Android Malware detection using predictive analytics.

The growth of android applications is causing a threat and a serious issue towards Android’s security. The number of malware targeting the Android operating system is increasing daily. As a result, in recent days the traditional ways that are being used to detect malware are not able to defend alone against the rapid development of hackers attacking techniques and novel malware. This capstone project focuses on using predictive analytics toward detecting malware from the network traffic. In this capstone project, we aim to train and test our data to find the best machine learning model with the highest accuracy of detecting malware in the network traffic. Through a variety of machine learning algorithms and models, we focused on 5 models starting with the logistic regression that was successfully able to predict malware by 67%. Moving to the decision tree that was effectively able to predict malware by 69% which was exactly equal to the random forest prediction ability. The AdaBoost came about 84% exactness, and KNN came with the highest anticipation of 86% between all the models. This shows us the advantage of adopting predictive analytics in malware detection within the traditional approaches to build a strong and defendable Android operating system against malware

Efficient Thermal Image Segmentation through Integration of Nonlinear Enhancement with Unsupervised Active Contour Model

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity enhancement technique and Unsupervised Active Contour Models (UACM). The nonlinear intensity enhancement improves visual quality by combining dynamic range compression and contrast enhancement, while the UACM incorporates active contour evolutional function and neural networks. The algorithm is tested on segmenting different objects in thermal images and it is observed that the nonlinear enhancement has significantly improved the segmentation performance

Automatic Building Change Detection in Wide Area Surveillance

We present an automated mechanism that can detect and characterize the building changes by analyzing airborne or satellite imagery. The proposed framework can be categorized into three stages: building detection, boundary extraction and change identification. To detect the buildings, we utilize local phase and local amplitude from monogenic signal to extract building features for addressing issues of varying illumination. Then a support vector machine with Radial basis kernel is used for classification. In the boundary extraction stage, a level-set function with self-organizing map based segmentation method is used to find the building boundary and compute physical area of the building segments. In the last stage, the change of the detected building is identified by computing the area differences of the same building that captured at different times. The experiments are conducted on a set of real-life aerial imagery to show the effectiveness of the proposed method

The reality of women\u27s entrepreneurship in the sultanate of Oman, the factors affecting it and its relationship to some variables

The current study sought to present the reality of women\u27s entrepreneurship in Oman, and to clarify the factors affecting their success. The sample study consisted of (96) female entrepreneurs. An electronic interview form was applied as well as group interviews. The results showed that the majority of Omani women entrepreneurs assessed the success of their projects as good . The most important factors that positively influence women\u27s entrepreneurship were participation in exhibitions and events , followed by economic feasibility study for the project , followed by having an e-mail to communicate with customers or institutions concerned , while the factors that negatively affect their success, including weak logistics support for the implementation of the project , followed by difficulty in obtaining funding when the project is expanded . Then influenced by international economic and political events. The results also showed that there were no significant differences in the degree of impact of the factors facing Omani women entrepreneurs due to the change in social status, academic qualification, sector type, and province. The study recommended a number of measures to encourage women entrepreneurship in the Sultanate, the most important of which is the establishment of a national center specialized in training entrepreneurs

Continuous face authentication scheme for mobile devices with tracking and liveness detection

We present a novel scheme for continuous face authentication using mobile device cameras that addresses the issue of spoof attacks and attack windows in state-of-the-art approaches. Our scheme authenticates a user based on extracted facial features. However, unlike other schemes that periodically re-authenticate a user, our scheme tracks the authenticated face and only attempts re-authentication when the authenticated face is lost. This allows our scheme to eliminate attack windows that exist in schemes authenticating periodically and immediately recognise impostor usage. We also introduce a robust liveness detection component to our scheme that can detect printed faces and face videos. We describe how the addition of liveness detection enhances the robustness of our scheme against spoof attacks, improving on state-of-the-art approaches that lack this capability. Furthermore, we create the first dataset of facial videos collected from mobile devices during different real-world activities (walking, sitting and standing) such that our results reflect realistic scenarios. Our dataset therefore allows us to give new insight into the impact of user activity on facial recognition. Our dataset also includes spoofed facial videos for liveness testing. We use our dataset alongside two benchmark datasets for our experiments. We show and discuss how our scheme improves on existing continuous face authentication approaches and efficiently enhances device security

Situational factors shape moral judgements in the trolley dilemma in Eastern, Southern and Western countries in a culturally diverse sample

The study of moral judgements often centres on moral dilemmas in which options consistent with deontological perspectives (that is, emphasizing rules, individual rights and duties) are in conflict with options consistent with utilitarian judgements (that is, following the greater good based on consequences). Greene et al. (2009) showed that psychological and situational factors (for example, the intent of the agent or the presence of physical contact between the agent and the victim) can play an important role in moral dilemma judgements (for example, the trolley problem). Our knowledge is limited concerning both the universality of these effects outside the United States and the impact of culture on the situational and psychological factors affecting moral judgements. Thus, we empirically tested the universality of the effects of intent and personal force on moral dilemma judgements by replicating the experiments of Greene et al. in 45 countries from all inhabited continents. We found that personal force and its interaction with intention exert influence on moral judgements in the US and Western cultural clusters, replicating and expanding the original findings. Moreover, the personal force effect was present in all cultural clusters, suggesting it is culturally universal. The evidence for the cultural universality of the interaction effect was inconclusive in the Eastern and Southern cultural clusters (depending on exclusion criteria). We found no strong association between collectivism/individualism and moral dilemma judgements