# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 3 Related Works  
[PERSON] et al. introduced a technique for detecting botnet attacks by analyzing packet length at the border router between physical and network devices, but they did not provide details on how they developed a normal behavior profile or how the method would perform on IoT nodes with limited resources [22].  
[PERSON] et al. proposed a new intrusion detection system called SVELTE, designed for IoT networks to detect routing attacks such as spoofing, Sinkhole, and Selective Forwarding. They implemented SVELTE in the Contiki OS and evaluated it, showing that it can detect all malicious nodes in simulated scenarios with some false alarms, while having minimal overhead that enables it to be deployed on constrained nodes with limited resources [23].  
[PERSON] et al. proposed a semi-supervised fuzzy learning-based distributed attack detection framework for IoT and evaluated it using the NSL-KDD dataset, which has similar dataset-related issues to those mentioned previously [24].  
To identify DDoS and DoS assaults against genuine IoT network traffic, [PERSON] et al. used an Artificial Neural Network (ANN). The suggested ANN model was tested against the simulated IoT network.  
[PERSON] et al. proposed a new approach to cybersecurity that uses deep learning to detect attacks in the Social Internet of Things (IoT). The paper compares the performance of the deep learning model with traditional machine learning approaches and evaluates a distributed attack detection system against a centralized detection system. The experiments show that the distributed attack detection system using deep learning outperforms centralized detection systems, and that the deep learning model is more effective in detecting attacks compared to shallow counterparts [25].  
To identify aberrant activity in various botnet attempts against the Domain Name System (DNS), Hyper Text Transfer Protocol (HTTP), and Message Queue Telemetry Transport, [PERSON] et al. suggested an ensemble of IDSs (MQTT) [26]. Their ensemble approaches are based on the AdaBoost learning approach, and they evaluated their approach using three machine learning techniques: Artificial Neural Networks (ANN), Decision Trees (DT), and Naive Bayes (NB) [26]. Performance overhead is impacted by the planned IDS.  
Using Artificial Neural Networks (ANN) to identify Distributed Denial of Service (DDoS/DoS) attacks, [PERSON] et al. presented a threat analysis of IoT devices. Using web packet traces for training, a multi-level perceptron, a form of supervised ANN, is evaluated on its capacity to prevent (DDoS/DoS) attacks [27]. The likelihood of implementing the suggested IDS in the IoT ecosystem with low-capacity devices was not taken into account by [PERSON] et al. Their testing revealed that the technology has a 99.4% accuracy rate for DDoS/DoS. However, no information on the dataset was offered.  
IDSs were suggested by [PERSON] et al. for 6 LoWPAN for the IoT to detect Sinkhole Attacks. Their IDS strategy combines anomaly detection with signatures, supported by a Support Vector Machine (SVM). Each IDS agent learns the SVM as part of their IDS's training process, and a majority voting decision is made to identify the infected nodes [28]. According to their simulation results, their IDS can detect sinkholes up to 92% of the time in a stationary situation, and 75% of the time in a mobile scenario. Their strategy has not been tested against other IoT attack types either.  
[PERSON] et al. combined a C5 classifier with a one-class Support Vector Machine classifier to create a hybrid Intrusion Detection System (HIDS). The Bot-IoT dataset, including realistic IoT network traffic and a variety of attacks, is used to evaluate the proposed HIDS [7]. Table 1 shows the summary of the proposed research on IDSs for IoT.