Biblio

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learning methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level data coming from the dark web. Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’s accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features. When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available.

As networks continue to grow in complexity using wired and wireless technologies, efficient testing solutions should accommodate such changes and growth. Network simulators provide a network-independent environment to provide different types of network testing. This paper is motivated by the observation that, in many cases in the literature, the success of developed network protocols is very sensitive to the initial conditions and assumptions of the testing scenarios. Network services are deployed in complex environments; results of testing and simulation can vary from one environment to another and sometimes in the same environment at different times. Our goal is to propose mutation-based integration testing that can be deployed with network protocols and serve as Built-in Tests (BiT).This paper proposes an integrated mutation testing framework to achieve systematic test cases' generation for different scenario types. Scenario description and variables' setting should be consistent with the protocol specification and the simulation environment. We focused on creating test cases for critical scenarios rather than preliminary or simplified scenarios. This will help users to report confident simulation results and provide credible protocol analysis. The criticality is defined as a combination of network performance metrics and critical functions' coverage. The proposed solution is experimentally proved to obtain accurate evaluation results with less testing effort by generating high-quality testing scenarios. Generated test scenarios will serve as BiTs for the network simulator. The quality of the test scenarios is evaluated from three perspectives: (i) code coverage, (ii) mutation score and (iii) testing effort. In this work, we implemented the testing framework in NS2, but it can be extended to any other simulation environment.