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dc.contributor.authorLefoane, Moemedi
dc.contributor.authorGhafir, Ibrahim
dc.contributor.authorKabir, Sohag
dc.contributor.authorAwan, Irfan U.
dc.date.accessioned2022-04-05T17:37:57Z
dc.date.accessioned2022-04-07T10:12:30Z
dc.date.available2022-04-05T17:37:57Z
dc.date.available2022-04-07T10:12:30Z
dc.date.issued2021-12
dc.identifier.citationLefoane M, Ghafir I, Kabir S and Awan IU (2021) Machine Learning for Botnet Detection: An Optimized Feature Selection Approach. The 5th International Conference on Future Networks & Distributed Systems (ICFNDS 2021). December 15–16, 2021. Dubai, United Arab Emirates. ACM, New York. 6 pages.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18862
dc.descriptionYesen_US
dc.description.abstractTechnological advancements have been evolving for so long, particularly Internet of Things (IoT) technology that has seen an increase in the number of connected devices surpass non IoT connections. It has unlocked a lot of potential across different organisational settings from healthcare, transportation, smart cities etc. Unfortunately, these advancements also mean that cybercriminals are constantly seeking new ways of exploiting vulnerabilities for malicious and illegal activities. IoT is a technology that presents a golden opportunity for botnet attacks that take advantage of a large number of IoT devices and use them to launch more powerful and sophisticated attacks such as Distributed Denial of Service (DDoS) attacks. This calls for more research geared towards the detection and mitigation of botnet attacks in IoT systems. This paper proposes a feature selection approach that identifies and removes less influential features as part of botnet attack detection method. The feature selection is based on the frequency of occurrence of the value counts in each of the features with respect to total instances. The effectiveness of the proposed approach is tested and evaluated on a standard IoT dataset. The results reveal that the proposed feature selection approach has improved the performance of the botnet attack detection method, in terms of True Positive Rate (TPR) and False Positive Rate (FPR). The proposed methodology provides 100% TPR, 0% FPR and 99.9976% F-score.en_US
dc.language.isoenen_US
dc.rights© 2021 Association for Computing Machinery. Reproduced in accordance with the publisher's self-archiving policy. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.en_US
dc.subjectBotnet detectionen_US
dc.subjectMachine learningen_US
dc.subjectCyber attacksen_US
dc.subjectInternet of Thingsen_US
dc.subjectNetwork securityen_US
dc.titleMachine Learning for Botnet Detection: An Optimized Feature Selection Approachen_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.1145/3508072.3508102
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-04-05T17:37:58Z
refterms.dateFOA2022-04-07T10:13:26Z
dc.openaccess.statusrestrictedAccessen_US


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