Enterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet, instant detection using static analysis has become very difficult due to the exponential rise in volume and variety of malware. The compelling need of early stage detection of malware-based attacks significantly motivates research inclination towards automated malware detection. The recent machine learning aided malware detection approaches using static analysis are mostly supervised. Supervised malware detection using static analysis requires manual labelling and human feedback; therefore, it is less effective in rapidly evolutionary and dynamic threat space. To this end, we propose a progressive deep unsupervised framework with feature attention block for static analysis-based malware detection (PROUD-MAL). The framework is based on cascading blocks of unsupervised clustering and features attention-based deep neural network. The proposed deep neural network embedded with feature attention block is trained on the pseudo labels. To evaluate the proposed unsupervised framework, we collected a real-time malware dataset by deploying low and high interaction honeypots on an enterprise organizational network. Moreover, endpoint security solution is also deployed on an enterprise organizational network to collect malware samples. After post processing and cleaning, the novel dataset consists of 15,457 PE samples comprising 8775 malicious and 6681 benign ones. The proposed PROUD-MAL framework achieved an accuracy of more than 98.09% with better quantitative performance in standard evaluation parameters on collected dataset and outperformed other conventional machine learning algorithms. The implementation and dataset are available at https://bit.ly/35Sne3a.

Background: Stress has an effect on the cognitive performance of medical students, which leads to poor health and burnout. Aim: To figure out how much stress medical students perceive and what the likely stressors are. Methods: This cross-sectional study was conducted in medical colleges of Faisalabad. Study duration was 4 months (October 2021 to January 2022). A sample size of 380 was taken. The inclusion criteria included medical undergraduate students and who gave consent while those who didn’t give the consent and who were not medical students were excluded.Non-probability convenient sampling technique was used for collection of study participants. Data was collected from medical students. Time required to complete the questionnaire was 5 - 10 min. A questionnaire titled “The Perceived Stress Scale (14 items)” was used. Results: Majority of the students were having moderate stress. The mean perceived stress score of participants was 29.79±5.3. Mean perceived stress score of males and females was 29.56±5.41 and 29.94±5.18 respectively. Class 2nd year MBBS showed the highest mean of perceived stress score as 31.79±6.39 than other classes. Also, younger age group perceived more stress. Among the stressors, academic and psychosocial stressors played significant role as compared to environmental stressors. Conclusion: The perceived stress is higher among younger age group, class 2nd year MBBS students and females. Academic and psychosocial stressors were more common in participants than environmental. Personal and institutional initiatives are urgently required to keep medical students from becoming distressed. Medical students must be taught coping mechanisms and self-care practices. It is critical to underline that, in complement to teaching students, it is also critical to consider the students' well-being during their years of medical study. These things can be applied to community as well and with help of this study, their stress factors can be assessed and coping mechanisms can be advised. Keywords: Academic, psychological, medical, prevalence, students, stress, stressors

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