ISSN : 2663-2187

A MACHINE LEARNING BOTNET DETECTION FEASIBILITY ANALYSIS

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Dr. Deevi HariKrishna Dr A.HANUMAT PRASAD
ยป doi: 10.33472/AFJBS.6.10.2024.4535-4450

Abstract

The security threat is one of the most pressing challenges in today's networks, and it is regarded as the most critical in the management of multimedia data. Bots are a type of security attack that targets a person or a group of nodes in a network, turning them into bots. A botnet is a network of bots that can be controlled by a bot master. These bots are programmed to disrupt network activity. It collects very sensitive data including bank account numbers and personal information. Botnets are now identified utilising an intrusion detection system (IDS), which efficiently monitors network activity in entities and companies on a regular basis. Bots create bogus or undesirable data, which is then passed on to all nodes in the network, lowering network efficiency. The botnet's primary goal is to assault as many devices as possible while also disseminating malicious programmes as much as possible. Botnet assaults infect many types of technology, and even the most basic internet security suites, firewalls, and antivirus software provide some defense. We offered dynamic analysis in advance, looking for signs of infection in behavioral analysis, as well as network and network traffic anomalies. Individual symptoms of botnet attacks are combined with network-level attacks

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