IoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP

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dc.contributor.advisorHabibi Lashkari, Arash
dc.contributor.authorNiktabe, Sepideh
dc.date.accessioned2024-11-07T11:11:25Z
dc.date.available2024-11-07T11:11:25Z
dc.date.copyright2024-08-01
dc.date.issued2024-11-07
dc.date.updated2024-11-07T11:11:25Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThe proliferation of IoT devices has enhanced connectivity but exposed networks to new cyber threats, particularly from botnets. Detecting and identifying malicious data is critical for early threat detection, understanding botnet attack patterns, and deploying countermeasures. This research proposes an IoT Bot detection and identification profiling model using XAI. The proposed model introduces a novel feature selection techqnique with the XGBoost algorithm and a correlation-based feature selection technique to enhance efficiency. An optimized LSTM neural network enables accurate bot detection and identification, with hyperparameters selected using the Bayesian Optimization algorithm. SHAP analysis provides insightful individual and collective bot characteristic profiles. The model’s performance was evaluated using the augmented BCCC-Aposemat-Bot-IoT-24 dataset, built upon the Aposemat-Bot-IoT-23 dataset, and compared against established models assessed primarily on the same dataset in previous research. The results showed that the proposed model performed comparably to these models, with distinct advantages, including handling sequential and time-series data, managing imbalanced datasets, and providing explainable insights into botnet behavior. The model’s design also emphasizes computational efficiency, making it potentially suitable for deployment in resource-constrained environments.
dc.identifier.urihttps://hdl.handle.net/10315/42467
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsInternet of Things (IoT)
dc.subject.keywordsTransmission control protocol (TCP)
dc.subject.keywordsMalicious bot
dc.subject.keywordsMalicious profiling
dc.subject.keywordsAttack detection
dc.subject.keywordsNetwork security
dc.titleIoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP
dc.typeElectronic Thesis or Dissertation

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