The Internet of Things (IoT) has become an integral part of our daily life as it is growing in many fields, such as engineering, e-health, smart homes, smart buildings, agriculture, weather forecasting, etc. However, the growing number of IoT devices and their weak configuration raise many security challenges such as designing protocols to protect these devices from various types of attacks such as using them as bots for DDoS attacks on target servers. In order to protect IoT devices from enslavement as bots in a home environment, we develop a lightweight security model consisting of various security countermeasures. The working mechanism of the proposed security model is presented in a two-part experimental scenario. Firstly, we describe the working mechanism of how an attacker infects an IoT device and then spreads the infection to the entire network. Secondly, we propose a set of mechanisms consisting of filtration, detection of abnormal traffic generated from IoT devices, screening, and publishing the abnormal traffic patterns to the rest of the home routers on the network. We tested the proposed scheme by infecting an IoT device with malicious code. The infected device then infects the rest of the IoT devices in its network and launches a DDoS attack by receiving attack-triggering commands from the botmaster. Finally, the proposed detection mechanism is used to detect the abnormal traffic and block the connection of infected devices in the network. The results reveal that the proposed system blocks abnormal traffic if the packets from an IoT device exceeded a threshold of 50 packets. Similarly, the network packet statistics show that, in the event of an unwanted situation, the detection mechanism runs smoothly and avoids any possible delays in the network.

Since cyber attacks are increasing and evolving rapidly, the need to enhance cyber-security defense is crucial. A cyber incident exercise model is a learning technique to provide knowledge about cyber security to enhance a security team’s incident response. In this research work, we proposed a cyber incident model to handle real-time security attacks in various scenarios. The proposed model consisted of three teams: (1) the black team, (2) the red team, and (3) the blue team. The black team was a group of instructors responsible for setting up the environment. They had to educate the red and blue teams about cyber security and train them on facing cyber attacks. Once the training period was completed, the members were divided into two teams to conduct a cyber-security competition in a cyber game scenario. Each of the two teams performed a different task. The red team was the offensive team that was responsible for launching cyber-security attacks. The blue team was the defensive team that was responsible for countering attacks and minimizing the damage caused by attackers; they had to conduct both cyber-security configuration and incident handling. During the scenario, the black team was responsible for guiding and monitoring both the red and the blue teams, ensuring the rules were applied throughout the competition. At the end of the competition, the members of each team changed with each other to make sure every team member was using the knowledge they gained from the training period and every participant was evaluated impartially. Finally, we showed the security team’s offensive and defensive skills via the red team and the blue team, respectively.

This research explores the integration of cyber gamification and adaptive learning techniques in cybersecurity education, specifically enhancing cybersecurity teams' learning experience and efficacy. The primary contribution of this research is developing an innovative educational platform that customises learning content based on individual and cyber security experience team performance. This platform introduces gamified elements, such as interactive challenges, role-playing scenarios, and reward systems, to cultivate a highly engaging and motivational learning environment. Empirical results from the deployment of this system indicate a substantial improvement in both individual and collective cybersecurity knowledge and skills. The platform's adaptability ensures the continuous relevance of the learning content, aligning with the latest developments and trends in the cybersecurity field. This approach presents a significant shift from traditional cybersecurity education methods, proposing a more engaged and interactive cybersecurity gaming and education model that promises to better prepare the team's knowledge for the evolving landscape of cyber awareness, threats, and security measures.

In heterogeneous networks (HetNets), the vertical handover (VHO) is an essential process for mobile users (MUs) aiming to secure ubiquitous connectivity and maintain the highest quality of service (QoS) across various types of radio access technology (RAT), such as wireless fidelity (Wi-Fi), the global system for mobile communication (GSM), and the universal mobile telecommunications system (UMTS). In the literature, many recent VHO research works have been proposed in which a number of critical issues still arise when performing seamless vertical handover, including VHO packet loss, VHO delay, and VHO throughput. Moreover, the security aspect of triggering VHO has not been carefully considered, particularly when an MU intends to use a payment application. In this paper, we present a comprehensive performance evaluation for a new secure VHO algorithm, taking into account all the above issues, for co-located Wi-Fi and UMTS networks. The simulation results are compared with the media independent handover (MIH) IEEE 802.21 standard, which facilitates the seamless transfer of connection among heterogeneous networks. We show that the proposed algorithm outperforms the MIH standard in terms of VHO delay, VHO packet loss, and VHO throughput in providing uninterrupted connection to security-constrained applications.

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box.