioned in earlier studies [50][51][52][53][54], it undergoes relatively simpler preprocessing stages, such as removing noises, missing values, tacking inconsistent or redundant data as well as data cleaning. Then, a role is assigned to the target attribute as a label.…”

Section: Dataset Preprocessingmentioning

confidence: 99%

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

Syamsuddin¹,

Barukab

2022

Electronics

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A Comparative Analysis of Supervised Machine Learning Models for Smart Intrusion Detection in IoT Network

Mittal,

Mishra,

Tripathi

et al. 2023

2023 3rd Asian Conference on Innovation in Technology (ASIANCON)

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Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

et al. 2023

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In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.

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A Novel Approach for Detecting IoT Botnet Using Balanced Network Traffic Attributes

Cited by 3 publications

References 14 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

A Comparative Analysis of Supervised Machine Learning Models for Smart Intrusion Detection in IoT Network

Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

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