Table 1.
Survey of the intrusion detection techniques in MANET.
| Novelist | Year | Algorithm | Consequences |
|---|---|---|---|
| Cheng et al.21 | 2019 | PPVF-RSU-CSP | An initiative is undertaken to integrate cloud storage with VANET, and a PPVF is introduced for cloud-assisted VANET |
| Prasad et al.22 | 2019 | Machine learning | In the case of wormhole attack detection in ad hoc networks, the accuracy is 93.12%. |
| Jhanjhi et al.23 | 2020 | Machine learning | Making a recommendation for a hybrid RPL protocol that uses machine learning to counter attacks utilizing rank and wormholes |
| Wang et al.24 | 2020 | FD-WCFRSNPS | According to the results of five different experiments, the FD-WCFRSNPS that was suggested is both efficient and effective. |
| Singh et al.25 | 2020 | Artificial neural network | Monitoring wireless sensor networks for the presence of wormhole attacks |
| Srilakshmi et al.26 | 2021 | Hybrid reactive search and bat algorithm | It is necessary to estimate both the harm detection rate and the node energy in order to determine the lifespan of the node |
| Goyal et al.27 | 2021 | CDMA-based security | Wormhole attack in underwater wireless sensor networks. The proposed strategy enhances energy efficiency in comparison to existing methods |
| Wang et al.28 | 2021 | Approaches to service selection that is quick and dependable | Our proposed approach has more reliability and reduced time expenditure compared to prior implementations, as indicated by the findings. |
| Ni et al.29 | 2021 | Fault detection method relies on TDMA | The proposed methodology decreases resource use by 88-90% in comparison to the chain-TDMA strategy. |
| Amutha et al.30 | 2021 | Clustering techniques | An examination of WSN clustering of three categories: classical, optimization, and machine learning techniques. |
| Jiang et al.31 | 2021 | System for adaptive cosite fading channels | The analytical modeling and experiments validate the theoretical reasoning as both sound and beneficial. |
| Ahmadi et al.32 | 2021 | New KATP network for adaptation | The state-of-the-art efficiency of your model has been validated by tests on a variety of typical samples and clinical data. |
| Tami and Lim et al.33 | 2021 | Ensemble learning | The success value of classification algorithms is statistically studied using metrics such as accuracy, FPR, Matthews coefficients, and AUC. |
| Chen et al.34 | 2021 | RPPTD | A secure and private method of discovering the truth |
| Sultana et al.35 | 2021 | Considering bottleneck intermediate node | The data indicate that the oversight of esteemed packet loss nodes by IDS algorithms constitutes a substantial problem that adversely affects network efficiency. |
| Sultanuddin et al.36 | 2022 | Decision tree and Reinforcement learning | Proposed model to select a dependable and trustworthy nearby node for data transmission in the MANET. |
| Majid Hamid Ali et al.37 | 2022 | Improved Whale Optimization Algorithm-SVM | It was capable of identifying multiple forms of cyber-attacks, including Distributed Denial of Service (DDoS) attacks, password guessing attempts and phishing attacks. |
| Jayakanth et al.38 | 2023 | Equalized Multi-routing protocol | It engages with multipath routing strategies to rectify significant flaws. The author employed a trust table to identify malicious nodes. |
| Edwin Singh et al.39 | 2023 | Fuzzy Extreme Learning Machine | For all circumstances of the Green Smart Transportation network, the attacks are classified after the PCA features |
| Mohamad et al.40 | 2023 | Deep Learning ANN | The application of ANN techniques in the Internet of Things introduces a detection system for rank and wormhole attacks |
| Manoj Gupta et al.41 | 2024 | Machine learning and deep learning model | The author devised an advanced, self-learning protection system proficient in detecting and countering Blackhole Attacks in MANET |
| Mohammadi et al.42 | 2024 | Federated learning with AI | It enables secure data sharing without accessing local data, which ensures fair benefit redistribution |
| Hassan et al.43 | 2024 | Advanced Machine learning | Analyzing MANET characteristics and detailing black and gray hole attacks and detection techniques |