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@article{160829,
        author = {Sajidha Thabassum B and Chaitanya Patel G M and Darshan Krishna Keshan and Tegginamathada Basavananda},
        title = {Detection And Mitigation Of Black Hole And Gray Hole Attack},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {1403-1408},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160829},
        abstract = {A Mobile Ad Hoc Network (MANET) is a grouping of wireless nodes that are interacting to each other without demanding any fixed or physical architecture thanks to wireless technology and the latest breakthroughs in a mobile or dynamic devices. These networks are vulnerable to so many attacks, along with Black Hole Attack (BHA), Gray Hole Attack (GHA), and various others due to the highly dynamic nature of a distributed system structure. The identification and protection of certain assaults, such as Gray Hole Attack or Black Hole Attack nodes, have been the attention of several researchers. This review introduces a new scheme for defending against dual attacks in BHA and GHA, which incorporates use of Artificial Neural Networks (ANNs) as a supervised neural algorithm and an optimization techniques called the Artificial Bee Colony (ABC). This method aims to improve the security of BHA and GHA systems by implementing advanced machine learning techniques While there is limited research on protecting Mobile Ad-hoc Networks (MANETs) from dual threats, this approach offers a promising solution. The system's performance has improved as a result of the best and most suitable nodes being picked to relay data packets.
For the network setup and design, NS2 (Network Simulator Version 2) software on linux platform is used and the same software is used to simulate proposed Ad hoc communication system.},
        keywords = {ABC, ANN, AODV, Black Hole Attack, Gray Hole Attack,    MANET},
        month = {},
        }