# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 2 Literature Review
The outcomes of the experiment demonstrated that the ANN outperforms conventional machine learning methods, achieving an impressive area under the ROC curve of 0.9689. A hybrid approach proposed in [32] combined the definition of fuzzy logic with the learning ability of neural networks to detect routing attacks in WSNs. The success of this solution depends on the quality and quantity of training data, the creation of fuzzy rules, the neural network architecture and the starting point. To ensure that the system effectively detects and mitigates attacks in a dynamic network environment, regular maintenance and development are required. A comprehensive overview of the proposed mechanisms discussed in this section is given in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Author Name** & **Attack Type** & **Prevention** & **Benefits** \\ \hline
2021 [13] & [PERSON] and [PERSON] & Multi-layer intrusion detection in WSNs & Naive Bayes classifier, cloud-based detection, random forest classifier & Improved network security, enhanced true-positive rate (TPR), true-negative rate (TNR), false-positive rate (FPR), and false-negative rate (FNR), increased accuracy. \\ \hline
2022 [14] & [PERSON] and [PERSON] & Black-hole detection using O-LEADM & On-demand link and energy-aware dynamic multipath routing system for MANETs & Enhanced black-hole node detection, differentiation between packet loss due to congestion and packet loss due to malicious nodes. \\ \hline
2022 [15] & [PERSON], \& & DDoS detection & Machine learning-classification algorithms & Detection of flooding, gray-hole, and black-hole attacks, improved accuracy and speed. \\ \hline
2022 [16] & [PERSON] and [PERSON] & Grey-hole detection and prevention & Network data collection, grey-hole detection, grey-hole prevention, and reduction prevention gray-hole & Efficient gray-hole detection, prevention, and reduction mechanism. \\ \hline  & & & Prevention, gray-hole reduction & \\ \hline
2023 [17] & [PERSON] & Sinkhole attack in MWSNs & Lightweight encryption-based technique, hybrid encryption algorithm, secure key exchange & Counteraction of sinkhole attacks, minimized energy consumption, enhanced security. \\ \hline
2021 [19] & [PERSON], [PERSON] & Cluster-based DoS detection & Survivillance nodes, monitoring and anomaly detection mechanism, energy threshold & Utilizing cluster-head nodes for reputation and trust route calculation. \\ \hline
2020 [20] & [PERSON], [PERSON], [PERSON] & Trust-based routing protocol & Trust-based low-power routing protocol (TEESR) & Enhanced security, authorization mechanism, focus on external attacks. \\ \hline
2021 [21] & [PERSON], [PERSON], [PERSON] & Trust-aware secure routing protocol & Trust-aware secure routing protocol (TSRP) & Prevention of wormhole, selective forwarding, and black-hole attacks. High performance in packet loss rate. \\ \hline
2021 [22] & [PERSON], [PERSON] & Black-hole attack & Intrusion detection, digital signatures & Mitigation of black-hole attacks in AODV protocol, improved QoS metrics. \\ \hline  & & & K-nearest neighbors (KNN) algorithm, beta distribution, mental logic, fuzzy inference, trust server & Efficient black hole attack identification, enhanced network parameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of literature.