Mobile ad hoc networks (MANETs) are considered as decentralized networks, which can communicate without pre-existing infrastructure. Owning to utilization of open medium access and dynamically changing network topology, MANETs are vulnerable to different types of attacks such as blackhole attack, gray hole attack, Sybil attack, rushing attack, jellyfish attack, wormhole attack (WHA), byzantine attack, selfishness attack, and network partition attack. Out of these, worm hole attack is the most common and severe attack that substantially undermines the performance of the network and disrupts the most routing protocols. In the past two decades, numerous researchers have explored the number of techniques to detect and mitigate the effect of WHAs to ensure the safe operation of wireless networks. Hence, in this article, we mainly focus on the WHAs and present the different state of art methods, which have been employed in previous years to discern WHA in wireless networks. The existing WHA detection techniques are lacking due to usage of additional hardware, higher delay, and consumption of higher energy. Round trip time (RTT) based detection methods are showing better results as they do not require additional hardware. Machine learning (ML) techniques can also be applied to ad-hoc network for anomaly detection and has a great influence in future; therefore, ML techniques are also analyzed for WHA detection in this article. SVM technique is mostly used by the researchers for outstanding results. It has been analyzed that hybrid approach which uses the traditional detection technique and ML technique are showing better results for WHA detection. Finally, we have identified the areas where further research can be focused so that we can apply the WHA detection methods for larger topological area for more flexibility and accurate results.
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