Browsing by Author "Senarathne, A. N"

Now showing 1 - 16 of 16

Embargo
Air Visio: Air Quality Monitoring and Analysis Based Predictive System
(IEEE, 2019-12-05) Dissanayaka, A. D; Taniya, W. A. D; De Silva, B. P. A. N; Senarathne, A. N; Wijesiri, M. P. M; Kahandawaarachchi, K. A. D. C. P
Sri Lanka is facing a serious air pollution problem that severely impacts the daily life of every Sri Lankan. The main source of ambient air pollution in Sri Lanka is vehicular emissions. A methodology to monitor the air quality in real-time with an overall coverage of Sri Lanka, and automatically process these huge data to identify air quality levels in a specific area is now becoming a timely research topic. An air quality monitoring and analysis based predictive system is proposed to monitor the ambient air quality, provides the best route with minimum polluted air, maps the heatmaps to identify the current air quality of an area easily and predict the future air quality of each area. The prototype was implemented by hierarchically deploying two different gas sensors, an Arduino Uno board and a wifi module, to implement in open spaces between smart buildings, and transfers the sensor data back to the information processing center by using IoT technology for real-time display. The information processing center stores real-time information which is collected from the sensors to the database. By reading sensor data stored in the database, the front-end system draws real-time, accurate air quality levels included maps and predicts the less polluted routes and the air quality level over an area. Further, an energy harvesting system is also presented for the power consumption of the device. A route is suggested in an accuracy of 70% from this system. The final product provides a low cost, highly portable and easily maintainable system for the users.
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. Y
With 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.
Embargo
Enhanced Secure Solution for PoS Architecture
(IEEE, 2019-12-05) Samaranayake, C; Kuruppu Achchige, R. P; Shanaz, T; Ranasinghe, A; Senarathne, A. N
Today 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.
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. Y
Mobile 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.
Embargo
Intelligent SOC Chatbot for Security Operation Center
(IEEE, 2019-12-05) Perera, V. H; Senarathne, A. N; Rupasinghe, L
Information security analysts currently face many challenges: both hidden and visible in the face of unique attack records. The rapid increase patterns of security monitoring and investigation tools (as an average of 20 security solutions have been used per company) leads to frequent changing between screens, alert fatigue, disjointed record keeping, and increased investigation time. This chatbot can suggest the flow of investigation and the relevant commands that will help to obtain the results which need to be resolved the incident. Automate the incident ticket creation is one of major achievement of this research. Security analysts also receive messages of security alerts of the AWS hosted instances. Security analysts are also continuing to work on their sub tasks, quite overloaded with their main tasks to engage in collaborative investigations and knowledge sharing. Chat-Ops help to vanquish and meet those challenges. Processes, automated workflows, the chatbot, security tools, and humans exist in the same chat window feeding data and commands in a worthy cycle. It will lead to huge changes in everything from remediation times and investigation depth to future learning and knowledge administration. Different analysts will drive the investigation in different ways. Most of the time, analysts will miss most important parts and techniques, but those parts could be very valuable for the result. The investigation flow and commands will suggest based on past investigations and commands that previous analysts were used. This chatbot will help in many ways of current analyst who work in a security operation center.
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.N
In 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.
Embargo
The Next Gen Security Operation Center
(IEEE, 2021-04-02) Perera, A; Rathnayaka, S; Che, C; Madushanka, W. W; Senarathne, A. N
Due 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.
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. L
Cheque 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.
Embargo
R-Killer: An email based ransomware protection tool
(IEEE, 2018-08-08) Lokuketagoda, B; Weerakoon, M. P; Kuruppu, M. U; Senarathne, A. N; Abeywardena, K. Y
Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user's data to public threat intelligence services, hence protecting the confidentiality of user data.
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. N
With 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.
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. N
It'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.
Embargo
Security Platform for Mobile OS
(IEEE, 2019-12-05) Benett, A. S. B; Vinushanth, K; Ranjitha, L; Abisherk, R. S; Senarathne, A. N
Evolution 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.
Embargo
Smart Human Resource Management System to Maximize Productivity
(IEEE, 2020-12-17) Hewage, H. A. S. S; Hettiarachchi, K. U; Jayarathna, K. M. J. B; Hasintha, K. P. C; Senarathne, A. N; Wijekoon, J
Human resource is one of the most valuable assets in an organization. They are bounded to develop the unique and dynamic aspects that strengthen their competitive advantage to persist in an always changing market environment. In order to recruit a quality candidate for an organization, reducing human involvement and verifying details of the candidate is important in recruitment process. Furthermore, having an idea about how well or poor the employees perform, and how likely the employee attrition can occur is vital in human resource management process. This paper is an attempt to introduce smart human resource management system that can maximize the productivity of an organizational environment using machine learning and blockchain technologies. The end goal of this research is a smart human resource management system that reduces human judgment, time in the candidate selection process and predicts employee performance and attrition to motivate current employers to maximize productivity with minimal financial loss in the workplace environment. Skill assessment and resume classification have been done using unsupervised learning algorithms and natural language processing after extracting raw data from employee resumes using Object Character Recognition. Candidate details verification is done by comparing the hashes of the records which are stored in the blockchain. Employee performance and attrition are predicted using supervised machine learning classification techniques with high accuracy and the result of the final performance is generated as a score for each employee considering the multiple attributes that has been standardized and regulated by some specifically considered e-competence frameworks.
Embargo
Smart Office Automation System for Covid Prevention
(IEEE, 2021-12-09) Rajapaksha, R. A. D. S; Costa, L. S; Prasanna, P. L. U. S. C; Disanayaka, A. P. D; Senarathne, A. N; Wijekoon, J
Today, this coronavirus is spread all around the world. Most organizations and businesses start to think about how to continue their business in a situation like COVID-19 and their employees’ health and business security. To avoid and be safe from this type of disease, there are some common rules to follow. Keeping a distance, wearing a mask, cleaning our hands, are some health guidelines from them. According to the current situation, many inventors are trying and have already given some solutions to avoid these kinds of situations aligning with health guidance’ provided by WHO. With the advantage of advanced modern-day technologies and ideas, researchers started to think about how to face situations like these with the new technologies and found that many users are highly interested and motivated with automated systems. Thus, from this study, we aim to provide a fully automated office management system to prevent corona with advanced technology in combination with IoT technologies, Machine learning, Cloud technologies, and sensor technologies. Considering the security aspect, Controlling the main entrance, identifying, ensuring user’s authentication before entering the building, and monitoring employee activities are very significant aspects of the study. As the result of the study, the combination of IoT technologies and Machine Learning with deep learning mechanisms have guaranteed organizational business continuity, employees' health, and security.
Embargo
A steganography-based fingerprint authentication mechanism to counter fake physical biometrics and trojan horse attacks
(IEEE, 2021-12-06) Karunathilake, H; Shahan, A. R. M; Shamry, M. N. M; De Silva, M. W. D. S; Senarathne, A. N; Yapa, K
In the modern world, unique biometrics of every individual play a vital role in authentication processes. However, as convenient as it seems, biometrics come with their own set of drawbacks. For instance, if a passphrase is compromised (which is highly likely), changing it to a new passphrase would solve the issue. However, when someone's biometrics are compromised, there is no turning back. Simultaneously, biometric systems are often compromised due to the use of fake physical biometrics and trojan horse attacks that are capable of modifying the authentication process to fulfill a malicious user's intents. This research focuses on proposing a novel and secure authentication process that uses steganography. This “all-in-one” solution also focuses on mitigating the aforementioned drawbacks with the use of four modules, namely, the feature extraction module, the payload generation and authentication module, the fake physical biometrics countering module and the trojan horse countering module. This solution is implemented such that the idea behind it can be easily adopted to enhance the existing biometric authentication systems as well as improve the overall condition and user experience of the multi-factor authentication processes that are widely in use today.
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. N
An 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.