This paper, improves the Snort-IDS rules for Botnets detection and we analyze Botnets behaviors in three rules packet such as Botnets_attack_1.rules, Botnets_attack_2.rules, and Botnets_ attack_3. There are some existing rules which can detect Botnets. The Snort Intrusion Detection System (Snort-IDS) is the popular usage software protection of the network security in the world and utilizes the rules to match the data packets traffic. An organization should find the solutions to protect the data and network system to reduce the risk of the Botnets. The Botnets has become a serious problem in network security. The experimental results show that the MCG botnet has the ability of automatic grouping and antitracking. We refer to the botnet proposed in this paper as a multichannel automatic grouping botnet (MCG botnet) and conduct verification experiments using social networks and more than 2,000 docker nodes. The Diffie–Hellman key exchange method is leveraged for efficiently generating the unique group ID, thereby scaling up automatic bot grouping. The basic idea of the proposed approach is to establish a reliable and unsuspicious social network-based C&C channel capable of automatically grouping bots, wherein a group of bots have a unique ID that is against cross-group tracking. To solve this problem, this paper proposes an automated approach to group management of large-scale IoT bots. Specifically, once a bot is captured, the command and control (C&C) channel may be cracked and then tracked, potentially rendering more bots being discovered. The growing population, though making a botnet powerful, results in an increased risk of exposure. Because of the sheer volume and continual operation of many vulnerabilities (many users do not pay much attention to IoT update alerts and leave the configurations by default) of IoT devices, the population of an IoT botnet becomes increasingly tremendous.
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