Botnet detection in internet of things (IOT) by swarm intelligence (SI) algorithm

• Published in: AIP conference proceedings Vol. 2587; no. 1
• Main Authors: Thota, Swapna, Menaka, D.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 21.11.2023
• Subjects: Algorithms; Cybersecurity; Data collection; End users; Exchanging; False alarms; Internet of Things; Malware; Software; Swarm intelligence
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0150966

Internet of Things is well known as IoT is changing human life smarter, comfort that support to communicate and exchange of data by ‘things’ and ‘devices’ through Internet. Sensors as well as communication devices are connect, communicate and co-coordinately exchange of data that are nearby surroundings. Internet of Things(IoT) is a next generation of Internet. It is well known as IoT. IoT is a combination of ‘things’ and ‘devices’ that are connect, communicate and exchange of data through internet. IoT collect the data from surroundings and send these collected data to Internet for further processing to take decision by end users. Due to tremendous growth of IoT, that makes world smart, economical and comfortable life but it has also added a lot of challenges in dealing with its security and privacy. Botnet cause DDoS attacks to perform various actions like steal data, edit data content and make this data to unusable finally empower attacker to access the devices and its network connections to put IoT service as un-usable. The Botnet is malware that disrupt the IoT completely and hamper the IoT communication network. For protecting these IoT devices from Botnet is a challenging task and also to detect and solve these Botnet problems by applying efficient software tools or efficient algorithms for effective management of IoT network Swarm Intelligence algorithms are in nature having the characteristics of self learning, self-adaptation as per surroundings and collective behavior to complete a particular task. In this paper by applying swarm intelligence algorithm IMOPSO shows better perform than that of MPOSO, NSGA-II algorithms with respect to evaluation measures like False Alarm Rate(FAR), Detection Rate(DR), G-mean, and AUC of Internet of Things(IoT) to detect Botnet in Internet of Things.