Faculty of Computing
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Publication Open Access Machine learning-based criminal behavior analysis for enhanced digital forensics(Public Library of Science, 2025-10-06) Dananjana, W. P; Arambawela, J. S; Gonawala, D.G. S. N; Rathnayaka, R.K. G.H; Senarathne, A. N; Siriwardena, S.M. D.NIn an increasingly digital world, uncovering criminal activity often relies on analyzing vast amounts of online behavior. Traditional methods in digital forensics struggle to keep up with the complexity and volume of data, particularly when trying to detect subtle deviations in online activity that could signal illegal intent. This research introduces an innovative approach that leverages machine learning to analyze internet activity—specifically browser artifacts—shedding new light on criminal behaviors that would otherwise remain hidden.Using advanced machine learning techniques such as Long Short-Term Memory (LSTM) networks and Autoencoders, this study focuses on detecting suspicious patterns and anomalies in browsing activity. By understanding the sequence and timing of a user’s online actions, this method enhances digital forensics investigations, allowing for faster and more accurate detection of criminal intent and behavior. The research aims to improve the speed and accuracy of identifying malicious online activity but also offers law enforcement and investigators a powerful tool to make sense of complex data. These findings represent an important step towards advancing digital forensics, enabling deeper insights into criminal behavior and more effective investigations, ultimately contributing to a safer digital environment.Publication Embargo Secure IoT Middleware Using SDN and Intent-Based Routing(IEEE, 2022-04-07) Lakpriya, R. A. K; Rathsara, W. A. S; Fernando, P. N. R. S; Thenuwara, H. S; Ruggahakotuwa, L. O; Senarathne, A. NWith the rapidly increasing volume of IoT devices in the last decade due to the adaptation of the smart home/office concepts around the world, IoT devices are being targeted by hackers to perform attacks like DDOS and most likely creating botnets which will drastically decrease the performance of IoT devices and may also compromise the networks they are connected to. It is difficult to detect compromised IoT devices because there is more than one device in a simple IoT network, and it is difficult to monitor every device in the network. To solve this issue, this research proposes a Secure Middleware for IoT devices that will collect data generated by IoT devices, scan them for any malicious activity and then trigger an alert if any threat is detected in the IoT Network. The secure middleware is implemented in the proposed SDN architecture that uses Fog Computing, Cloud Computing, and VPN technologies to create a secure, scalable, flexible, and fast network architecture. A machine learning model is used to examine and detect any malicious activity in the IoT network. An open-source SIEM called the ELK stack is used to trigger alerts for the malicious activity identified by the ML model. With the help of the ML model and the SIEM, this proposed middleware will efficiently secure the IoT Software Defined network by detecting malicious attacks in real-time.Publication Embargo Online Digital Cheque Clearance and Verification System using Block Chain(IEEE, 2021-04-02) Bogahawatte, W. W. M. K. A; Isuri Samanmali, A. H. L; Perera, K. D. M; Kavindi, M. A. T; Senarathne, A. N; Rupasinghe, P. LCheque Truncation System (CTS) is an image-based cheque clearing framework used in Sri Lanka. This semi manual process has certain limitations and takes up to 3 working days to clear an inter-bank national cheque in Sri Lanka. Faced with the limitations of this system, cheque users and commercial banks must need an efficient and a secured system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. This research portrays an automated solution, which is feasible for any commercial bank in Sri Lanka, to address above-mentioned issues. The proposed system is based on the blockchain where all banks willing to take an interest in this framework must connect the proposed blockchain based system to supply the quicker cheque clearance to its clients. Answers were proposed with a complete framework consisting of four main phases: (i) paper cheque clearing process, (ii) digital cheque issuing and clearing process, (iii) cheque fraud detection process and (iv) cheque transaction securing process. Python along with Flutter framework and Ethereum were the major technologies used for implementing the system. The proposed system is highly scalable as Ethereum provides added integrity to the system. The approach advocates the customer as well as the bank with much simpler and speedier cheque clearing process with increased security. It also contributes with a paper cheque fraud detection system with faster and reliable results. The proposed system provides benefits to the user as well as the bank by addressing the requirement of producing a secure, effective and environment friendly system. Finally, CheckMate permits a consistent stream of cheque clearance operation for the payer and the payee without any mediators.Publication Embargo The Next Gen Security Operation Center(IEEE, 2021-04-02) Perera, A; Rathnayaka, S; Che, C; Madushanka, W. W; Senarathne, A. NDue to the evolving Cyber threat landscape, Cyber criminals have found new and ingenious ways of breaching defenses in networks. Due to the sheer destruction these threat actors can cause to an organization, most modern-day organizations have focused their attention towards protecting their critical infrastructure and sensitive information through multiple methods. The main defense against both internal and external threats to an organization has been the implementation of the Security Operations Center (SOC) which is responsible for monitoring, analyzing and mitigating incoming threats. At the heart of the Security Operations Center, lies the Security Information and Event Management system (SIEM) which is utilized by SOC analysts as the centralized point where all security notifications from various security technologies including firewalls, IPS/IDS and Anti-Virus logs are collected and visualized. The effective operation of SOC in an organization is dependent on how well the SIEM filters log events and generates actual alerts. Here lies the major problem faced by SOC analysts in detecting threats. If proper alert correlation is not accomplished, analysts would have to deal with too much alert noise due to a high false positive count. This would ultimately cause analysts to miss critical security incidents, thus causing severe implications to the organization's security. The performance of a SIEM can be enhanced through adding various functionalities such as Threat Hunting, Threat Intelligence and malware identification and prevention in order to reduce false positive alarms, threat framework and machine learning which would increase the accuracy and efficiency of the overall Security Operations process of an organization. Even though many products which provide these additional functionalities exist in the current market, they can be too expensive for smaller scale organizations to handle. Our aim is to make security operations deliverable to any organization regardless of the size and scale without any financial implications and enhance its functionalities with the aid of Advanced Machine Learning Techniques.Publication Embargo Secure Web Navigation with Intrusion Detection And Quota Management for SOHO and Small Scale Businesses(IEEE, 2019-12-05) Perera, M. A. D. S. R; Hemapala, C; Udugahapattuwa, M; Senarathne, A. NIt's a modern day necessity and a trend to offer free and open web access to their customers and employees in small scale and Small Office Home Office (SOHO) business culture (restaurants, malls, coffee shops). Unfortunately, internet security and quota management are mostly overlooked which makes it an intruders' paradise. The existing solutions that incorporate machine learning based dynamic aspects, cannot be afforded by our target audience nor do they possess the extensive IT knowledge to configure and maintain them. To cater to this gap, this research proposes the network management device `Dynamic Defender', targeted for small scale and SOHO type businesses which focuses on affordability and user-friendliness as key factors while incorporating cutting edge machine learning technologies. The Dynamic Defender's Intrusion Detection Engine is comprised of 99.13% accuracy with its base run on Artificial Neural Networks. URL Classification Engine produced high accuracy on all 3 machine learning algorithms which were used. Specifically, Random Forest with 92.94 % precision, Artificial Neural Networks with 90.33% precision and Logistic Regression with 91.41% precision. The Dynamic Bandwidth Management System consisted of an 89% accuracy level on the hybrid module of Linear Regression and Decision Trees while the Quota Management System (QMS) provided an accuracy level of 82% in K-Nearest Neighbors and 89% on Decision Tree algorithm.Publication Embargo Security Platform for Mobile OS(IEEE, 2019-12-05) Benett, A. S. B; Vinushanth, K; Ranjitha, L; Abisherk, R. S; Senarathne, A. NEvolution of human is evident in everything that we see, feel and use today. History of phone is one such example we can see. Modern devices have all the features that helped it to become the ultimate source of data for an individual. It was easy for an individual to keep all his data intact with him in his hands. There are vulnerable points which can be exploited to acquire the personal and sensitive data from the device in order to gain unethical advantage over an individual. Bluetooth, Wi-Fi and human errors are some of those vulnerable points. In this paper, multilevel malware detection with the help of machine learning, Rogue access point detection and accidental data leakage prevention are proposed with an emphasis on Android mobile operating system. As a result, accidental leakage of sensitive data by the user can be prevented. Further, rogue access point detection will help the user to prevent data loss through wireless network and the malware detection can prevent all the known and 85% of the unknown malwares.Publication Embargo Enhanced Secure Solution for PoS Architecture(IEEE, 2019-12-05) Samaranayake, C; Kuruppu Achchige, R. P; Shanaz, T; Ranasinghe, A; Senarathne, A. NToday retail businesses expect to bring the utmost in sales and payment transactions by adapting new technologies. Therefore, Advanced Point of Sales (PoS) Systems are widely used in the industry. Regardless of how efficient and secure these systems or applications work, unexpected information security risks can arise. Such risks could be a threat to their business and organization. It is important to ensure that critical information such as payment card information, handled in PoS systems is kept secure from attacks that could bring financial loss. This research provides a solution by studying the overall infrastructure of a PoS System and identifies the key events that such data would be at risk. The major concern of it was to enhance the existing security features of the system to avoid any type of malicious activity. This research consists of four main sections under security related to PoS Systems that would address the risk; Studying of malware and classifying them, detecting possible attacks and means of preventing it, a robot (BOT) to predict and generate the system status with a Data Leakage Prevention(DLP) solution for all the events occurring at a PoS. The key objective of implementing this solution was to protect the confidential data that is being used in the PoS System and to avoid threats that lead to the unavailability of the system. The implemented security features using machine learning and Deep Learning methods to the existing PoS functions produced a 99.3% of accuracy in Malware Detection and 95% of accuracy in its Classification process while the DLP Solution was able to obtain an accuracy of 84.6%. The above results retrieved fulfilled the research objectives and aided to integrate an enhanced security solution for a PoS system.Publication Embargo VAULT - A Shared Distributed And Redundant Storage Solution(IEEE, 2019-12-05) Peiris, T. R. N. R; Bandara, W. M. U. K. M. T; Sachintha, K. V. A; Senarathne, A. NAn ideal distributed storage solution must have the ability to provide redundant, reliable, shared and secure access to user data without compromising the ability to scale and descend while maintaining performance. VAULT is an attempt to avert the negatives of the cloud in a local environment using a decentralized methodology. VAULT makes use of individual idle storage space on a network of peer-to-peer nodes which is then provided to an end user to store files in the pooled space. VAULT implements redundancy by the use of Reed-Solomon codes and maps file fragment locations using a blockchain as a distributed ledger. Fragment distribution is optimized using a machine learning approach where node characteristics are used to determine the reliability of each node. The aggregation of above features makes VAULT an ideal solution for corporate environments where consumer hardware and infrastructure is already allocated.Publication Embargo Intelligent Enterprise Security Enhanced COPE (Intelligent ESECOPE)(IEEE, 2018-12-21) Samarathunge, R. D. S. P; Perera, W. P. P; Ranasinghe, R. A. N. I; Kahaduwa, K. K. U. S; Senarathne, A. N; Abeywardena, K. YMobile devices have come a long way of supporting humans' day to day tasks. Companies from all over the world tend to implement Information Technology (IT) consumerization in their premises in order to attain high productivity as well as employee satisfaction. Bring Your Own Device (BYOD), Corporate Owned Personally Enabled (COPE) and Choose Your Own Device (CYOD) assist to implement IT consumerization according to the organization's requirements. This research looks at the security issues in Corporate Owned Personally Enabled concept. The purpose of this research is to identify major security concerns an organization could have and propose sophisticated yet effective countermeasures. Research components are categorized into four main parts which are web data loss prevention, email data loss prevention, malware identification and malware classification. The information leak can be occurred either deliberately or unintentionally by an individual or a group of individuals in any organization which affects financial status, customer or public security and the reputation. ESECOPE is built with a revived technique that is based on keyword-based search detection to reach the goal. Proposed Implementations consist range of features in data loss prevention such as deep content analysis, secure wiping of sensitive data, encryption of sensitive data. The combination of both machine learning techniques, signature, and behavioral based analysis will be used to craft a tool which is integrated into the system that outputs less false negative results. Apart from identification and classification generation of IT administrator alerts, Quarantine identified malware can be listed as additional features provided by the tool. The platform which supports deploying multiple vulnerability scanning tools together makes the end product unique from other existing COPE solutions provides a vast amount of advantages including mobile device scanning individually or at once, report generation and also it reduces the workload of IT administrator.Publication Embargo Credit Card Fraud Prevention Using Blockchain(IEEE, 2021-04-02) Balagolla, E. M. S. W; Fernando, W. P. C; Rathnayake, R. M. N. S; Wijesekera, M. J. M. R. P; Senarathne, A. N; Abeywardhana, K. YWith the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. When blockchain systems' immutability meets smart contracts, third-party removal and decentralization could be met as a high level of security. Proposed blockchain with fraud detection technology will assist to mitigate fraudulent credit card transactions due to its intermediate parties. Authors propose a solution (B-Box.com) where credit card transactions are modeled on a blockchain so that the credit card processing can be decentralized and verifiable with an accredited set of computing nodes. This solution reduces fraud due to ambiguous contracts with the use of a smart contract between the bank and the customer. Also, this project includes a scaling mechanism to blockchain because the current projects have a lack of scalability. Moreover, the solution introduces a proactive anomaly detection to detect fraudulent credit card transactions, in which the system will resist frauds before the fraudulent transaction enters the blockchain. So the proposed solution will make transparency between the banks and the end-users and at the same time prevents frauds before it happens which helps the banks to save millions in otherwise lost due to fraud.