Scopus Index Publications

Now showing 1 - 3 of 3

Embargo
Project HyperAdapt: An Agent-Based Intelligent Sandbox Design to Deceive and Analyze Sophisticated Malware
(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, S; Dias, S; Vithanage, V; Dilhara, A; Senarathne, A; Siriwardana, D; Liyanapathirana, C
Malware increasingly employs sophisticated evasion techniques to bypass sandbox-based analysis, rendering traditional detection methods ineffective. This research presents Project HyperAdapt: Agent-Based Intelligent Sandbox, a framework that integrates both offensive and defensive machine learning models to enhance malware detection, deception, and behavioral analysis. The offensive RL model generates evasive malware samples, challenging the sandbox, while the defensive models including hybrid evasion detection, GAN-based behavior simulation, and a dynamically adapting RL agent work collectively to improve sandbox resilience. By continuously learning from evasive malware behavior, the defensive RL agent adapts in real-time, strengthening detection capabilities. Experimental results demonstrate that this approach enhances sandbox effectiveness, ensuring long-term adaptability against evolving malware threats.
Embargo
Smart Advertising Based on Customer Preferences and Manage the Supermarket
(IEEE, 2022-12-09) Wickramasinghe, A.Y.S. W; Eishan Dinuka, W.H.A.; Weerasinghe, W.S. H; Karunaratne, K.P. G; Liyanapathirana, C; Rupasinghe, L
As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users’ feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users’ preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly.
Embargo
Policies based container migration using cross-cloud management platform
(IEEE, 2018-12-21) Janarthanan, K; Peramune, P. R. L. C; Ranaweera, A. T; Krishnamohan, T; Rupasinghe, L; Sampath, K. K; Liyanapathirana, C
Over the last decade, cloud computing has helped in variety of ways to humanity. Mainly in the ways of, achieving Disaster Recovery (DR) and in protecting the end users' data and Anywhere, Any device, Anytime access to the users' data. This research further helps people and organization to overcome common problems related to clouds such as, vendor-lock in and legal regulation. In today's world, more and more organizations are adopting the cloud services mainly because of the reliability and affordability provided by them. However, there are several drawbacks faced by the cloud users and cloud service providers. Apart from the security perspective, the cloud users are facing challenges in control and visibility, lack of standard service interfaces, difficulty in deploying applications across multiple clouds and vendor lock-in. Also, cloud service providers are facing challenges in degradation of the quality of service provided because of the distance between cloud data center and the end user and unexpected interruption of services etc. The above problems can be reduced to a greater extent or mitigated by adopting Multi Cross Cloud Infrastructure. This benefits the cloud users to receive the best quality services to increase their productivity. Hence, the main aim of this research is to build a common platform to manage the cross-cloud environment particularly Microsoft AZURE cloud and Amazon Web Services (AWS) with multiple features such as policies based container migration among the clouds and finding the best virtual machines (VM) across the clouds to deploy new containers. Cross-cloud management platform can be implemented within an organization or Enterprise and is used by the 3rd level support team such as Infrastructure team to provide multiple services (E.g. - Delivering application containers, Migration of containers on request) to end users based on some service level agreements (SLA) with more control and visibility.

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