MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network

Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method

GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively

Editorial

This special edition is devoted to Pedagogies for Higher Education, which has become an important research area in this decade. Higher education faces many challenges like engaging reluctant students, implementing active learning pedagogies effectively for diverse and large classes etc. This issue tried to incorporate various solutions and case studies to cater above mentioned challenges in higher education

Mock test using Akash tablet

Due to the widespread adoption and use of handheld mobile devices, the application of mobile technologies in enhancing learning activities has attracted research interest. This application presents an attempt to exploit mobile technologies to simplify the exam management and performance assessment activities of a learning process. The work focuses on key aspects of mobile device and platform oriented design, light-weight and efficient implementation, interface usability issues related to fast and convenient question navigation, and performance assessment. Mock test is android application that establishes a network between the institutes and the students. Akash tablet is used for working. Institutes enter on the application the questions they want in the exam. These questions are displayed as a test to the eligible students. The answers enter by the students are then evaluated and their score is calculated and saved. This score then can be accessed by the institutes to determine the passes students or to evaluate their performance. The site has an administrator who keeps an eye on the overall functioning of the system.  This will be an Android client-server application and one can register them self for test as a student and simultaneously he can get the exam result. After submit or End of the Test the form will be submitted and evaluated.Due to the widespread adoption and use of handheld mobile devices, the application of mobile technologies in enhancing learning activities has attracted research interest. This application presents an attempt to exploit mobile technologies to simplify the exam management and performance assessment activities of a learning process. The work focuses on key aspects of mobile device and platform oriented design, light-weight and efficient implementation, interface usability issues related to fast and convenient question navigation, and performance assessment. Mock test is android application that establishes a network between the institutes and the students. Akash tablet is used for working. Institutes enter on the application the questions they want in the exam. These questions are displayed as a test to the eligible students. The answers enter by the students are then evaluated and their score is calculated and saved. This score then can be accessed by the institutes to determine the passes students or to evaluate their performance. The site has an administrator who keeps an eye on the overall functioning of the system.  This will be an Android client-server application and one can register them self for test as a student and simultaneously he can get the exam result. After submit or End of the Test the form will be submitted and evaluated