## 7 Open Challenges and Future Directions  
This section provides insights regarding the open challenges in this domain of research that can be addressed by the research community as future research works. The study in this work opens following new research directions as detailed below. * **Evaluation of Lightweight approaches in real-world environments:** Throughout the study, it is identified that the majority of existing lightweight solutions have not been evaluated in real-world IoT network environments. Testing of such IDS models in real-world or testbed environments is the area that requires more attention from the research community for the development of efficient, lightweight IDS. A few IDS models proposed by [PERSON] et al. [116] and [PERSON] et al. [117] have already initiated some efforts by employing their model in the real environment to evaluate its suitability and performance within the IoT ecosystems. However, a limited number of approaches are based on real-world network traffic evaluation. Subsequently, this can be considered as a potential direction for adoption in future research works. * **Hybrid lightweight IDS:** it is observed that existing research works either adopt feature selection or a collaborative approach for developing lightweight IDS solutions. The earlier builds a more resource efficient classification model, while the later distributes the workload over different nodes, hence reducing the workload per node. A proper merger of both techniques can have great potential to devise more optimized and better-performing lightweight IDS solutions as compared to utilizing a solo approach. Such a hybrid approach is an open area of investigation that may realize a significant reduction in resource consumption, deeming the approach suitable for IoT end devices as being the least equipped devices over the IoT ecosystem. * **Optimization of ML algorithms**: Machine learning algorithms rely heavily on hyper-parameters, which significantly influence the model's accuracy and performance. In the context of IDS, these parameters need to be carefully tuned to minimize false alarms and maximize detection accuracy. However, in complex environments like IoT, where devices generate enormous amounts of heterogeneous data, the selection of optimal hyperparameters becomes increasingly challenging. Given the dynamic and distributed nature of IoT systems, efficient hyper-parameter optimization is essential to ensure that ML models adapt and perform well in such environments. For that, swarm intelligence and other meta-heuristic optimization techniques can be investigated for tuning the hyper-parameters in IDS, as their application in IoT-based environments is still largely unexplored. The unique characteristics of IoT, including resource constraints, decentralized data, and real-time processing demands, will present new challenges and opportunities for parameter optimization. For that, algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Programming (GP) can be investigated to search for optimal parameter values in a highly efficient manner. * **Adoption of embedded techniques:** Embedded feature selection techniques are known to have the potential to generate higher accuracy rates as compared to filter-based feature selection techniques. However, the embedded techniques integrate the feature selection process along with the learning algorithm, which in-turn limits the flexibility in the selection of the learning algorithms, as not all machine learning algorithms support embedded techniques. However, the assessment of such machine learning algorithms that support embedded techniques is an area worth investigating for their suitability to be deployed over resource-constraint IoT devices. Additionally, the training time required to prepare embedded feature selection based classifiers should be taken into future considerations for suitability assessment to time-sensitive IoT applications.