{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:24:09Z","timestamp":1753885449134,"version":"3.41.2"},"reference-count":42,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Info. Know. Mgmt."],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:p> One of the most dangerous threats to computer networks is the use of botnets, which can seriously harm systems and steal private data. They are remote-controlled networks of compromised computers that an individual or group of individuals is using for malicious purposes. These infected computers are frequently called \u201cbots\u201d or \u201czombies\u201d. A wide variety of malicious activities, including the distribution of malware and credential theft, can be carried out using botnets. The CTU-13 dataset is a collection of network traffic information that includes examples of various botnet types. Using this, our study compares the abilities of decision trees, random forests, 1D convolutional neural networks, and a proposed system based on long short-term memory and residual neural networks to detect botnets. According to our findings, the suggested system performs better than every other algorithm, achieving a higher accuracy rate. Our suggested system has the ability to precisely identify botnet traffic patterns, which can assist organisations in proactively preventing botnet attacks. <\/jats:p>","DOI":"10.1142\/s0219649224500862","type":"journal-article","created":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T02:19:43Z","timestamp":1723342783000},"source":"Crossref","is-referenced-by-count":0,"title":["Detection and Classification of Network Traffic in Bot Network Using Deep Learning"],"prefix":"10.1142","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1729-8109","authenticated-orcid":false,"given":"K.","family":"Srinarayani","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5465-6986","authenticated-orcid":false,"given":"B.","family":"Padmavathi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8483-6037","authenticated-orcid":false,"given":"Kavitha","family":"Datchanamoorthy","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1095-9015","authenticated-orcid":false,"given":"T.","family":"Saraswathi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Easwari Engineering College, Ramapuram, Chennai 600089, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9765-3645","authenticated-orcid":false,"given":"S.","family":"Maheswari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8505-3138","authenticated-orcid":false,"given":"R. 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