INTRUSION DETECTION SYSTEM FOR ZIGBEE-BASED IOT USING DATA ANALYSIS RULES

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 new types of attacks for systems and networks. This article outlines various techniques for detecting known attacks in ZigBee-based IoT systems. We introduced an Detection System (IDS) specific for ZigBee using data analytics method, that are used to provide an accurate detection method for known attacks. This article looks at our IDS implementation covering a wide variety of detection techniques to detect known attacks ZigBee IoT systems

INVESTIGATION AND PENETRATION OF DIGITAL ATTACKS ON ZIGBEE-BASED IOT SYSTEMS

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

ANOMALY DETECTION IN ZIGBEE-BASED IOT USING SECURE AND EFFICIENT DATA COLLECTION

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

Efficient Key Management System for Large-scale Smart RFID Applications

Due to low-cost and its practical solution, the integration of RFID tag to the sensor node called smart RFID has become prominent solution in various fields including industrial applications. Nevertheless, the constrained nature of smart RFID system introduces tremendous security and privacy problem. One of them is the problem in key management system. Indeed, it is not feasible to recall all RFID tags in order to update their security properties (e.g. update their private keys). On the other hand, using common key management solution like standard TLS/SSL is too heavy-weight that can drain and overload the limited resources. Furthermore, most of existing solutions are highly susceptible to various threats reaching from privacy threats, physical attacks to various technics of Man-in-the-Middle attacks. This paper introduces novel key management system, tailored to the limited resources of smart RFID system. It proposes light-weight mutual authentication and identity protection to mitigate the existing threats