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Abstract-The amount and the complexity of malicious activity increasing and evolving day by day. Typical static code analysis is futile when challenged by diverse variants. The prolog of new malware samples every day is not uncommon and the malware designed by the attackers have the ability to change as they propagate. Thus, automated dynamic malware analysis becomes a widely preferred technique for the identification of unknown malware.In this paper, an automated malware detection system is presented based on dynamic malware analysis approach. The behavior of malware is observed in the controlled environment of the popular malware analysis system. It uses the clustering and classification of embedded malware behavior reports to identify the presence of malicious behavior. Based on the experimentation and evaluation it is evident that the proposed system is able to achieve better F-measures, FPR, FNR, TPR and TNR values resulting in accurate classification leading to more efficient detection of unknown malware compared to the traditional hierarchical classification approach.

Active learning techniques surpassed traditional teacher-centric approaches owing to their benefits of better learning from the stakeholders. The selection of a specific learning strategy varies from one course to another for productive results. Many students face difficulties in understanding programming subjects especially when it comes to applying it practically for application developments, since the curriculum permits only a certain number of specific programs to be implemented and some courses will have only theoretical subjects without a practical component. Thus, it becomes a need of the hour to make students productive in order to be confident in applying concepts learnt to the application development. The proposed method of applying a combination of project based learning involving collaborative learning will prove to be an effective platform for the students to enhance their design & development skills. This experimentation has been applied to a total of about 420 engineering students and the average performance in the university examinations as well as their demonstration/oral presentation, viva answering skills are considered as metrics for measurement. Apart from this, students' ability to face challenges is gauged through their ability to excel in placement drives for selection to various companies. This approach has resulted in a significant improvement in students' performance in the university examinations with upto 54% of the students securing higher grades. Apart from this, it has been estimated that there is an improvement in placement rate of upto 10 -15% along with increase in no. of offerings for the individual students.

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The tremendous gain owing to the ubiquitous acceptance of the cloud services across the globe results in more complexity for the cloud providers by way of resource maintenance. This has a direct effect on the cost economy for them if the resources are not efficiently utilized. Most of the allocation strategies follow mechanisms involving direct allotment of VMs onto the servers based on their capabilities. This paper presents a VM allocation strategy that looks at VM placement by allowing server capacity to be partitioned into different classes. The classes are mainly based on the RAM and processing abilities which would be matched with VMs need. When the match is found the servers from this category are provisioned for the task executions. Based on the experimentation for various datacenter scenarios, it has been found that the proposed mechanism results in significant energy savings with reduced response time compared to the traditional VM allocation policies.