http://dlib.iit.ac.lk/xmlui/handle/123456789/681 Tue, 07 Apr 2026 12:04:35 GMT 2026-04-07T12:04:35Z http://dlib.iit.ac.lk/xmlui/handle/123456789/686 Analyze The Intuition Readiness On implementing Personal Data Protection Regulation, Within Private sector Higher Academic Institutes In Sri Lanka, A study Of Data Protection Draft 2019, Mithrananda, Madusanka Chamara Personal data protection has become one of the hot topics in countries which process European Union citizen data after enacting General Data Protection Regulation (GDPR) from 2018 May 25. In Sri Lankan context, there are minimum limitations to handle personal data while the available regulations focused only on a few business sectors. With international influence, Sri Lanka is also in a process to establish separate legislation amendments specifically for personal data protection along with a legal draft on cyber security. The finalized act (pending approval from the parliament of Sri Lanka) on personal data protection states eight obligations for the data processor and four rights to the data subject to safeguard personal data of Sri Lankan citizens. To comply with these rights and obligations there should be properly placed processes, systems, and internal culture within the Sri Lankan institutes. In this study, it checked whether there are competencies to comply with the data protection acts in private sector higher educational institutes in Sri Lanka. Thus the research is exploratory in nature with case study approach is used. Research findings suggest there are nor or minimum compliance is there for selected data protection obligations and data subject rights. A new conceptual framework is proposed with six privacy principles and four step method to reduce the compliance gap with the local and global privacy legislations. Wed, 01 Jan 2020 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/686 2020-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/685 DEEPASSESSOR: A context -Aware Deep Learning-Based Solution For Multi Class Fake News Detection Sandakelum, Delankage Akila Fake news is a type of story that has no basis in fact but presented as being factually accurate. It may have misleading, false, imposter, manipulated, fabricated content, or satire, parody, and propaganda with the intent to mislead people. During the last few years, there has been year-on-year growth in information emerging from various social media networks, blogs, Twitter, Facebook, etc. Detecting fake news, in proper time, is very important as otherwise, it might cause damage to social fabrics. That has gained much interest worldwide due to its impact on numerous aspects of life, politically, economically and socially. Ever since the 2016 US presidential election where fake news campaigns were launched to gain political advantage by misleading people, false news detection in the political domain has drawn significant attention in worldwide (Bovet and Makes, 2019). Detecting fake news is significantly challenging due to many reasons: Firstly, language usage in fake news is complicated. Secondly, Fake news usually mixes true stories with false details, confusing to be recognized correctly. Thirdly, the fake news data sets are limited to carrying out extensive research identifying fake news patterns. Numerous researches are currently carried out in data analysis, machine learning, and deep- learning aspects to identify the patterns and correlations between various fake news factors. Using traditional text mining and machine learning techniques is insufficient to detect fake news due to the aforementioned unique challenges in fake news. Utilizing deep learning in false news identification is the new state-of-the-art and seems promising. This project presents a novel, context-aware, deep learning solution to identify the varies of the truths or credibility of the news statements in the political domain by utilizing external supportive claims. A supervised deep learning model is implemented by integrating many different network architectures that could explore the various features of speaker profiles and outside justification. Our deep learning model surpasses the current state-of-the-art with a 46.47 of f1 score and 46.97 of accuracy in six-way classification and 75.76 of f1 score and 76.04 of accuracy in two-way classification. A user-friendly interface is also developed to make predictions on the new political statements by integrating the best deep learning models produced by the research. Wed, 01 Jan 2020 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/685 2020-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/684 Data Acquisition Methodology for Database Forensic Senarath, S.D.D.S The database crimes are increasing day by day and there is a large number of harmful incidents have occurred due to the database attacks. The large organizations have lost their profits and reputation because of these incidents. These crimes harm the confidentiality, integrity, and availability of database systems. The traditional database security mechanisms do not ensure the accuracy of data protection and that is why database forensic is much important for an organization. Then the evidence of the crimes should be identified and collected accurately, and the table records should not be tampered. Therefore, a data acquisition method should be required in database forensic to retrieve the evidence without any harm to the actual database records. This research was conducted to find an accurate data acquisition method to collect the evidence in database forensic without tampering the table records. A survey using a questionnaire was conducted with the participation of experienced employees related to database administration and system engineering from different organizations. Interviews were also held with industry experts and gathered information regarding data acquisition in database forensic. An experimental analysis was done using the identified steps of the new data acquisition method in a real time environment using the resources of an organization and with the instructions of some industry experts. After the analysis of the survey results, interview results and the experiment conducted within a real environment, the framework for the data acquisition methodology was created and identified the steps for the data acquisition in detail. According to the survey results the data acquisition method is mainly required for large organizations who are handling large volumes of data and most of the organizations are related to finance and banking sectors. The framework was designed and developed specially for Oracle databases. This data acquisition method allows to gather evidence and report to the relevant legal parties without tampering the original data or table records. Wed, 01 Jan 2020 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/684 2020-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/683 Person Verification Using Linguistic Profiling For Continuous Authentication Based On ML Esufali, Murtaza Anverali Usage of mobile devices has increased exponentially over the past decade. The wide use has brought with it a variety of concerns including security. Currently, the most widely used method of authentication for devices is PINs, biometric fingerprint scanning and so on. These methods and their limitations have given rise to continuous authentication mechanisms. This involves verifying a user implicitly and continuously without causing a hindrance i.e. in the background while the user is using his/her device. Person verification is the process that involves verifying a person uniquely using specific traits they have. Various advancements have been made to carry out this type of verification including usage of linguistic, behavioral profiling, tracking user location, and gait dynamics and many more. Linguistic profiling involves using the user’s stylometry for verification i.e. verify using his/her writing style. This research uses various machine learning techniques combined with dynamic feature extraction to create a platform that can be used by operating systems and chat application developers to implement continuous authentication using linguistic profiling. An additional use of this platform includes forensic analysis. The platform developed accepts What Sapp chats which it then pre-processes, extracts static and dynamic features, and builds relevant models. The models are built using the Support Vector Machines (SVMs), Multi-Layer Perceptron (MLPs) and Ensemble algorithms. This dynamic feature extraction process identifies the most common words, emoji’s, word extensions etc. used by the individual. Chat messages can then be posted to predict if the message is from an imposter or the actual user. The results obtained have been commendable. It includes an accuracy of 75.81%, FAR of 8.62% and FRR of 15.58% respectively. Wed, 01 Jan 2020 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/683 2020-01-01T00:00:00Z