The new generation of security threats has been promoted by real-time applications, where several users develop new ways to communicate on the internet via web applications. Structured Query Language injection Attacks (SQLiAs) is one of the major threats to web application security. Here, unauthorised users usually gain access to the database via web applications. Despite the giant strides made in the detection and prevention of SQLiAs by several researchers, an ideal approach is still far from over as most existing techniques still require improvement, especially in the area of addressing the weak characterisation of input vectors which often leads to low prediction accuracy. To deal with this concern, this paper put forward a hybrid optimised Logistic Regression (LR) model with Improved Term Frequency Inverse Document-Frequency (ITFIDF-LR). To show the effectiveness of the proposed approach, attack datasets is used and evaluated using selected performance metrics, i.e., accuracy, recall, specificity and False Positive Rate. The experimental results via simulation when compared with the benchmarked techniques, achieved performance record of 0.99781 for accuracy, recall and F1-score as well as 0.99782, 0.99409 and 0.00591 for precision, specificity and False Positive Rate (FPR) respectively. This is an indication that the proposed approach is efficient and when deployed is capable of detecting SQLiA on web applications.

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Cloud computing is a consumable technology of our time that allow sharing of resources (e.g. virtual machines, storage, bandwidth) to meet the exponential demand of cloud end-users [1−4]. Three service models associated with cloud computing environment include software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS). The SaaS layer of cloud computing allows cloud customers to run applications remotely, where application delivery is carried out via the Internet and manage by a third-party vendor. The cloud customers often interact with SaaS layer to get their task submitted to the IaaS layer (e.g. datacenter) and later receives its processed results via the SaaS layer. *Author for correspondenceThe SaaS layer function with the support of PaaS layer, providing interactive mechanisms for both cloud customers and service providers [5] while PaaS allow cost-efficient development and deployment of applications [6,7]. On the other hand, the IaaS layer provides services for cloud customers in terms of infrastructure (e.g. virtual machine) as a service. The IaaS provide a pool of resources of varied types that can be leased by cloud customers according to their computing requirements.Currently, due to escalating number of end-users accessing the cloud services, providing efficient scheduling to meet their QoS expectations has become a greater concern. Although, high-level research has been conducted in unveiling the supremacy of metaheuristic techniques toward mitigating these concerns [8], however, the metaheuristics are attributed to global and local

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

The growing number of customers that are requesting computation-based-resources to meet the increasing demand of resource hungry applications have spark a greater challenge on how effective can scheduling can be carried out at the cloud datacenters. Recent advancement in the uses of metaheuristics techniques are promising approach in scheduling resources to hungry applications, but however, are limited in their performances due to issues like premature convergence. To overcome this concern with the aim to provide an effective scheduling, we propose a non-preemptive Hybrid Cat Swarm Optimization Scheme (HCSOS) to serve as an ideal solution. In the proposed scheme, orthogonal Taguchi approach is incorporated to overcome premature convergence, and minimizes local and global imbalance, while Pareto dominant strategy is used for providing customers with the option of selecting their service preferences. The results of the simulation on CloudSim tool show that our proposed scheme compared to the benchmarked schemes can achieve a minimum total execution time and cost (with up to 42.87%, 35.47%, 25.49% and 38.62%, 35.32%, 25.56% reduction). We further unveiled that a statistical analysis based on 95% confidence interval shows our proposed HCSOS scheme is remarkable in term of efficiency.