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Table 1 Comparing related works: IDS for attacks on IoT routing

From: Clustering and reliability-driven mitigation of routing attacks in massive IoT systems

Related works
Targeted attack Work Approach Issues
Sinkhole Le et al. [11] Use of supernodes, finite machine state, RPL High positive rate compared to other IDSs for IoT in the literature.
  Cervantes et al. [7] Use of reputation and trust model in INTI, the designed system Neglect other types of attacks in IoT routing.
Selective forwarding Mathur et al. [8] Cryptography High positive and negative rates, and high energy consumption.
Sinkhole and Selective forwarding Sheikhan and Bostani [3] MapReduce, supervised and unsupervised machine learning Focuses only on static nodes and execution time of approximately 13 min.
  Khan and Herrmann [12] Reputation, trust management False positive and negative rates above 60%.
Other Adeilson et al.-2018 [13] IDS against DoS attacks Not specific to a type of DoS generating high positive and negative rates
  Bhatti et al.-2018 [14] IDS based on machine learning techniques for detecting network anomalies Detection not effective for all targeted attacks, and high positive and negative rates.
  Sonar et al. [15] Watchdog on hardware Limited to a IoT with few nodes and the use of a limiar constraints its effectiveness.
  Yang et al. [10] IDS based on watchdog for detecting false data injection on the network and uses a Bayesian spatio-temporal model Model yields high energy consumption, and the probabilistic testing applied results in high error rates.