The comparison between internal determinat of conventional and Islamic banking profitability (Maybank and Bank Islam) / Nurul Hidayah Ab Rahman

Type of bank can be dividing by two which are conventional bank and Islamic bank. For this research study, the researcher chooses Malayan Banking Berhad (Maybank) as a conventional bank and Bank Islam Malaysia Berhad (BIMB) as a Islamic bank. The aim of this study was to examine the factors that influencing each bank profitability. There are 3 factors that been studied, which were Operating Expenses (OE), Credit Risk (CR) and Bank Siza (BS). This study also was conduct to identify which bank are the most preferred factors that influence to gain a profit. The period of study is from first quarter 2006 until fourth quarter 2010. Findings of the study show that Maybank as a conventional bank will got a great impact by all the factor chosen compare to Bank Islam. The research methodology use for this study is by using the secondary data from Quarterly Financial Statement report. Meanwhile, in analyzing the data the Statistical Procedure of Social System (SPSS) is being used in order to transfer the data into output. As a conclusion from the study, the finding of this study gives suggestion to the both bank to improve their internal factors to gain more profi

A visualization approach to analyse android smartphone data

This study aims to design and develop an interactive system that can visualize evidence collected from Android smartphone data. This project is developing to support forensic investigator in investigating the security incidents particularly involving Android smartphone forensic data. The used of smartphone in crime was widely recognized. Several types of personnel information are stored in their smartphones. When the investigator analyses the image data of the smartphone, the investigator can know the behaviour of the smartphone’s owner and his social relationship with other people. The analysis of smartphone forensic data is cover in mobile device forensic. Mobile device forensics is a branch of digital forensics relating to recovery of digital evidence from a mobile device under forensically sound condition. The digital investigation model used in this project is the model proposed by United States National Institute of Justice (NIJ) which consists four phases, which are collection phase, examination phase, analysis phase and presentation phase. This project related with analysis phase and presentation phase only. This paper introduces Visroid, a new tool that provides a suite of visualization for Android smartphone data

An evidence-based cloud incident handling framework

Cloud computing is increasingly adopted by both individual and organisational users; thus, ensuring the security and privacy of data stored in the cloud is a crucial requirement in an organisation‘s business continuity and risk assessment strategies. An incident handling strategy is key to mitigating risks to the confidentiality, integrity and availability of information assets, particularly those outsourced to the cloud located in one or more different countries. Thus, organisational cloud users may face challenges or be limited in their capability to handle security incidents (e.g. security breaches) on their sites since the infrastructure on which the data resides belongs to the cloud providers. Surveys were conducted with industry practitioners to identify: (1) the implications of emerging technologies and its information security threats on the incident handling practices, and (2) the factors influencing incident handling adoption for organisational cloud users. The results indicated that the current landscape of information security threats have impacted on their security strategic planning, resulting in practitioners being more proactive, requiring better tactical tools, and cultivating a culture of information security. The factors identified as having a significant influence on the adoption were determined using an integration of Situational Awareness and Protection Motivation Theory. Users are more likely to adopt if they are aware of cloud security and privacy related risks, confident in their capability, understand the benefits, and understand the impact due to an ineffective strategy. The cloud incident handing framework presented in this thesis draws upon principles and practices from both incident handling and digital forensics. The integration of digital forensic principles and practices facilitates the collection of digital evidence, reconstructing of events and establish facts of who, what, when, where, how, and why an incident took place. The framework consists of six phases, namely: Preparation (integrated with forensic readiness principles); Identification; Assessment (integrated with forensic collection and analysis practices); Action and Monitoring; Recovery; and Evaluation (integrated with forensic presentation practices). A feasibility study was conducted that simulates private cloud storage (i.e. ownCloud) in a virtual environment. A security information and event management tool was used to demonstrate that each phase is feasible with significant evidence artefacts can be collected. This framework was also validated using two case studies: mobile cloud storage (Google Drive, Dropbox, and OneDrive) and mobile communication (Viber, Telegram, Skype, WhatsApp and Messenger) applications. Both studies simulated typical user activities on the studied applications on the Android platform. Mobile forensics and network tools were deployed for the collection and analysis of evidence artefacts. The first case study simulated uploads, share, read and download files. The artefacts were then analysed based on the activities. The second case study was setting up the scenario of terrorists‘ use of mobile communication applications by simulating chat conversation, adds contacts, and shared media files activities. The artefacts were classified into accounts, contacts, chat logs, shared media files, and location data to facilitate terrorism investigations. This research has shown that the framework supports organisational users in both the incident handling and forensic investigations, as well as informing the design of security strategies for organisations

A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection

Advanced Persistent Threat (APT) attacks are a major concern for the cybersecurity in digital world due to their advanced nature. Attackers are skilful to cause maximal destruction for targeted cyber environment. These APT attacks are also well funded by governments in many cases. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. It is highly important to study proper countermeasures to detect these attacks as early as possible due to sophisticated methods. It is difficult to detect this type of attack since the network may crash because of high traffic. Hence, in this study, this research is to study the comparison between Multilayer Perceptron and Naïve-Bayes of APT attack detection. Since the APT attack is persistent and permanent presence in the victim system, so minimal false positive rate (FPR) and high accuracy detection is required to detect the APT attack detection. Besides, Multilayer Perceptron algorithm has high true positive rate (TPR) in the detection of APT attack compared to Naïve Bayes algorithm. This means that Multilayer Perceptron algorithm can detect APT attack more accurately. Based on the result, it also can conclude that the lower the false positive rate (FPR), the more accurate to detect APT attack. Lastly, the research would also help to spread the awareness about the APT intrusion where it possibly can cause huge damage to everyone

A mobile forensic visualization tool for android data partition

In the 21th century, digital crimes would be one of the biggest challenges to government and public. Digital crime cases that involve mobile phones are on the rise, resulting in digital forensic analysis tools are on the demand. However, there are limitations in the current mobile forensic tool, such as lack of automation and visualization process, false positives are too high and performance of the analysis is low. This study therefore aims to design, develop and test a tool - MF Visualizer – to visualize the metadata from databases in the Android data partition. The android data partition is chosen as the scope of the project. MF Visualizer follows the mandatory requirements of the forensic tool and is compatible with suitable modules to accomplish the task. The tool is developed by adopting Object-Oriented Software Development Model and using .Net Windows Presentation Foundation (WPF) framework to develop. The findings show that the tools could extract metadata from android data partitions as well as visualize the data in different visualization forms such as Bar Chart, Word Cloud, Map, Pie Chart and the Timeline method. Functionality and users testing results indicate that MF Visualizer has achieved the project objectives. This further indicates that MF Visualizer is a promising tool to be used in a real world scenario with further improvements

Factors Influencing the Adoption of Cloud Incident Handling Strategy: A Preliminary Study in Malaysia

This study seeks to understand the factors influencing the adoption of an incident handling strategy by organisational cloud service users. We propose a conceptual model that draws upon the Situation Awareness (SA) model and Protection Motivation Theory (PMT) to guide this research. 40 organisational cloud service users in Malaysia were surveyed. We also conduct face-to-face interviews with participants from four of the organisations. Findings from the study indicate that four PMT factors (Perceived Vulnerability, Self-Efficacy, Response Efficacy, and Perceived Severity) have a significantly influence on the adoption of cloud incident handling strategy within the organisations. We, therefore, suggest a successful adoption cloud incident handling strategy by organisational cloud service users involves the nexus between these four PMT factors. We also outline future research required to validate the model

An evidence-based forensic taxonomy of windows phone dating apps

Advances in technologies including development of smartphone features have contributed to the growth of mobile applications, including dating apps. However, online dating services can be misused. To support law enforcement investigations, a forensic taxonomy that provides a systematic classification of forensic artifacts from Windows Phone 8 (WP8) dating apps is presented in this study. The taxonomy has three categories, namely: Apps Categories, Artifacts Categories, and Data Partition Categories. This taxonomy is built based on the findings from a case study of 28 mobile dating apps, using mobile forensic tools. The dating app taxonomy can be used to inform future studies of dating and related apps, such as those from Android and iOS platforms

Comparison of data recovery techniques on master file table between Aho-Corasick and logical data recovery based on efficiency

Data recovery is one of the tools used to obtain digital forensics from various storage media that rely entirely on the file system used to organize files in these media. In this paper, two of the latest techniques of file recovery from file system (new technology file system (NTFS)) logical data recovery, Aho-Corasick data recovery were studied, examined and a practical comparison was made between these two techniques according to the speed and accuracy factors using three global datasets. It was noted that all previous studies in this field completely ignored the time criterion despite the importance of this standard. On the other hand, algorithms developed with other algorithms were not compared. The proposed comparison of this paper aims to detect the weaknesses and strength of both algorithms to develop a new algorithm that is more accurate and faster than both algorithms. The paper concluded that the logical algorithm was superior to the Aho-Corasick algorithm according to the speed criterion, whereas the algorithms gave the same results according to the accuracy criterion. The paper leads to a set of suggestions for future research aimed at achieving a highly efficient and high-speed data recovery algorithm such as the file-carving algorithm

Mentoring approach in learning fundamentals of Islamic banking / Noor Saliza Zainal ...[et al.]

This paper reviews the mentoring approach and its application at the learning institutions. Basically, mentoring approach is defined as a teaching method which involves a transfer of wisdom, where the mentor or coach provides advice or direction, probably based on their experience and expertise. Practically, often one selected student in the group is assigned to conduct learning relationship for understanding to other students. Currently, many courses taught at the universities are based on the traditional approach of teaching and learning. But, since there are many topics to be delivered for each course, some students might have problems in understanding each topic in depth. In addition to that, the lecturers also might not have enough time to clarify every topic thoroughly in class. Due to this problem, the group propose a mentoring approach as a supplementary approach of learning at the universities. In this study, we examine the perceptions on mentoring approach in learning of Fundamentals of Islamic Banking (CTU351) amongst the students at Universiti Teknologi MARA Kelantan, Machang campus. The objectives of this study have twofold. First, to determine the difference in mean perception on mentoring approach in learning Fundamentals of Islamic Banking across selected students’ profiles. Secondly is to identify the factors affecting the perception on learning of Fundamentals of Islamic Banking. The results of the study show that many students do not have Mentor during their studies. Next, the study also found that their perceptions on mentoring approach do not significantly differ across gender, accommodation and mentor. Lastly, the results also prove that there is a positive correlation between Classroom with Outside Classroom and Surroundings in determining the learning of Fundamentals of Islamic Bankin

An evidence-based forensic taxonomy of Windows phone communication apps

Communication apps can be an important source of evidence in a forensic investigation (e.g., in the investigation of a drug, trafficking or terrorism case where the communications apps were used by the accused persons during the transactions or planning activities)., This study presents the first evidence-based forensic taxonomy of Windows Phone communication apps, using an existing two-dimensional, Android forensic taxonomy as a baseline. Specifically, 30 Windows Phone communication apps, including Instant Messaging (IM) and Voice, over IP (VoIP) apps, are examined. Artifacts extracted using physical acquisition are analyzed, and seven digital evidence objects of forensic, interest are identified, namely: Call Log, Chats, Contacts, Locations, Installed Applications, SMSs and User Accounts. Findings from this study, would help to facilitate timely and effective forensic investigations involving Windows Phone communication apps