IoT networks attacks detection using multi-novel features and extra tree random - voting ensemble classifier (ER-VEC)

Sarwar, Ayesha; Mushtaq, Muhammad Faheem; Akram, Urooj; Rustam, Furqan; Hamza, Ameer; Rupapara, Vaibhav; Ullah, Saleem

doi:10.1007/s12652-023-04666-x

Cited by 3 publications

(3 citation statements)

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“…IoT-Based Network Intrusion Detection Systems ((References: [54,58,62,63,64,65,66,67,68,69,99,100,101]) Disparate Membership Inference Attacks (Reference: [35]) Analysis of Open Source Datasets for IDS (Reference: [23,27]) Hybrid Deep Learning Model for IDS (Reference: [32]) Smart Home Anomaly-Based IDS ( Reference: [36]) The research articles meticulously examined in this study have been judiciously classified according to their quartile rankings, providing invaluable insights into the academic influence and eminence of these esteemed publications. The quartile categorization, elegantly portrayed in Fig.…”

Section: ) Cluster 4: Other Topicsmentioning

confidence: 99%

“…Comprises several files of IoT network traffic data. Each file contains both benign, or normal, network traffic data, as well as malicious traffic data associated with the prevalent IoT botnet assaults often referred to as the Mirai botnet [67]. Three distinct forms of IoT botnet attacks, namely SYN-Flooding, ACK-Flooding, and HTTP-Flooding, were the subject of research emphasis.…”

Section: G Mirai Dataset (Cicids-mirai)mentioning

confidence: 99%

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From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

Khanan,

Abdelgadir Mohamed,

Mohamed

et al. 2024

IEEE Access

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In the wake of the expanding digital realm, the imperative for robust cybersecurity measures has burgeoned significantly. This extensive investigation digs into the complicated realm of cybersecurity datasets, with the goal of improving our understanding and implementation of these critical tools. This study's comprehensive evaluation of 37 distinct datasets shows a complicated world in which no one dataset stands out as totally suitable for all uses. A precise balance must be struck between crucial dataset qualities such as diversity, authenticity, and usefulness. Using a complete assessment technique, this paper illuminates the challenges and possibilities that developers and researchers face in the field of cybersecurity datasets. Although some databases accurately identify certain forms of cyberattacks, their coverage may not include the whole range of cyber threats. On the other hand, datasets with a strong emphasis on accurate portrayal may forgo comprehensiveness or practical use. This intricacy is heightened by the dynamic and sophisticated nature of cyber threats, emphasizing the delicate balance required between accuracy and practicality. The study emphasizes the necessity of selecting datasets strategically and contextually for cybersecurity studies, with the goal of matching research objectives with the most appropriate dataset selections. Furthermore, it emphasizes the need of continual cooperation and innovation within the cybersecurity community in developing datasets that accurately represent the ever-changing nature of cyber threats. After analyzing 37 cybersecurity datasets, it is obvious that no one dataset can meet all of the field's unique demands, demonstrating the need of a flexible, adaptable, and developing dataset for intrusion detection systems (IDS). This inquiry offers a critical assessment of dataset characteristics and their related issues, providing essential insights for academics, professionals, and dataset creators, enabling the construction of a more resilient and adaptable cybersecurity infrastructure.

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Section: ) Cluster 4: Other Topicsmentioning

confidence: 99%

Section: G Mirai Dataset (Cicids-mirai)mentioning

confidence: 99%

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

Khanan,

Abdelgadir Mohamed,

Mohamed

et al. 2024

IEEE Access

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“…The quality of a single model can also decrease over time due to the ongoing evolution of botnets 11,12 . The utilization of multiple classifiers in some developed models for botnet detection has shown limitations, as they tend to yield comparatively lower detection rates and higher False Positive Rates (FPR) [13][14][15] . Imbalanced datasets pose a significant challenge to achieving accurate botnet detection.…”

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confidence: 99%

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

Hossain,

Islam

2023

Sci Rep

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In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

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Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset using ER-VEC

Jha,

Ojha,

Mishra

et al. 2024

2024 2nd International Conference on Disruptive Technologies (ICDT)

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IoT networks attacks detection using multi-novel features and extra tree random - voting ensemble classifier (ER-VEC)

Cited by 3 publications

References 55 publications

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset using ER-VEC

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