Security is critical to the success of software, particularly in today's fast-paced, technology-driven environment. It ensures that data, code, and services maintain their CIA (Confidentiality, Integrity, and Availability). This is only possible if security is taken into account at all stages of the SDLC (Software Development Life Cycle). Various approaches to software quality have been developed, such as CMMI (Capability maturity model integration). However, there exists no explicit solution for incorporating security into all phases of SDLC. One of the major causes of pervasive vulnerabilities is a failure to prioritize security. Even the most proactive companies use the "patch and penetrate" strategy, in which security is accessed once the job is completed. Increased cost, time overrun, not integrating testing and input in SDLC, usage of third-party tools and components, and lack of knowledge are all reasons for not paying attention to the security angle during the SDLC, despite the fact that secure software development is essential for business continuity and survival in today's ICT world. There is a need to implement best practices in SDLC to address security at all levels. To fill this gap, we have provided a detailed overview of secure software development practices while taking care of project costs and deadlines. We proposed a secure SDLC framework based on the identified practices, which integrates the best security practices in various SDLC phases. A mathematical model is used to validate the proposed framework. A case study and findings show that the proposed system aids in the integration of security best practices into the overall SDLC, resulting in more secure applications.

Internet of Things (IoT) devices work mainly in wireless mediums; requiring different Intrusion Detection System (IDS) kind of solutions to leverage 802.11 header information for intrusion detection. Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks. This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods, IDS techniques, IDS placement strategies, and traffic data analysis techniques. This paper's main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions. Specifically, the Knowledge Discovery in Databases (KDD) Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network (WN). The paper starts with a review of various intrusion detection techniques, data collection methods and placement methods. The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment. Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities. So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment. The main wireless environments to look into would be Wireless Sensor Networks (WSN), Mobile Ad Hoc Networks (MANET) and IoT as this are the future trends and a lot of attacks have been targeted into these networks. So it is very crucial to design an IDS specifically to target on the wireless networks.

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles.

One of the leading healthcare concerns worldwide is the aging population. Aged patients require more significant healthcare resources because they are more likely to have chronic diseases that result in higher healthcare expenses. The design and implementation of e-health solutions, which offer patients mobile services to assist and enhance their treatment based on monitoring specific physiological data, is one of the key achievements in medical information technology. In the last few decades, there have been tremendous advancements in healthcare technology regarding mobility, size, speed, and communication. However, the critical drawback of today’s e-Health monitoring systems is that patients are confined to smart rooms and beds with monitoring gadgets. Such tracking is not widespread due to chronic patients’ mobility, privacy, and flexibility issues. Further, health monitoring devices that are fastened to a patient’s body do not give any analysis or advice. To improve the health monitoring process, a multi-agent-based system for health monitoring is provided in this study, which entails a group of intelligent agents that gather patient data, reason together, and propose actions to patients and medical professionals in a mobile context. A multi-agent-based framework presented in this study is evaluated through a case study. The results show that the proposed system provides an efficient health monitoring system for chronic, aged, and remote patients. Further, the proposed approach outperforms the existing mHealth system, allowing for timely health facilities for remote patients using 5G technology.

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