An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

This paper extensively reviews and analyses Light Fidelity (LiFi), a new technology that uses light to transmit data as a high-speed wireless connection system from a wide spectrum of domains. An in-depth analysis and classifications of pertinent research areas for LiFi networks are presented in this paper. The various aspects constituting this paper include a detailed literature review, proposed classifications, and statistics, which further is deliberated to encompass applications, system architecture, system components, advantages, and disadvantages. LiFi and other technologies are compared, multi-user access techniques used in LiFi networks are investigated and open issues are addressed in detail. The paper is concluded with a comprehensive taxonomy of literature comparison that has served as the basis of the proposed open issues and research trends.

Since light fidelity (LiFi) and wireless fidelity (WiFi) do not interfere with one another, a LiFi/WiFi hybrid network may provide superior performance to existing wireless options. With many users and constant changes, a network can easily become overloaded, leading to slowdowns and fluctuations in data transfer speeds. Access point assignment (APA) is required with the increase of users, which can negatively impact the system performance and quality-of-service (QoS) due to mobility and line-of-sight (LOS) blockage. Many variables could influence the APA process; these variables are considered as criteria, such as the network capacity, the degree of blockage, the speed of the connected user, etc. Unlike conditional APA methods, recent studies have considered treating these variables as “evaluation criteria”. Considering these criteria can offer better and more accurate results, eventually enhancing the APA process and QoS. However, the variety of these criteria, the conflict amongst them, their weights (importance), and priority have not been addressed so far. Moreover, treating the criteria equally might result in inaccurate outcomes. Therefore, to solve this issue, it is essential to investigate the impact of each criterion on the APA process. In this work, a multicriteria decision-making (MCDM) problem is formulated to determine a network-level selection for each user over a period of time The decision problem is modeled as a hierarchy that fragments a problem into a hierarchy of simple and small subproblems, and the selection of the AP network among various alternatives is a considered as an MCDM problem. Based on the previous works, we are not aware of any previous research attempts using MCDM methods in the LiFi research area for network selection. Therefore, this work proposes an access point assignment framework using an MCDM approach for users in a hybrid LiFi/WiFi network. The experiment was conducted based on four phases: Five criteria were identified and evaluated with eleven APs (alternatives). The outcome of this phase was used to build the decision matrix and an MCDM was developed and built based on user mobility and blockages with various scenarios using all the criteria; The analytic hierarchy process (AHP) was employed to identify the criterion of the subjective weights of each criterion and to determine the degree of importance supported by experts’ judgement. Determining the weights in the AHP process considered various investigations, including the consistency ratio (CR) and the AHP consensus indicator, which is calculated using the rank-based maximum likelihood method (RGMM) and Shannon entropy techniques. The VIekriteri-Jumsko KOmpromisno Rangiranje (VIKOR) method is adopted in the selection of the optimal AP for the proper selection of whether a LiFi or WiFi AP must serve the users. The integrated AHP–VIKOR was effective for solving the APA and was the best solution based on using weighted criteria simultaneously. Moreover, the ranking outcomes of the developed integrated AHP–VIKOR approach were evaluated using sensitivity analysis. The result of this work takes the APA for hybrid LiFi networks to a new perspective.

Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numerical optimisation problems. This algorithm imitates the symbiotic associations (mutualism, commensalism, and parasitism stages) displayed by organisms in an ecosystem. Despite the improvements made with the discrete symbiotic organism search (DSOS) algorithm, it still becomes trapped in local optima due to the large size of the values of the makespan and response time. The local search space of the DSOS is diversified by substituting the best value with any candidate in the population at the mutualism phase of the DSOS algorithm, which makes it worthy for use in task scheduling problems in the cloud. Thus, the eDSOS strategy converges faster when the search space is larger or more prominent due to diversification. The CloudSim simulator was used to conduct the experiment, and the simulation results show that the proposed eDSOS was able to produce a solution with a good quality when compared with that of the DSOS. Lastly, we analysed the proposed strategy by using a two-sample t-test, which revealed that the performance of eDSOS was of significance compared to the benchmark strategy (DSOS), particularly for large search spaces. The percentage improvements were 26.23% for the makespan and 63.34% for the response time.