Distributed Denial of Service (DDoS) attack is an attack that causes the shutting down of a network. It is usually directed at a service, and disruption of that service can be detected through methods such as penetration. This paper aims to evaluate diverse machine learning (ML) classifiers concerning their performance when classified DDoS attacks using CICDDoS2019 dataset, which is a complete benchmark dataset for the study of DDoS attacks. The paper investigates effectiveness using nine different ML classifiers: XGBoost, Random Forest, AdaBoost, Gradient Boosting, Decision Tree, Stochastic Gradient Descent (SGD), CatBoost, LightGBM, and Support Vector Machine (SVM). Evaluation of each classifier conducted on exhaustive performance indicators accuracy, precision, recall, F-measure. The results conclude that XG Boost and Light GBM are superior on all ML classifiers of detection accuracy, precision, sensitivity, and F-measure. The findings will assist this research to indicate the best possible ML classifiers to enhance the network security of a region and mitigate DDoS impacts. The study adds to the increasing body of knowledge in leveraging ML for cybersecurity applications.

The rapid expansion of medical data, characterized by its complex high-dimensional attributes, presents numerous promising opportunities and substantial challenges in healthcare analytics. Adopting effective feature selection techniques is essential to take advantage of the potential of such data. This research presents a modified algorithm called (mDA), which is the hybrid algorithm between the Evolutionary Population Dynamics and the Dragonfly Algorithm. This method combines Evolutionary Population Dynamics’s strength with the Dragonfly Algorithm’s flexible capabilities, offering a robust evolutionary machine learning approach specifically designed for medical data analysis. By integrating the dynamic population modeling of Evolutionary Population Dynamics with the adaptive search techniques of Dragonfly Algorithm, the proposed mDA significantly improves accuracy, reduces the number of features, and obtains the minimum average of the fitness scores. Comparative experiments conducted on seven diverse medical datasets against other established algorithms confirm the superior performance of the proposed mDA, establishing it as a valuable approach in examining complex medical data.

The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy and a hard-RIME puncture mechanism, along with an improved positive greedy selection mechanism, to resist getting trapped in local optima and enhance its overall search capabilities. The article also introduces Binary modified RIME (mRIME), a binary adaptation of the RIME algorithm to address the unique challenges posed by FS problems, which typically involve binary search spaces. Four different types of transfer functions (TFs) were selected for FS issues, and their efficacy was investigated for global optimization using CEC2011 and CEC2017 and FS tasks related to disease diagnosis. The results of the proposed mRIME were tested on ten reliable optimization algorithms. The advanced RIME architecture demonstrated superior performance in global optimization and FS tasks, providing an effective solution to complex optimization problems in various domains.

Eliminating security threats on the Internet of Things (IoT) requires recognizing threat attacks. IoT and its implementations are currently the most common scientific field. When it comes to real-world implementations, IoT's attributes, on the one hand, make it simple to apply, but on the other hand, they expose it to cyber-attacks. Denial of Service (DoS) attack is a type of threat that is now widespread in the field of IoT. Its primary goal is to stop or damage service or capability on a target. Conventional Intrusion Detection Systems (IDS) are no longer sufficient for detecting these sophisticated attacks with unpredictable behaviors. Machine learning (ML)--based intrusion detection does not need a massive list of expected activities or a variety of threat signatures to create detection rules. This study aims to evaluate different ML classifiers for network intrusion detection that focus on DoS attacks in the IoT environment to determine the best ML classifier that can detect the DoS attack. The XGBoost, Decision Tree (DT), Gaussian Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) ML classifiers are used to evaluate the DoS attack. The UNSW-NB15 dataset was used for this study. The obtained accuracy rate for XGboost was 98.92%, SVM 98.62%, Gaussian NB 83.75%, LR 97.74%, RF 99.48%, and DT 99.16%. where the precision rate for XGboost, SVM, Gaussian NB, LR, RF, and DT was 98.40%, 98.29%, 77.50%, 97.14%, 99.21%, and 99.12%, respectively. The sensitivity rate for XGboost, SVM, Gaussian NB, LR, RF, and DT was 99.29%, 98.76%, 91.87%, 98.06%, 99.69%, and 99.08%, respectively. The results show that the RF classifier outperformed other classifiers in terms of Accuracy, Precision, and Sensitivity.

A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO’s behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO.

A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.

Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.

Feature Selection (FS) techniques extract the most recognizable features for improving the performance of classification methods for medical applications. In this paper, two intelligent wrapper FS approaches based on a new metaheuristic algorithm named the Snake Optimizer (SO) are introduced. The binary SO, called BSO, is built based on an S-shape transform function to handle the binary discrete values in the FS domain. To improve the exploration of the search space by BSO, three evolutionary crossover operators (i.e., one-point crossover, two-point crossover, and uniform crossover) are incorporated and controlled by a switch probability. The two newly developed FS algorithms, BSO and BSO-CV, are implemented and assessed on a real-world COVID-19 dataset and 23 disease benchmark datasets. According to the experimental results, the improved BSO-CV significantly outperformed the standard BSO in terms of accuracy and running time in 17 datasets. Furthermore, it shrinks the COVID-19 dataset’s dimension by 89% as opposed to the BSO’s 79%. Moreover, the adopted operator on BSO-CV improved the balance between exploitation and exploration capabilities in the standard BSO, particularly in searching and converging toward optimal solutions. The BSO-CV was compared against the most recent wrapper-based FS methods; namely, the hyperlearning binary dragonfly algorithm (HLBDA), the binary moth flame optimization with Lévy flight (LBMFO-V3), the coronavirus herd immunity optimizer with greedy crossover operator (CHIO-GC), as well as four filter methods with an accuracy of more than 90% in most benchmark datasets. These optimistic results reveal the great potential of BSO-CV in reliably searching the feature space.

The surge in cyber-attacks has driven demand for robust Intrusion detection systems (IDSs) to protect underlying data and sustain availability of network services. Detecting and classifying multiple type of attacks requires robust machine learning approaches that can analyze network traffic and take appropriate measures. Traffic data usually consists of redundant, irrelevant, and noisy information, which could have a negative influence on the model performance. In this paper, we propose an improved bio-inspired meta-heuristic algorithm for efficient detection and classification of multi-stage attacks. The proposed model uses a one-versus-all sub-model based technique to deal with the multi-class classification problem. Each sub-model employs an enhanced Harris Hawk optimization with extreme learning machine (ELM) as the base classifier. This hierarchy produces the best subset of features per attack, along with optimized ELM's weights, which can improve the detection rate significantly. The proposed technique was tested against various meta-heuristic algorithms and multi-class classifiers using the UNSWNB-15 dataset. In seven different types of attacks, experimental results outperformed other existing methods in terms of decreasing the crossover-error rate and obtaining the best values for the G-mean measure.

The growth of data creates more analysis and mining challenges related to speed and accuracy. Feature selection (FS) is an optimization problem used as a preprocessing phase to reduce the data dimensionality while obtaining the best classification accuracy. FS removes redundant and irrelevant features and preserves the best informative features. Various meta-heuristic optimization algorithms were employed in the literature to solve the FS problem. This paper proposes an improved Harris hawk optimization algorithm called (IHHO) to find the optimal feature set for classification purposes in a wrapper-based environment. Three main improvements are obtained in the binary version of HHO. The first improvement is to speed up the convergence, which is implemented using the most informative features in population initialization. Both filter-based and wrapper-based techniques are used during the initialization phase. The second one is to ensure the global and local search and avoid trapping into local optima using the X-shaped transfer function. While the third one is using the extreme learning machine as the base classifier to guide the searching process, speed up the convergence, and improve the accuracy of the FS process. The proposed model was evaluated using 18 well-known UCI benchmarks and compared with traditional HHO, particle swarm optimization, gray wolf optimizer, grasshopper optimization algorithm, and five standard filter-based techniques. The experiment results prove the superior performance of the IHHO compared to other algorithms and methods presented in the literature.

Cyber-attacks and unauthorized application usage have increased due to the extensive use of Internet services and applications over computer networks, posing a threat to the service’s availability and consumers’ privacy. A network Intrusion Detection System (IDS) aims to detect aberrant traffic behavior that firewalls cannot detect. In IDSs, dimension reduction using the feature selection strategy has been shown to be more efficient. By reducing the data dimension and eliminating irrelevant and noisy data, several bio-inspired algorithms have been employed to improve the performance of an IDS. This paper discusses a modified bio-inspired algorithm, which is the Grey Wolf Optimization algorithm (GWO), that enhances the efficacy of the IDS in detecting both normal and anomalous traffic in the network. The main improvements cover the smart initialization phase that combines the filter and wrapper approaches to ensure that the informative features will be included in early iterations. In addition, we adopted a high-speed classification method, the Extreme Learning Machine (ELM), and used the modified GWO to tune the ELM’s parameters. The proposed technique was tested against various meta-heuristic algorithms using the UNSWNB-15 dataset. Because the generic attack is the most common attack type in the dataset, the primary goal of this paper was to detect generic attacks in network traffic. The proposed model outperformed other methods in minimizing the crossover error rate and false positive rate to less than 30%. Furthermore, it obtained the best results with 81%, 78%, and 84% for the accuracy, F1-score, and G-mean measures, respectively.

As COVID-19 pandemic emerged, quick decisions in response to sudden emergence and rapid spread around the world were required. Strict actions deployed to tackle the COVID-19 pandemic are likely to have prevented millions of additional infections and reduce the number of fatalities during the pandemic. The actions varied from one country or territory to another based on the threat control management (TCM) strategy such as preventing, avoiding, mitigating, and accepting. The TCM involved emergency measures such as isolation, restriction on traveling, closing of nonessential businesses, physical distancing, lockdown and quarantine. Thus, this study introduces a generic dynamic framework that a country can follow to reduce the effects of COVID-19 on the number of infected people and fatalities. Samples of information and data about countries were reviewed, collected, and analyzed. The countries were classified based on the collected data and the curve representing the numbers of infections into countries; with green (flattened the curve and winning), orange (need more actions), and red (failing) colors. The analyses indicate that most of the countries deployed strict actions and applied TCM with preventing and avoiding strategies were winning countries, apparently, avoiding is the best TCM based on the values of the performance indicators. In conclusion, the generic dynamic framework can be implemented to study the effects of the TCM on the number of infected patients and fatalities caused by the COVID-19. Also, strict actions in response to the pandemic are promising to prevent millions of additional infections and reducing the number of fatalities during the pandemic.

Maximum Flow Problem (MFP) is considered as one of several famous problems in directed graphs. Many researchers studied MFP and its applications to solve problems using different techniques. One of the most popular algorithms that are employed to solve MFP is Ford-Fulkerson algorithm. However, this algorithm has long run time when it comes to application with large data size. For this reason, this study presents a parallel whale optimization (PWO) algorithm to get maximum flow in a weighted directed graph. The PWO algorithm is implemented and tested on datasets with different sizes. The PWO algorithm achieved up to 3.79 speedup on a machine with 4 processors.

Social media has played a significant role in marketing and advertising. Monitoring the attitude of customers and analyzing their written sentiments to evaluate their opinions toward a particular product, topic or situation becomes essential to improve the product quality and customer services. Due to the importance of sentiment analysis (SA), a plethora of tools and systems have been developed for analyzing the polarities of people's sentiments. However, SA is a difficult task, especially when dealing with massive data resources. Feature selection (FS) algorithms are needed for the machine learning (ML) process to reduce the high dimensionality space. In this paper, we propose an enhancement of a bio-inspired optimizer, called the salp swarm algorithm (SSA) designed for feature selection to solve the problem of Arabic sentiment analysis. Our proposed algorithm operates in two phases: The first phase employs a filtering technique based on the information gain (IG) metric to reduce the number of features. The second phase employs a wrapper technique which combines the basic SSA optimizer with four variants of S-shaped transfer functions. Experimental results show that the SSA combined with the S-shaped transfer functions outperformed the particle swarms optimizer (PSO) and the grey wolf optimizer (GWO) in term of classification accuracy.

Task scheduling is considered as one of the most critical problems in cloud computing environment. The main target of task scheduling includes scheduling jobs on virtual machines as well as improves performance. This study employed Grey Wolf Optimization (GWO) algorithm with modifications on the fitness function by making it handles multi-objectives in single fitness; the makespan and cost are the objectives included in the fitness in order to solve task scheduling problem. The main target of this technique is to reduce both cost and makespan. CloudSim tool is used to evaluate the objectives of the proposed method. The simulation results showed that the proposed method (Modified Grey Wolf Optimizer - MGWO) has better performance than both the traditional Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) with makespan based fitness in terms of makespan, cost and degree of imbalance.

The 0/1 Knapsack (KP) is a combinatorial optimization problem that can be solved using various optimization algorithms.Ant Colony System (ACS) is one of these algorithms that is operated iteratively and converged emphatically to a matured solution.The convergence of the ACS depends mainly on the heuristic patterns that are used to update the pheromone trails throughout the optimization cycles.Although, ACS has significant advantages, it suffers from a slow convergence, as the pheromones, which are used to initiate the searching process are initialized randomly at the beginning.In this paper, a new heuristic pattern is proposed to speed up the convergence of ACS with 0/1 KP.The proposed heuristic enforces an order-critical item selection.As such, the proposed heuristic depends on considering the profit added by each item, as similar to the existing heuristics, besides the order of item selection.Accordingly, the proposed heuristic allows the items that are added at the end to get more value in order to be considered in the beginning of the next round.As such, with each cycle, the selected items are varied substantially and the pheromones are vastly updated in order to avoid long trapping with the initial values that are initialized randomly.The experiments showed that the proposed heuristic is converged more rapidly compared to the existing heuristics by reducing up to 30% of the cycles required to reach the optimal solution using difficult 0/1 KP datasets.Accordingly, the times required for convergence have been reduced significantly in the proposed work compared to the time required by the existing algorithms.

Requirement engineering is the base phase of any software project, since this phase is concerned about requirements identification, processing and manipulation. The main source of these requirements is the project stakeholders with considering the project constraints and limitation. Number of requirement is varying for each project, so the requirements prioritization term comes for prioritizing the order of execution for software requirements according to the stakeholder's opinions and decisions. Various proposed optimization algorithms are employed to solve optimization problems; recently whale optimization (WO) algorithm is proposed in 2016 by Mirjalili which mimics the main characteristic of humpback whales which is the foraging method that is called bubble-net technique. On the other hand Grey wolf optimization (GWO) algorithm was proposed in 2014 in order to solve optimization problems by imitating the grey wolves hunting behavior. In this paper, a Hybrid approach based on Whale and Grey wolf optimization algorithms (WGW) is proposed by combining the advantages of each algorithm in order to prioritize the software requirements. Moreover, the data set that used in this paper is RALIC which a real software project’s requirements is in order to evaluate the proposed method. Thus, the proposed method shows 91% accuracy of requirements prioritization comparing with RALIC data sat.

Requirement prioritization is one of the most important approach in the process of requirement engineering due to use it in order to prioritize the execution sort of requirements with taking into account the viewpoints of stakeholders. Thus, in this study, grey wolf optimization (GWO) algorithm is applied in order to prioritize the requirements of a software project. GWO imitates the hunting behavior of grey wolves in nature. Which distinct from others that it has dominant leadership hierarchy which contains four main types; alpha, beta delta and omega wolves. In this paper, a proposed algorithm is presented to prioritize the requirements into ordered list. Furthermore, it is compared and evaluated with analytical hierarchy process (AHP) technique in terms of average running time and dataset size. The findings display that the RP-GWO performs better than AHP mechanism by approximately (30%).

Requirements prioritization is considered as one of the most important approaches in the requirement engineering process. Requirements prioritization is used to define the ordering or schedule for executing requirement based on their priority or importance with respect to stakeholders’ viewpoints. Many prioritization techniques for requirement have been proposed by researchers, and there is no single technique can be used for all projects types. In this paper we give an overview of the requirement process and requirement prioritization concept. We also present the most popular techniques used to prioritize the software project requirements and a compression between these techniques. On the other hand, we spot the light on the importance of involving the non-functional requirements prioritization because of the great effects of non-functional on project success and quality; some approaches that used in prioritize non-functional requirements are discussed in this paper, in addition a general model is proposed based on reviewing the prioritization techniques in order to suggests a best suited technique for specific projects according to decision makers parameters.