Mobile Malware and Smart Device Security: Trends, Challenges and Solutions

This work is part of the research to study trends and challenges of cyber security to smart devices in smart homes. We have seen the development and demand for seamless interconnectivity of smart devices to provide various functionality and abilities to users. While these devices provide more features and functionality, they also introduce new risks and threats. Subsequently, current cyber security issues related to smart devices are discussed and analyzed. The paper begins with related background and motivation. We identified mobile malware as one of the main issue in the smart devices' security. In the near future, mobile smart device users can expect to see a striking increase in malware and notable advancements in malware-related attacks, particularly on the Android platform as the user base has grown exponentially. We discuss and analyzed mobile malware in details and identified challenges and future trends in this area. Then we propose and discuss an integrated security solution for cyber security in smart devices to tackle the issue

Machine learning based intrusion detection system for software defined networks

Software-Defined Networks (SDN) is an emerging area that promises to change the way we design, build, and operate network architecture. It tends to shift from traditional network architecture of proprietary based to open and programmable network architecture. However, this new innovative and improved technology also brings another security burden into the network architecture, with existing and emerging security threats. The network vulnerability has become more open to intruders: the focus is now shifted to a single point of failure where the central controller is a prime target. Therefore, integration of intrusion detection system (IDS) into the SDN architecture is essential to provide a network with attack countermeasure. The work designed and developed a virtual testbed that simulates the processes of the real network environment, where a star topology is created with hosts and servers connected to the OpenFlow OVS-switch. Signature-based Snort IDS is deployed for traffic monitoring and attack detection, by mirroring the traffic destine to the servers. The vulnerability assessment shows possible attacks threat exist in the network architecture and effectively contain by Snort IDS except for the few which the suggestion is made for possible mitigation. In order to provide scalable threat detection in the architecture, a flow-based IDS model is developed. A flow-based anomaly detection is implemented with machine learning to overcome the limitation of signature-based IDS. The results show positive improvement for detection of almost all the possible attacks in SDN environment with our pattern recognition of neural network for machine learning using our trained model with over 97% accuracy

Impact of Distributed Denial-of-Service Attack on Advanced Metering Infrastructure

The age of Internet of Things has brought in new challenges specifically in areas such as security. The evolution of classic power grids to smart grids is a prime example of how everything is now being connected to the Internet. With the power grid becoming smart, the information and communication systems supporting it is subject to both classical and emerging cyber-attacks. The article investigates the vulnerabilities caused by a distributed denial-of-service (DDoS) attack on the smart grid advanced metering infrastructure. Attack simulations have been conducted on a realistic electrical grid topology. The simulated network consisted of smart meters, power plant and utility server. Finally, the impact of large scale DDoS attacks on the distribution system’s reliability is discussed

COVID‐19 Pandemic Cybersecurity Issues

This paper studies the cybersecurity issues that have occurred during the coronavirus (COVID‐19) pandemic. During the pandemic, cyber criminals and Advanced Persistent Threat (APT) groups have taken advantage of targeting vulnerable people and systems. This paper emphasizes that there is a correlation between the pandemic and the increase in cyber‐attacks targeting sectors that are vulnerable. In addition, the growth in anxiety and fear due to the pandemic is increasing the success rate of cyber‐attacks. We also highlight that healthcare organizations are one of the main victims of cyber‐attacks during the pandemic. The pandemic has also raised the issue of cybersecurity in relation to the new normal of expecting staff to work from home (WFH), the possibility of state‐sponsored attacks, and increases in phishing and ransomware. We have also provided various practical approaches to reduce the risks of cyber‐attacks while WFH including mitigation of security risks related to healthcare. It is crucial that healthcare organizations improve protecting their important data and assets by implementing a comprehensive approach to cybersecurity

Development of smart grid testbed with low-cost hardware and software for cybersecurity research and education

Smart Grid, also known as the next generation of the power grid, is considered as a power infrastructure with advanced information and communication technologies (ICT) that will enhance the efficiency and reliability of power systems. For the essential benefits that come with Smart Grid, there are also security risks due to the complexity of advanced ICT utilized in the architecture of Smart Grid to interconnect a huge number of devices and subsystems. Cybersecurity is one of the emerging major threats in Smart Grid that needs to be considered as the attack surface increased. To prevent cyber-attacks, new techniques and methods need to be evaluated in a real-world environment or in a testbed. However, the costs for setting-up Smart Grid testbed is extensive. In this article, we focused on the development of a smart grid testbed with a low-cost hardware and software for cybersecurity research and education. As a case study, we evaluated the testbed with most common cyber-attack such as denial of service (DoS) attack. In addition, the testbed is a useful resource for cybersecurity research and education on different aspects of SCADA systems such as protocol implementation, and PLC programming

Energy-aware virtual machine consolidation for cloud data centers

One of the issues in virtual machine consolidation (VMC) in cloud data centers is categorizing different workloads to classify the state of physical servers. In this paper, we propose a new scheme of host's load categorization in energy-performance VMC framework to reduce energy consumption while meeting the quality of service (QoS) requirement. Specifically the under loaded hosts are classified into three further states, i.e., Under loaded, normal and critical by applying the under load detection algorithm. We also design overload detection and virtual machine (VM) selection policies. The simulation results show that the proposed policies outperform the existing policies in Cloud Sim in terms of both energy and service level agreements violation (SLAV) reduction

Smart streetlights in Smart City: a case study of Sheffield

Smart streetlights can be used to enhance public safety and well-being. However, not only it is one of the most draining structures in terms of electricity, but it is also economically straining to local government. Typically, many councils adopt a static or conventional approach to street lighting, this presents many inefficiencies as it does not take into account environmental factors such as light levels and traffic flows. This paper will present the utilities of a streetlights in Sheffield and how different councils tackle the issue by using different lighting schemes. Investigation of current implementations of information and communication technologies (ICT) such as Internet of Things (IoT) in streetlights will be necessary to understand different proposed models that are used in ‘smart’ street lighting infrastructure. Case studies from Doncaster and Edinburgh are explored as they are using similar technology and having a similar sized topology as Sheffield. To analyze different models, StreetlightSim, an open-source streetlight simulator, is used to present different lighting schemes. There will be four time-based schemes: Conventional, Dynadimmer, Chronosense and Part-Night which have varying capabilities that will be simulated to present a plethora of solutions for Sheffield’s street lighting problem. The results from the simulations showed mixed readings, the time-based schemes showed reliable data from StreetlightSim’s own evaluations, however its adaptive approach will need to be further analyzed to demonstrate its full capability

The enhancement of collaborative learning through integrated knowledge management systems: E-learning model

There are still a few educational platforms that apply a Knowledge Management System (KMS) concept in conducting its operational work. In addition, several obstacles associated with e-learning implementation trigger the in-effectiveness of collaborative learning. However, the concept of Knowledge Management (KM) from a Sharia perspective has significant implications for education systems. This research, therefore, explored the relevance of the Learning Management System (LMS), KM theory, and Sharia education perspective on the development of the Integrated Knowledge Management System (IKMS) Framework. The IKMS components and structures are literature reviewed and then qualitatively justified through the focus group discussion which involved some students, lectures, and experts from two Sharia-based Universities in Indonesia. To verify and test the framework, an IKMS-Edu system was developed by focusing on the adoption of a controlling agent system in the online discussion. Herein, filtering and summarization technology was embedded into IKMS-Edu towards a smart controlling agent. This agent adopted the operational work of IKMS-Edu framework leveraging in four constructs activities viz., knowledge creation and knowledge acquisition (construct 1), knowledge organization and knowledge storage (construct 2), knowledge dissemination and knowledge retrieval (construct 3), and knowledge evaluation and feedback (construct 4). To date, the statistical evaluation of the IKMS-Edu system’s acceptance is conducted by disseminating the questionnaires. The mean scores revealed 40.45% of the respondents strongly agreed, and 42.18% agreed on the proposed framework and prototype system thus the framework aided in performing the IKMS during the collaborative learning activities. As such, this evidence provides the strong support that IKMS-Edu significantly enhanced the effectiveness of collaborative learning by considering the Sharia values of trust, knowledge, virtue, psychosocial, and civilization development into knowledge management activities

Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020