The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other reactive measures detect all types of threats. The majority of IDS employ features from various network layers. One issue is calculating a huge layered features set from an ad-hoc network. This research implements genetic algorithm (GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks. For attack detection, the reduced feature set was put to a fuzzy logic system (FLS). The performance of proposed model was compared with principal component analysis (PCA) and statistical parametric mapping (SPM). Network performance analysis like delay, packet dropping ratio, normalized overhead, packet delivery ratio, average energy consumption, throughput, and control overhead are evaluated and the IDS performance parameters like detection ratio, accuracy, and false alarm rate are evaluated for validation of the proposed model. The proposed model achieves 95.5% in detection ratio with 96.8% accuracy and produces very less false alarm rate (FAR) of 14% when compared with existing techniques.

Intrusion detection systems (IDS) can be used to detect irregularities in network traffic to improve network security and protect data and systems. From 2.4 times in 2018 to three times in 2023, the number of devices linked to IP networks is predicted to outnumber the total population of the world. In 2020, approximately 1.5 billion cyber-attacks on Internet of Things (IoT) devices have been reported. Classification of these attacks in the IoT network is the major objective of this research. This research proposes a hybrid machine learning model using Seagull Optimization Algorithm (SOA) and Extreme Learning Machine (ELM) classifier to classify and detect attacks in IoT networks. The CIC-IDS-2018 dataset is used in this work to evaluate the proposed model. The SOA is implemented for feature selection from the dataset, and the ELM is used to classify attacks from the selected features. The dataset has 80 features, in the proposed model used only 22 features with higher scores than the original dataset. The dataset is divided into 80% for training and 20% for testing. The proposed SOA-ELM model obtained 94.22% accuracy, 92.95% precision, 93.45% detection rate, and 91.26% f1-score.