Faculty of Computing

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Embargo
ARCSECURE: Centralized Hub for Securing a Network of IoT Devices
(Springer, Cham, 2021-07-06) Yapa Abeywardena, K; Abeykoon, A. M. I. S; Atapattu, A. M. S. P. B; Jayawardhane, H. N; Samarasekara, C. N
As far as it is considered, IoT has been a game changer in the advancement of technology. In the current context, the major issue that users face is the threat to their information stored in these devices. Modern day attackers are aware of vulnerabilities in existence in the current IoT environment. Therefore, securing information from being gone into the hands of unauthorized parties is of top priority. With the need of securing the information came the need of protecting the devices which the data is being stored. Small Office/Home Office (SOHO) environments working with IoT devices are particularly in need of such mechanism to protect the data and information that they hold in order to sustain their operations. Hence, in order come up with a well-rounded security mechanism from every possible aspect, this research proposes a plug and play device “ARCSECURE”.
Embargo
Behavior & Bio metric based Masquerade Detection Mobile Application
(Springer, Cham, 2019-07-29) Chandrasekara, P; Abeywardana, H; Rajapaksha, S; Sanjeevan, p
Mobile phone has become an important asset when it comes to information security since it has become a virtual safe. However, to protect the information inside the mobile, the manufacturers use the technologies as password protection, face recognition or fingerprint protection. Nevertheless, it is clear that these security methods can be bypassed. That is when the urge of a post-authentication is coming to the surface. In order to protect the phone from an unauthorized or illegitimate user this method is proposed as a solution. The aim of the proposed solution is to detect the illegitimate user by monitoring the behavior of the user by four main parameters. They are: 1) Keystroke dynamics with a customized keyboard; 2) location detection; 3) voice recognition; 4) Application usage. In the initial state machine learning is used to train this mobile application with the authentic user’s behavior and they are stored in a central database. After the initial training period the application is monitoring the usage and comparing it with the already saved data of the user. Another unique feature of this is the prevention mechanism it executes when an illegitimate user is detected. Furthermore, this application is proposed as an inbuilt application in order to avoid the deletion of app or uninstallation of the app by the intruder. With this Application which is introduced as “AuthDNA” will help you to protect the sensitive information of your mobile device in a case of theft and bypassing of initial authentication.
Embargo
AuthDNA: An Adaptive Authentication Service for any Identity Server
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) De Silva, H.L.S.R.P.; Claude Wittebron, D.; Lahiru, A.M.R.; Madumadhavi, K.L.; Rupasinghe, L.; Abeywardena, K.Y.
Adaptive authentication refers to the way that configures two factors or multi-factor authentication, based on the user’s risk profile. One of the most pressing concerns in modern days is the security of credentials. As a solution, developers have introduced the multifactor authentication. The multi-factor authentication has an adverse effect on user experience. This paper proposes a novel adaptive authentication mechanism which tries to eradicate the negative user experience of the traditional multi factor authentication systems. Adaptive authentication gathers information about each user and prevents fraudulent attempts by validating them against the created profiles. This approach will increase the usability, user-friendliness by introducing multi-factor authentication only when its necessary using a risk based adaptive approach. Furthermore, the solution ensures security by authenticating the legitimate user through collectively analyzing the properties, behavior, device and network related information. In the creation of the user profile, the adaptive authentication system will gather and analyze the user typing behaviors using a unique recurrent neural network algorithm named LSTMs with 95.55% accuracy and mouse behaviors using SVMs with 95.48% accuracy. In device-based authentication, a fingerprint is generated to the browser and to the mobile device which is utilized in the analysis of the accuracy rate of the authentication. Blacklisting and whitelisting of the networks and geo velocity of the authentication request are captured under the geolocation and network-based authentication. All the accuracy rates are fed to the risk-based authentication which helps the decision of re-authentication or in the grant of access to the system by analyzing the risk score generated for the authentication request.

Faculty of Computing

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