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

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

NTFS 2022: Authenticity in teaching and assessment

The world of education is constantly evolving and so it needs to provide context to what is being delivered at each session. This is more so for complex topics within the cyber science domain that evolves and changes every second

Teaching French as a Foreign Language (FFL) in the primary schools of the Nigerian Refugee Camp in Minawao, Mayo-Tsanaga Department/Far-North Region of Cameroon

The reflection on the particular conditions of teaching French as a Foreign Language (FFL) and the development of the professional skills of the teachers involved are relatively recent fields of research in Cameroon. The purpose of this study is to describe and understand the motives for teaching French as a foreign language to Nigerian refugee pupils at the primary schools of Minawao Camp in Mayo-Tsanaga Department/Far-North Region of Cameroon. The information gathered through semi-structured interviews with teachers (n=17) in relation to their initial and continuous training, teaching contexts and teaching practices, allowed us to note that the teachers who teach FFL in the primary schools of Minawao Camp are not sufficiently trained or equipped to teach FFL. The description of their teaching practices in the classroom reveals that they still confuse traditional teaching in an "ordinary classroom" with that of FFL. The incompatibility of terms of reference with the curriculumbeing implemented, the particular intercultural contexts of their students, makes the teaching process of FFL difficult and unstable. Teaching FFL imposes challenges on teachers. To this end, starting from the theory of the foundations of convergent didactics, it is necessary to proceed to the development of the cultural competence of the teachers in order to adapt them to the different types of interventions, audiences and teaching situations of the FFL

Detecting ransomware using process behavior analysis

Ransomware attacks are one of the biggest and attractive threats in cyber security today. Anti-virus software's are often inefficient against zero-day malware and ransomware attacks, important network infections could result in a large amount of data loss. Such attacks are also becoming more dynamic and able to change their signatures - hence creating an arms race situation. This study investigates the relationship between a process behavior and its nature, in order to determine whether it is ransomware or not. The paper aim is to see if using this method will help the evading malicious software's and use as a self-defense mechanism using machine learning that emulates the human immune system. The analysis was conducted on 7 ransomware, 41 benign software, and 34 malware samples. The results show that we are able to distinguish between ransomware and benign applications, with a low false-positive and false-negative rate

User-centred and context-aware identity management in mobile ad-hoc networks

The emergent notion of ubiquitous computing makes it possible for mobile devices to communicate and provide services via networks connected in an ad-hoc manner. These have resulted in the proliferation of wireless technologies such as Mobile Ad-hoc Networks (MANets), which offer attractive solutions for services that need flexible setup as well as dynamic and low cost wireless connectivity. However, the growing trend outlined above also raises serious concerns over Identity Management (IM) due to a dramatic increase in identity theft. The problem is even greater in service-oriented architectures, where partial identities are sprinkled across many services and users have no control over such identities. In this thesis, we review some issues of contextual computing, its implications and usage within pervasive environments. To tackle the above problems, it is essential to allow users to have control over their own identities in MANet environments. So far, the development of such identity control remains a significant challenge for the research community. The main focus of this thesis is on the area of identity management in MANets and emergency situations by using context-awareness and user-centricity together with its security issues and implications. Context- awareness allows us to make use of partial identities as a way of user identity protection and node identification. User-centricity is aimed at putting users in control of their partial identities, policies and rules for privacy protection. These principles help us to propose an innovative, easy-to-use identity management framework for MANets. The framework makes the flow of partial identities explicit; gives users control over such identities based on their respective situations and contexts, and creates a balance between convenience and privacy. The thesis presents our proposed framework, its development and lab results/evaluations, and outlines possible future work to improve the framework

MIAEC: Missing data imputation based on the evidence Chain

© 2013 IEEE. Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis. Therefore, it is necessary to implement the imputation of missing value in the phase of data preprocessing to reduce the possibility of data missing as a result of human error and operations. The performances of existing imputation approaches of missing value cannot satisfy the analysis requirements due to its low accuracy and poor stability, especially the rapid decreasing imputation accuracy with the increasing rate of missing data. In this paper, we propose a novel missing value imputation algorithm based on the evidence chain (MIAEC), which first mines all relevant evidence of missing values in each data tuple and then combines this relevant evidence to build the evidence chain for further estimation of missing values. To extend MIAEC for large-scale data processing, we apply the map-reduce programming model to realize the distribution and parallelization of MIAEC. Experimental results show that the proposed approach can provide higher imputation accuracy compared with the missing data imputation algorithm based on naive Bayes, the mode imputation algorithm, and the proposed missing data imputation algorithm based on K-nearest neighbor. MIAEC has higher imputation accuracy and its imputation accuracy is also assured with the increasing rate of missing value or the position change of missing value. MIAEC is also proved to be suitable for the distributed computing platform and can achieve an ideal speedup ratio