# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 4 State-of-the-Art in IDS for IoT
### Additional Intrusion Detection Efforts within IoT  
The resource constraints in general and the limited available of power in particular are one of the challenges for any IDS within IoT. To this end, [PERSON] [67] seek to address this challenge by using an enhanced measurement of situation awareness in the IoT. The authors highlight the importance of awareness of state of monitoring system and propose a framework to enhance the energy efficiency of a self-reliant management and monitoring WSN cluster head selection algorithm. Although the experimental results demonstrate positive results for the approach, however, it is limited to assessingthe energy efficiency for a cluster head selection algorithm and, therefore, require further efforts to assess its impact on the monitoring capabilities of individual nodes.  
In [PERSON] et al. [68] an enhanced and safe home based IDS HIVE is proposed having three parts i.e., a sensor manager, firebase as a cloud database and user authentication service, and an android application for monitoring, configuring and remote notification. The proposed system is especially design for a smart home with focus on detecting physical intrusion into a home. HIVE aims to detect a physical intrusion by using three sensors i.e., an infrared sensor to detect motions, a magnetic switch sensor to detect status of a door or window and a load cell sensor to detect pressure such as footsteps.  
[PERSON] et al. [69] proposed a distributed system architecture for detecting the version number attacks in RPL-based networks and identifies malicious nodes. Furthermore, a number of intrusion detection system architectures have been developed by [PERSON] et al. [70] for resource-constrained 6 LoWPAN devices-based systems focusing on the sinkhole and selective-forwarding attacks. [PERSON] et al. [71] present an innovative approach, CIOTA, to intrusion detection for IoT leveraging blockchain technology. The proposed approach is comprised of local agents and a central component which coordinates information (alerts) received from these agents. Authors use blockchain technology to achieve assurances about the authenticity of alerts generated by local agents.  
[PERSON] et al. [72] present a network-based intrusion detection approach for IoT focusing on the task of feature extraction for effective intrusion detection. Authors have used deep migration learning model to develop the proposed approach and have evaluated their scheme with the KDD 1999 dataset. Although the proposed approach demonstrates good detection accuracy as compared to existing schemes, however, the evaluation is limited to detection accuracy, TPR and FPR and does not take into account performance efficiency of the proposed scheme, which is a critical metric due to the resource constraints of such devices. Similarly, [PERSON] et al. [73] presented one of the efforts focusing at detection of physical layer attacks on IoT networks. In particular, authors are focused at attacks whereby an attacker attempts to impersonate a victim IoT device through the use of Radio Frequency (RF) trace. Authors have proposed a deep learning-based method utilizing a dataset created through feature extraction of RF traces of similar devices to achieve device fingerprinting.