Cyber attackers are becoming smarter, and at the end of the day, many novel attacks are hosted in the cyber world. Security issues become more complex and critical when the number of services and subscribers increases due to advanced technologies. To ensure a secure environment, cyber professionals suggest reviewing the information security posture of the organization regularly via security experts, which is known as penetration testing. A pen tester executes a penetration test of an organization according to the frameworks and standardization guidelines. Security breaches of the system, loopholes in OS or applications, network vulnerabilities, and breaking data integration scopes are identified, and appropriate remediation is suggested by a pen tester team. The main aim of a penetration process is to fix the vulnerabilities prior to the attack in tangible and intangible resources. Firstly, this review work clarifies the penetration conception and is followed by the taxonomy of penetration domains, frameworks, standards, tools, and scoring methods. It performs a comparison study on the aforementioned items that develops guidelines for selecting an appropriate item set for the penetration process according to the demand of the organization. This paper ends with a constructive observation along with a discussion on recent penetration trends and the scope of future research.

The COVID-19 pandemic has significantly transformed the landscape of education delivery, leading to a surge in online teaching and learning methods, and consequently, increased Internet usage among students. These circumstances have created an environment that make them vulnerable to cybercrime due to the lack of cybersecurity awareness. This paper proposes a cyber-awareness program aimed at increasing students' knowledge of cybersecurity and their ability to manage the risk of cyber threats. The methodology employed consists of four phases: Initiation and Planning, Development of Modules, Implementation, and Evaluation. The results of descriptive and statistical analyses show an increase in participants' awareness of cyber security threats and risks following their participation in the program. In conclusion, this program has successfully achieved its objectives of enhancing cyber security awareness and promoting safe internet usage among participants. These findings suggest that similar programs can be utilized to increase cybersecurity awareness and promote safe internet usage among the students.

Wireless network devices can attain the required level of quality of service (QoS) and maintain connectivity even after detaching from a current point of access. This detachment (mobility) requires various mobility management (MM) mechanisms, which present numerous challenges due to the exponential growth of wireless devices and the demands of users. The network must be heterogeneous and dense to manage such a heightened escalation of network traffic, increased number of devices, and different types of user demands. Such factors will seriously challenge MM solutions, eventually making the networks non-feasible from the dependability, adaptability, extensibility, and power consumption points of view. Therefore, novel perspectives on MM mechanisms are desired for 5G networks and beyond. This paper introduces an innovative discussion of the functional requirements of MM mechanisms for advanced wireless networks. We present comprehensive arguments on whether the prevailing mechanisms perceived by standard bodies attempt to fulfill the stated requirements. We complete this discussion through innovative qualitative evaluation. We assess each of the discussed mechanisms in terms of their capability to fulfill the dependability, adaptability, extensibility, and power consumption benchmarks for upcoming MM schemes. Hereafter, we demonstrate the outcome and the identified gaps/challenges for the planning and deployment of 5G MM frameworks and beyond. Next, we present the capabilities and possible MM solutions to tackle the gaps/difficulties. We complete our discussion by proposing a 6G MM architecture based on defined parameters.

Scientific workflows consist of numerous tasks subject to constraints on data dependency. Effective workflow scheduling is perpetually necessary to efficiently utilize the provided resources to minimize workflow execution cost and time (makespan). Accordingly, cloud computing has emerged as a promising platform for scheduling scientific workflows. In this paper, level- and hierarchy-based scheduling approaches were proposed to address the problem of scheduling scientific workflow in the cloud. In the level-based approach, tasks are partitioned into a set of isolated groups in which available virtual machines (VMs) compete to execute the groups’ tasks. Accordingly, based on a utility function, a task will be assigned to the VM that will achieve the highest utility by executing this task. The hierarchy-based approach employs a look-ahead approach, in which the partitioning of the workflow tasks is performed by considering the entire structure of the workflow, whereby the objective is to reduce the data dependency between the obtained groups. Additionally, in the hierarchy-based approach, a fair-share strategy is employed to determine the share (number of VMs) that will be assigned to each group of tasks. Dividing the available VMs based on the computational requirements of the task groups provides the hierarchy-based approach the advantage of further utilizing the VMs usage. The results show that, on average, both approaches improve the execution time and cost by 27% compared to the benchmarked algorithms.

Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document

The rapid proliferation of the internet of things (IoT) has introduced significant security and privacy challenges. As IoT devices often have limited computational power and memory, they are highly vulnerable to cyber threats. Traditional intrusion detection systems (IDS) struggle to operate efficiently in these constrained environments, necessitating more adaptive and optimized security solutions. To address these challenges, this study proposes an innovative IDS model, MSAMLP, which combines the moth search algorithm (MSA) with a multilayer perceptron (MLP) classifier. The objective is to enhance the classification accuracy of malicious and benign network traffic while maintaining computational efficiency. The model was evaluated using two widely recognized intrusion detection datasets, benchmarking its performance against existing IDS approaches. Experimental results indicate that MSAMLP outperforms conventional classification models, achieving high accuracy, improved detection rates, and reduced false alarm rates. Its adaptive learning capability ensures better anomaly detection in dynamic IoT environments. In conclusion, the proposed MSAMLP model demonstrates superior performance in securing IoT networks, offering an effective solution to mitigate evolving cyber threats. This research contributes to the advancement of IoT security by introducing a robust and scalable intrusion detection approach.