The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security.

By identifying different kinds of attacks and application misuse that firewalls normally aren't able to identify, network intrusion detection systems (IDS) are intended to keep computer networks safe. When creating a network intrusion detection system, feature selection techniques are crucial. Several bionic meta-heuristic algorithms are used to quickly categorize network traffic as problematic or normal, then decrease features to demonstrate higher accuracy. Thus, in order to detect frequent attacks, this research proposes a hybrid model of network intrusion detection system (IDS) based on an algorithm inspired by a hybrid bionic element. There are two goals for the suggested model. The first step is to minimize the number of features that are chosen in Network IDS. By combining biosensing metaheuristics with hybrid models, this objective is accomplished. The algorithms used in this chapter are particle swarm optimization (PSO), multiverse optimizer (MVO), grey wolf optimization (GWO), moth flame optimization (MFO), firefly algorithm (FFA), whale optimization algorithm (WOA), bat algorithm (BAT), genetic bee colony (GBC) algorithm, artificial bee colony algorithm (ABC), fish swarm algorithm (FSA), cat swarm optimization (CSO), artificial algae algorithm (AAA), elephant herd optimization (EHO), cuckoo search optimization algorithm (CSOA), lion optimization algorithm (LOA), and cuttlefish algorithm (CFA) algorithm. Using machine learning classifiers, the second objective is to identify frequent attacks. SVM (support vector machine), C4.5 (J48) decision trees, and RF (random forest) classifiers are used to accomplish this purpose. Thus, the goal of the suggested model is to pinpoint frequent attacks. The data indicates that J48 is the top classifier when it comes to model building time when compared to SVM and RF. The data indicates that when it came to feature reduction for classification, the MVO-BAT model decreased the features to 24, whereas the MFO-WOA and FFA-GWO models lowered the accuracy, sensitivity, and F-measure of all features to 15. The accuracy, sensitivity, and F-measure of each feature are the same for every classifier.

The mobile ad hoc network is basically a self-configurable network scheme in which there is no fixed infrastructure available. Because of the regular mobility and dynamic topology, the network is imposed with several issues such as energy and security. In order to surpass these issues, the existing scenario proposed Multivariate Auto regressive models (MAR) and hybrid ACO-MGA algorithms for the cluster head selection and optimal path selection correspondingly. But the present mechanism has not entirely dealt with the performance issues associated with security by taking the shortest path into consideration. In order to resolve this issue, the proposed technique proposed Fuzzy aided ACO algorithm, utilized for optimal path selection along with lesser energy consumption. In this work, Fuzzy Relevance Degree algorithm is utilized for cluster head (CH) selection. Any node in the cluster, which has more power and signal strength, obtained from the RS will be chosen to bea CH. For establishing the secure means of transmission, trust aware model is employed. Fuzzy aided ACO isfocused over the selection of the optimal paths. The path information by ants will be provided to FIS (Fuzzy Interference system) for computing the score values of the available path, and on the basis of this score value from the FIS system, the optimal paths will be chosen having minimal hop counts. At last, the packets are transmitted along this path. This new system attains a better performance with respect to packet delivery ratio, bandwidth, end to end delay, energy consumption, throughput and network lifetime when compared with the already available techniques.