ZigBee IoT Intrusion Detection System: A Hybrid Approach with Rule-based and Machine Learning Anomaly Detection

Sadikin, Fal; Kumar, Sandeep

doi:10.5220/0009342200570068

Cited by 9 publications

(5 citation statements)

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“…Research in hybrid approaches could also explore the use of deep learning techniques, such as deep autoencoders, which can learn representations of data without the need for explicit feature engineering [89]. Deep learning techniques have shown promise in other domains and could be adapted for use in anomaly detection in NFV networks.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Zehra,

Faseeha,

Syed

et al. 2023

Sensors

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Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

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Section: Hybrid Approachesmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Zehra,

Faseeha,

Syed

et al. 2023

Sensors

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“…Prevention with authentication techniques and access control could be seen from examples [3]- [9]. Then, monitoring and detection could be adopted through methods [10]- [21].…”

Section: B Related Researchmentioning

confidence: 99%

Investigation and Penetration of Digital Attacks on Zigbee-Based Iot Systems

Sadikin

Wiranda

2023

JTekInfULM

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The market for Internet of Things (IoT) products and services has grown rapidly. It has been predicted that the deployment of these IoT applications will grow exponentially in the near future. However, the rapid growth of IoT brings new security risks and potentially opens up new types of attacks for systems and networks. This article outlines various techniques to carry out attacks on ZigBee-based IoT systems. We conducted penetration experiments on various possible attacks on Zigbee-based IoT. The purpose of this experiment’s results is for reference in developing an Intrusion Detection System (IDS) specifically for ZigBee-based IoT.

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“…This study cited some examples of organizational policy rules from Mohammad [1] and Sadikin [2], and several examples of prevention with authentication techniques and access control from Sadikin and Mkyas [3]- [9]. The monitoring and detection was adopted from the previous research [10]- [21].…”

Section: B Related Researchmentioning

confidence: 99%

Anomaly Detection in Zigbee-Based Iot Using Secure and Efficient Data Collection

Sadikin

Wiranda

2023

JTekInfULM

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This article outlines various techniques for detecting types of attacks that may arise in ZigBee-based IoT system. The researchers introduced a hybrid Intrusion Detection System (IDS), combining rule-based intrusion detection and machine learning-based anomaly detection. Rule-based attack detection techniques are used to provide an accurate detection method for known attacks. However, determining accurate detection rules requires significant human effort that is susceptible to error. If it is done incorrectly, it can result in false alarms. Therefore, to alleviate this potential problem, the system is being upgraded by combining it (hybrid) with machine learning-based anomaly detection. This article expounds the researchers’ IDS implementation covering a wide variety of detection techniques to detect both known attacks and potential new types of attacks in ZigBee-based IoT system. Furthermore, a safe and efficient meth-od for large-scale IDS data collection is introduced to provide a trusted reporting mechanism that can operate on the stringent IoT resource requirements appropriate to today's IoT systems.

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ZigBee IoT Intrusion Detection System: A Hybrid Approach with Rule-based and Machine Learning Anomaly Detection

Cited by 9 publications

References 0 publications

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Investigation and Penetration of Digital Attacks on Zigbee-Based Iot Systems

Anomaly Detection in Zigbee-Based Iot Using Secure and Efficient Data Collection

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