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Since the first idea of a collider was patented by Rolf Wideröe (1902–1996) in 1943 and the first $e^{+}{e}^{-}$ circular collider, AdA, was successfully principle-proved in LAL, Orsay, France in 1963, different kinds of colliders have been built in the past and planned for the future. In this paper, we will make a rather complete and brief review of the history of different colliders and give a perspective for the future.

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10Gbps worked quite well.

As education field has become online due to covid in both schools and colleges, e-learning security has become an important issue. An e-Learning framework provides a collection of online services that are helpful for the learners, resource persons and others who are involved in enhancing the management and delivery of education to all sections of people. The two most important aspects of an e-Learning system are better search of learning resources and the secure authentication between the learner and the trainer. This chapter introduces two novel methods: (i) optimization of Learning Object (LO) search based on learners’ characteristics, (ii) secure authentication of trainers and learners using visual cryptography. Storage and delivery of optimal resources that are well suited for individual learner is always a challenging task. To find the best learning objects, an enhanced attribute-based Ant Colony Optimization (ACO) algorithm that provides flexibility for the learners based on learner characteristics is proposed. A novel visual cryptography-based technique with kite-based partition technique is designed to perform file sharing and blockchain-based secure authentication and verification of valid learners is proposed for the framework. Several measures like match ratio, relevancy factor, and heuristic values show the efficiency of the proposed ACO search technique in the context of an e-Learning framework.

In recent scenario, major areas of application such as forensics, government and commercial domain demands higher accuracy and reliability to identify individuals in real-time. Biometrics is the automated measurement of ubiquitous and unique characteristic of an individual which can distinguish an individual from the mass. The design of a multi-biometrics system in real-time applications is an exciting research due to the limitations of unimodal biometric systems. The risk of attack to Multibiometric framework keeps on varying from time to time and hence adaptive systems are needed which can adapt to these varying security levels. Metaheuristic is an advanced level technique designed to choose a heuristic which results into a promising solution for the optimization issue with inadequate data. Hybrid metaheuristic is essentially a technique that results from the combination of a metaheuristic with other techniques of optimization. Hybrid metaheuristic approaches have progressed significantly in managing the real life optimization issues effectively. The application of metaheuristics techniques like Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Hybrid GA-PSO and Quadrature Programming (QP) Optimization for Support Vector Machines in developing real time multi-biometric authentication systems are discussed in this chapter.