TY - EJOU AU - Duhayyim, Mesfer Al AU - Alzahrani, Jaber S. AU - Mengash, Hanan Abdullah AU - Alnfiai, Mrim M. AU - Marzouk, Radwa AU - Mohammed, Gouse Pasha AU - Rizwanullah, Mohammed AU - Abdelmageed, Amgad Atta TI - Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection T2 - Computer Systems Science and Engineering PY - 2023 VL - 46 IS - 2 SN - AB - The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches. KW - Deep learning; internet of things; cloud computing; feature selection; intrusion detection DO - 10.32604/csse.2023.034137