Faculty of Computing

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Embargo
Optimization of Volume & Brightness of Android Smartphone through Clustering & Reinforcement Learning (“RE-IN”)
(IEEE, 2018-12-21) Abeywardhane, J. S. D. M. D. S; de Silva, E. M. W. N; Gallanga, I. G. A. G. S; Rathnayake, L. N; Wickramaratne, C. J; Sriyaratna, D
Smartphone has become one of the most significant piece of technology that humans were able to produce in the 21st century. It has become our life companion; hence the features of the smartphones have developed in advance. But, some features may not work as expected. For instance, auto brightness changing feature is now actualized with smartphones, yet we alter the brightness according to our preference. In the same manner, considering the volume of our smartphone it doesn't change according to our preference subsequently. This research will develop a mobile application (“RE-IN”) to overcome this issue for Android smartphones. Since android smartphones allow accessing its hardware layer we can roll out improvements as we need, yet Apple doesn't permit to proceed with its hardware layer thus hard to do this for the iPhone users. By utilizing the RE-IN mobile application users may have to encounter an optimal brightness and volume on their Android smartphones agreeing the present condition of smartphone users are in. RE- IN application will keep running as a background application on an Android smartphone. When the client changes the brightness and volume as his/her preference. At that point, the reinforcement learning algorithm over the time application will distinguish how to control user's smartphone's brightness and volume relying upon the user's circumstance. When client surrounding is loaded with light, the framework will modify brightness for his/her preference. The client doesn't need to do this manually. Moreover when the client is at the too much boisterous place all of a sudden gets a call from someone; client's smartphone amplifier volume will change consequently and solaces the client's discussion. To actualize this framework it is relied upon to reinforcement learning and machine learning as the research area. By finishing the literature review, research group unable to find an Android mobile application which automates the process of volume and brightness of the Android smartphone as per user preference. After using the reinforcement learning algorithm to learn the data set then distribute the process, using client-server model and come up with a clustering algorithm(K-means algorithm) to share common attributes by considering geographical area which they live in and variables like age, gender, how they interact with the device etc. In addition, this system will identify abnormal behaviors of some particular users. RE-IN will identify the users who are keeping volume level to the highest and brightness level to its maximum and notify them in advance.
Embargo
E-Secure: An Automated Behavior Based Malware Detection System for Corporate E-Mail Traffic
(SAI 2018: Intelligent Computing, 2018-11-02) Thebeyanthan, K.; Achsuthan, M.; Ashok, S.; Vaikunthan, P.; Senaratne, A. N; Abeywardena, K. Y
Over the year’s cyber-attacks have become much more sophisticated, bringing new challenges to the cyber world. Cyber security is becoming one of the major concerns in the area of network security these days. In recent times attackers have found new ways to bypass the malware detection technologies that are used in the security domain. The static analysis of malware is no longer considered an effective method compared to the propagating rate of malware bypassing static analysis. The first step that has to be followed to protect a system is to have a deep knowledge about existing malware, different types of malware, a method to detect the malware, and the method to bypass the effects caused by the malware. E-Secure is a behavior based malware detection system for corporate e-mail traffic. This paper proposes a malware security system as a solution to detect the malicious file that is passed through the e-mail of corporate network, and externally a file uploaded separately through a website for analysis. Since signature-based methods cannot identify the sophisticated malware effectively, the dynamic analysis is used to identify the malware. The Cuckoo Sandbox plays an important role in analyzing the behavior of malware but has no feature to extract the behavior, cluster it and produce results graphically in a way that is easier to understand. An application programming interface is used to extract the behavior of the malware and to train the machines automatically by feeding the extracted behavior. K-Means algorithm is used to cluster the malware based on the same behaviors. An application programming Interface is developed to illustrate the clusters graphically. After the completion of the training process, when a new malware arrives again an application programming interface is developed to identify the type of the malware. Risk analysis is used to state the criticality of a malware. The output of the whole process can be viewed through the E-Secure web interface which helps even a junior network security administrator to understand the detected malware and how critical the malware is.

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