This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.

As the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks to discern between regular network traffic and potential malicious attacks. This article proposes a new IDS based on a hybrid method of metaheuristic and deep learning techniques, namely, the flower pollination algorithm (FPA) and deep neural network (DNN), with an ensemble learning paradigm. To handle the problem of imbalance class distribution in intrusion datasets, a roughly-balanced (RB) Bagging strategy is utilized, where DNN models trained by FPA on a cost-sensitive fitness function are used as base learners. The RB Bagging strategy derives multiple RB training subsets from the original dataset and proper class weights are incorporated into the fitness function to attain unbiased DNN models. The performance of our IDS is evaluated using four commonly utilized public datasets, NSL-KDD, UNSW NB-15, CIC-IDS-2017, and BoT-IoT, in terms of different metrics, i.e., accuracy, precision, recall, and F1-score. The results demonstrate that our IDS outperforms existing ones in accurately detecting network intrusions with effective handling of class imbalance problem.

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible $100\times 100$ grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments.