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Advances, Systems and Applications

Journal of Cloud Computing Cover Image

Table 3 A comparison of IDSs designed for IoT systems, with a focus on the types, techniques and features of these systems with respect to their adaptability to IoT-based smart environments

From: Intrusion detection systems for IoT-based smart environments: a survey

Reference Type Technique Features
Liu et al. (2011) [87] NIDS Machine learning & Signature model 1- Self-adaption 2- Self-learning
   Hybrid intrusion detection  
Kasinathan et al. (2013) [88] NIDS Rule model & Signature model 1- Detection of DoS attacks in 6LoWPAN
   Hybrid intrusion detection 2- Decreased false alarm rate
Kasinathan et al. (2013) [90] NIDS Rule model & Signature model 1- Monitoring of large networks 2- Light weight and scalability
   Hybrid intrusion detection 3- Detection of DoS attacks in 6LoWPAN
Jun and Chi (2014) [91] NIDS Rule model 1- Real-time detection
   Anomaly-based intrusion detection 2- High performance in real time
Krimmling and Peter (2014) [92] NIDS Machine learning & Signature model 1- Applicability to CoAP applications
   Hybrid intrusion detection 2- Light weight
Butun et al. (2015) [93] NIDS Statistical model & Rule model 1- Applicability to hierarchical WSNs
   Hybrid intrusion detection 2- Dependence on WSN clustering
Surendar and Umamakeswari (2016) [82] NIDS Protocol model 1- Detection of sinkhole attacks in 6LoWPAN
   Specification-based intrusion detection 2- QoS preservation 3- Isolation of malicious nodes
Le et al. (2016) [83] NIDS Protocol model 1- Energy efficiency 2- Detection of RPL attacks in 6LoWPAN
   Specification-based intrusion detection 3- Applicability to large-scale networks
Bostani and Sheikhan (2017) [84] NIDS Protocol model & Machine learning 1- Detection of RPL attacks in 6LoWPAN
   Hybrid intrusion detection 2- Real-time detection 3- Reduced number of communication messages
Garcia-Font et al. (2017) [95] NIDS Machine learning & Signature model 1- Applicability to WSNs
   Hybrid intrusion detection 2- Applicability to large-scale networks
Fu et al. (2017) [96] NIDS Protocol model & Signature model 1- Classification of attacks into categories
   Hybrid intrusion detection 2- Use of GUI tools
Deng et al. (2018) [97] NIDS Machine learning & Data mining 1- Light weight
   Hybrid intrusion detection 2- Improved detection efficiency with a low FPR
Amouri et al. (2018) [99] NIDS Protocol model & Machine learning 1- Low computational complexity
   Hybrid intrusion detection 2- Low resource requirements
Liu et al. (2018) [100] NIDS Machine learning & Data mining 1- Adaptability to high-dimensional spaces
   Hybrid intrusion detection 2- Reduced detection time 3- High accuracy on high-volume data
Abhishek et al. (2018) [101] NIDS Statistical model 1- Real-time detection
   Anomaly-based intrusion detection 2- Based on theoretical foundations with no need for training data
Oh et al. (2014) [102] HIDS Pattern matching 1- Reduced memory size requirements
   Misuse-based intrusion detection 2- Reduced processing workload 3- Increased speed
    4- Scalable performance for a large number of patterns
Summerville et al. (2015) [103] HIDS Payload model 1- Low latency 2- Ultralight weight
   Anomaly-based intrusion detection 3- High throughput in hardware or software implementation
Mohan et al. (2016) [37] HIDS Rule model & Signature model 1- Simplicity
   Hybrid intrusion detection 2- Self-learning
Arrignton et al. (2016) [104] HIDS Machine learning 1- High-efficiency monitoring
   Anomaly-based intrusion detection 2- Cancellation of environment noise
Gupta et al. (2013) [105] Hybrid IDS Machine learning 1- Real-time detection 2- Adaptability to wireless networks
   Anomaly-based intrusion detection 3- Ability to operate as both a NIDS and a HIDS
Raza et al. (2013) [106] Hybrid IDS Protocol model & Machine learning 1- Detection of RPL attacks in 6LoWPAN
   Hybrid intrusion detection 2- Real-time detection 3- Light weight 4- Energy efficiency
Khan and Herrmann (2017) [107] Hybrid IDS Protocol model 1- Light weight 2- Energy efficiency
   Anomaly-based intrusion detection 3- Applicability in healthcare environments