# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 2 Related Work
### Machine Learning for Securing IoT--An Overview
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Attack** & **Countermeasure** & **Dataset for Model Development** & **Limitations/Gaps** \\ \hline
[12] & Protocol-specific attack & LGBM is leveraged to address protocol-specific attacks in RPL-based IoT & Self-generated & SN-inherited attacks are not considered \\ \hline
[14] & Protocol-specific and SN-inherited attacks & GAN-C is used to address rank, version, and hello flood attacks & IRAD & High processing and computational power required \\ \hline
[13] & SN-inherited attack & GRU-DL is used to counter hello flood attacks in RPL-based IoT & Self-generated & Protocol-specific attacks are not considered, DL methods require high processing and computation power \\ \hline
[49] & SN attacks & Generative DL using adversarial autoencoder and bidirectional generative adversarial network models & IoT-23 dataset & Protocol-specific attacks are not considered, SN-inherited attacks in RPL-based IoT setting are not considered \\ \hline
[50] & SN attacks & IoT device classification (binary and multiclass) using the logistic decision tree model which is based on logistic regression and decision tree methods & IoT device dataset & SN-inherited attacks in RPL-based IoT are not considered, protocol-specific attacks are not considered \\ \hline
[52] & SN attacks & Long short-term memory and gated recurrent unit-based DL framework for intrusion detection & CICIDS2017 dataset [53] & Protocol-specific attacks are not considered, SN-inherited attacks in RPL-based IoT setting are not considered \\ \hline
[54] & SN attacks & IDS for attack detection based on binary and multiclass DL-based classification using long short-term memory model & UNSW-NB15 and Bot-IoT datasets & SN-inherited attacks in RPL-based IoT are not considered, protocol-specific attacks are not considered \\ \hline
[55] & SN attacks & Graph-based botnet detection system for classification of attacks using Naive Bayes, decision and extra trees, random forest, AdaBoost, and k-Nearest Neighbors (k-NNs) & CTU-13 and IoT-23 datasets & Protocol-specific attacks are not considered, SN-inherited attacks in RPL-based IoT setting are not considered \\ \hline
[56] & SN attacks & DL recurrent-neural-network-based IDS and SL classifiers for attack classification & NSL-KDD dataset & SN-inherited attacks in RPL-based IoT are not considered, protocol-specific attacks are not considered, relatively old dataset is used \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent literature. Since PS-R attack is capable of altering the RPL components, while also affecting the network performance, forming routing loops, and disrupting the topology, it is considered as one of the gravest attacks. Therefore, PS-R attack is addressed in this research. Similarly, SN-W is also a highly damaging and detrimental attack which has a high potential to instigate other security attacks as well on an IoT network. Therefore, both of these attacks are addressed using ML approaches. The proposed ProSenAD model to handle these attacks is discussed in the next section, followed by results, comparison with benchmarks, and conclusions in the subsequent sections.