Optimizing ML classifiers for superior intrusion detection in resource-constrained smart homes

• Purpose of ML in Intrusion Detection: The paper discusses the use of machine learning (ML) to develop efficient intrusion detection systems (IDS) for smart home environments, which are vulnerable to attacks due to their limited hardware resources. • ML Algorithms Used: The study focuses on enhancing intrusion detection using two ML algorithms: Extra Tree Classification (ETC) and Linear Discriminant Analysis Classification (LDAC). • Optimization Algorithms: Two optimization algorithms, the Rider Optimization Algorithm (ROA) and the Aquila Optimizer (AO), are employed to enhance the predictive power of the base models. • Development of Hybrid Models: The integration of these optimizers with the base models leads to the creation of innovative hybrid models: ETAO (ETC with AO), ETRO (ETC with ROA), LDRO (LDAC with ROA), and LDAO (LDAC with AO). • Performance Results: The ETAO model achieved the highest performance in accuracy (0.984) and precision (0.987), while the LDAO model had the weakest performance in precision (0.888). Machine learning (ML) has indeed become essential to the enhancement of intrusion detection systems in different scenarios. It has become a critical barrier against various sophisticated cyber threats. Security vulnerabilities pose special challenges to smart homes, given that devices, sensors, and network connections make the ecosystem highly connected. Such systems improve convenience and efficiency but are generally based on hardware with limited processing power and storage capacity. Therefore, these are prone to a variety of potential attacks. To do so, an effective IDS would have to identify known and evolving threats at all the various vulnerable points, starting from network interfaces down to individual devices. This work tackles these challenges by designing and optimizing ML models that offer reliable intrusion detection tailored for resource-constrained smart home environments. This work argues for intrusion prediction in smart homes using the Extra Tree Classification (ETC) and Linear Discriminant Analysis Classification (LDAC). To strengthen these base models' predictive capability, this paper considered the use of 2 optimization algorithms: the Rider Optimization Algorithm (ROA) and the Aquila Optimizer (AO). The optimizers were integrated strategically with the base models for improved accuracy, thus giving rise to new hybrid models. The combination of ETC with AO provides the ETAO model, while ETC with ROA gives the ETRO model. In equal measure, the LDAC model combined with ROA gives the LDRO model, while that of the LDAC model combined with AO gives the LDAO model. Basically, these hybrid models aim to ensure better performance from a prediction perspective. In the test section, the ETAO model was head and shoulders above the others in this metric, with an excellent value of 0.984, while for the ETRO model, the second-best performing model achieved 0.975. Later on, looking at the entire section, the precision metric again scored highest with the ETAO model at 0.987, while the weakest performance was from the LDAO model, which had a value of 0.888.