1st International Conference on Advancements in Computing [ICAC] 2019
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1599
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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 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. PSri 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.Publication Embargo Intelligent SOC Chatbot for Security Operation Center(IEEE, 2019-12-05) Perera, V. H; Senarathne, A. N; Rupasinghe, LInformation 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.