Project Management

[Audio] Ladies and gentlemen, welcome to our presentation on the Data Leaks Detection System project. We are excited to share with you our findings and results from this project, which was undertaken by the Department of Computer Science and Engineering. Our team members, Jana Vikram K M S, Rajasekaran K, and Satheeshkumar V, have put in countless hours of hard work and dedication to make this project possible. Under the guidance of our project guide, Dr. A. Anandh, we have developed a system that is designed to detect and prevent data leaks. In today's digital age, data is a valuable asset and it is imperative to protect it from unauthorized access or leaks. Our project aims to address this issue and provide a solution to detect and prevent data leaks. As you can see on the slide, our project is titled "Data Leaks Detection System" and it was completed as a part of our CS2451-Project Work. We have also included our department's logo to showcase the support and resources we have received from our university. Throughout this presentation, we will be sharing the details and features of our system, as well as the methodology and algorithms used in its development. We believe that our project has great potential to help organizations and individuals protect their data and prevent any potential leaks..

[Audio] Data security is a major concern in today's digital world, where sensitive information is vulnerable to leaks. Unauthorized sharing of confidential files can lead to severe financial and reputational damage. Identifying the source of data leaks is crucial to preventing further breaches. Our project detects and traces leaked files using watermarking techniques and machine learning. By embedding unique watermarks for each user, we can track the origin of a leaked document. This approach ensures accountability and discourages unauthorized distribution. With an advanced admin dashboard, we provide monitoring tools to track file access and user activities. The system enhances security by logging interactions and analyzing watermark differences. This helps organizations take preventive actions and safeguard their critical data..

[Audio] Our project aims to develop a data leaks detection system that utilizes unique watermarks and machine learning algorithms to identify and track unauthorized file sharing. The system will embed these watermarks into user-specific data before sharing, allowing us to verify ownership and detect any potential leaks. Furthermore, our system will employ machine learning to analyze extracted watermarks and pinpoint the source of leaked documents. Additionally, we will implement an admin dashboard to monitor file access and user activities in real-time, providing organizations with the necessary tools to prevent and respond to data breaches..

[Audio] The data leak detection system has identified several research papers that have made significant contributions to the field of data leakage detection. The authors of these papers have proposed various techniques to detect data leaks, including surveys of existing methods, classifications, and comparisons of their effectiveness. One paper suggests using machine learning to ensure data integrity by preventing alterations, while another employs statistical analysis and threshold-based detection to identify anomalies. These findings highlight the significance of ongoing research in this area to develop more effective solutions for detecting data leaks..

[Audio] The authors have analyzed numerous papers published in prestigious journals like IEEE Transactions on Information Forensics and Security, Journal of Network and Computer Applications, and International Journal of Computer Science and Information Security. Their research reveals the efficacy of diverse approaches, encompassing convolutional neural networks on network packets, hybrid machine learning and anomaly-based techniques, and flow-based analysis with statistical features. These studies illustrate the potential of these methods in detecting data leaks, offering valuable insights for developing more effective and precise data leakage detection systems..

[Audio] Researchers have explored various approaches to prevent data leakage, including watermarked and key-sharing techniques on network packets, as proposed by A. Kumar and P. Singh in their 2022 IEEE Access paper. They aimed to detect unauthorized access to sensitive information. Moreover, M. Brown and T. Green developed a robust data security framework by combining machine learning and watermarking techniques, as presented in their 2021 Journal of Cyber Security article. Additionally, S. Patel and R. Mehta identified anomalies in network traffic using flow-based analysis, statistical features, networking, and machine learning, as described in their 2020 Computers & Security publication. These studies showcase the diverse approaches used to address data leakage prevention, emphasizing the need for ongoing research and innovation in this field..

[Audio] The literature survey shows that different methods have been suggested to stop data leakage in businesses. Artificial intelligence-driven approaches have enhanced the precision of detecting data leaks, whereas blockchain-based systems guarantee data integrity and block unauthorized access. Moreover, hybrid techniques combining artificial intelligence and watermarking offer a robust framework for identifying insider threats. These discoveries illustrate the efficiency of merging multiple technologies to boost data security..

[Audio] The literature survey reveals that watermarking and machine learning are crucial in detecting and preventing data leaks. Various approaches, including digital watermarking, have been employed to track and identify insiders responsible for data leaks. Combining watermarking with machine learning has been shown to significantly enhance data leak detection in cloud environments. This comprehensive review serves as a solid foundation for our project's objectives, highlighting the efficacy of these technologies in addressing the issue of data leaks..

[Audio] The development plan outlines five stages involved in creating the Data Leaks Detection System. The first stage, planning and requirements gathering, takes one week. The second stage, system design, also takes one week. The third stage, backend and database development, watermarking, and leak detection implementation, lasts for two weeks. During this stage, we will develop the web application's core functionality, including data processing, storage, and retrieval. The fourth stage, monitoring, testing, and documentation, takes two weeks, during which we verify the system's performance, identify bugs, and ensure it meets the required standards. The final stage, documentation of the work, takes one week, where we summarize the key findings and outline the next steps. Throughout these stages, we ensure that our deliverable, the web application for data leaks detection, meets the expected quality and functionality..

[Audio] The project timeline has been divided into twelve weeks, with specific activities assigned to each week. Week one is dedicated to front-end development, where we will design and implement the user interface and user experience of our Data Leaks Detection System. This includes creating the necessary templates, layouts, and visual elements to ensure a seamless user experience. In the second week, we will focus on designing the Data Leaks Detection System itself, including defining the system's architecture, identifying the key components, and determining how they will interact with each other. This will involve creating detailed diagrams and flowcharts to visualize the system's structure and functionality. From weeks three to five, we will implement the database and watermarking technology required for our system. This involves setting up the database schema, populating it with sample data, and implementing the watermarking algorithm to detect potential leaks. We will also develop the necessary APIs and interfaces to integrate the database and watermarking technology with the rest of the system. During weeks six to eight, we will conduct thorough testing and validation of our system. This includes simulating various scenarios, detecting potential leaks, and verifying that the system functions correctly. We will also perform unit testing, integration testing, and regression testing to ensure that all components work together seamlessly. Finally, from weeks nine to twelve, we will disseminate the results of our project, write the final report, and document our findings. This includes summarizing our achievements, highlighting any challenges we faced, and providing recommendations for future improvements..

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[Audio] The Data Leaks Detection System's architecture consists of four primary components, each serving a distinct purpose. The Registration Module handles user sign-ups and verification processes, ensuring secure access to the system. The Admin Module manages user profiles, document uploads, and monitors file access logs, providing administrators with a comprehensive overview of system activity. Users can utilize the User Module to download files embedded with unique watermarks and report any suspected leaks. The Leak Detection Module extracts and compares these watermarks to identify the source of unauthorized data distribution. This multi-faceted approach enables the system to effectively detect and trace data leaks, thereby maintaining the integrity of sensitive information..

[Audio] The acceptance plan specifies that we must embed unique watermarks into user-specific data prior to sharing it. This enables us to verify ownership by extracting the watermark from the shared data. Furthermore, we will detect leaked data and identify its source by extracting the watermark and mapping it to the original recipient. Our simulated data leaks will serve as a test case to evaluate the efficacy of this process..

D. Results. 15. Cloud-Based Data Security Platform Home Product Terms & Conditions Contact Overview About Welcome to Our Secure Cloud Platform Experience seamless and secure data management in the cloud. Our platform ensures sensitive files are monitored and protected through powerful watermarking, access tracking. and user verification systems, guilt on Cloud technologies, this solution is designed to help prevent and trace any data Sharing or leakage Let'S secure the cloud together. About This Platform.

D. Results. 16. [image] w.. Fig. 2 Login Page.

D. Results. 17. [image]. Fig. 3 Registration Page.

[Audio] The admin dashboard page offers various features and tools for administrators to manage user accounts, view file logs, activity logs, and message system. Additionally, it enables uploading important documents and checking leaked files. This facilitates effective monitoring and control of data leaks within an organization..

[Audio] The user dashboard page shows the username "raja" accompanied by a welcome message. The system offers access to various functions including messaging, inbox, and logout options. This page acts as a central hub for users to oversee their actions within the Data Leaks Detection System..

[Audio] The user can access their profile information from this page, where they can view their current password and update it if necessary. The option to change the password is available, but not mandatory. After clicking the "Back" button, the user will return to the previous page..

[Audio] The system permits administrators to regulate user accounts, including modifying and erasing existing users, as well as restricting access to specific users. The dashboard presents a list of all registered users, offering choices to modify, erase, or block each account. This capability empowers administrators to preserve control over who has access to the system and guarantees data security..

D. Results. 22. php File & Message Logs (Excluding Admin) 13 12 Sender sath123 raja sath123 sath123 sath123 sath123 Receiver sath123 Content hi: hh today work: complete this hello: this is impotant hello: this is intpotant Filename S.png cl.png 1739536222_335850.jpg 1739534011 _god 1739533946_wp3022745Jpg 1739533724_wp3022745.jpg Back to Dashboard Timestamp 2025-03-13 18:25:06 2025-02-14 2025-02-14 2025-02-14 2025-02-14 2025-02 .14 Actions Dcw.•nload Download Dcwvnload Download Ocrø.•nload aelete Delete Delete aelete 18-03-2025.

[Audio] The system records user login and logout activities, providing a comprehensive account of all user interactions with the application. The logs reveal that users sath123 and admin have been actively engaged with the system, with numerous logins and logouts documented. Timestamps indicate that these events transpired between March 13th and March 14th. A thorough examination of these logs may uncover patterns and trends in user behavior, which could inform security protocols to thwart unauthorized access..

[Audio] The system permits users to choose a recipient from a list of accessible users, including administrators. Users can then craft a message with optional attachments, like files. Although the subject line is not mandatory, it may be included if preferred. After composing the message, users can dispatch it to the chosen recipient. This functionality facilitates effortless communication among users and administrators within the organization..

[Audio] The admin inbox page shows a list of messages sent by users, featuring sender details, subject lines, and timestamps. By clicking on each message, the admin can view its contents, and reply to it. Additionally, the admin can download any attached files. The admin can sort the messages by date range using the timestamp option..

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[Audio] The user can navigate back to the dashboard from here, compose a new message, attach a file, or query the system. This functionality enables users to efficiently manage their messages and files within the system..

[Audio] The user accesses their inbox page to view all messages and files received from other users. Each message displays the sender's name, subject, and timestamp. The user can reply to a message by clicking the reply button, download attachments, or sort messages by sender, subject, or timestamp. This enables efficient management of incoming messages and files..

[Audio] The user inbox page shows a list of emails received by the user, displaying the sender's name, subject line, and timestamp of receipt. Users can reply to an emails by clicking on the reply button, enabling them to communicate with others through email within the system..

[Audio] The Computer Course Management System is a crucial component of our Data Leaks Detection System, designed to efficiently manage and organize all the courses offered by our organization. The courses_id in this system serves as a unique identifier for each course, which helps in tracking and managing the data associated with it. This not only makes it easier for our team to monitor the courses, but also ensures that the data is secure and can be easily accessed by authorized personnel. Our Data Leaks Detection System is a comprehensive project that aims to identify and prevent any potential data leaks within our organization. With the increasing number of cyber threats, it has become essential to have a system in place that can detect and address any data breaches effectively. Under the guidance of our esteemed mentor, Dr. A. Anandh, our team consisting of Jana Vikram K M S, Rajasekaran K, and Satheeshkumar V have worked tirelessly to develop this system. And with the addition of the Computer Course Management System, we are confident that our organization will be able to enhance its security measures and streamline its course management processes..

[Audio] The system permits users to upload leaked files, which are subsequently identified by the Ctæ algorithm. This procedure facilitates the recognition of potential data breaches, enabling prompt action to counteract any harm. The uploaded file undergoes analysis via the Ctæ algorithm, which examines its contents to ascertain whether it is a genuine file or a malevolent one. If deemed malicious, the system can initiate suitable actions such as erasing the file or notifying authorities. This capability empowers organizations to proactively detect and respond to data leaks, thus reducing the likelihood of sensitive information being compromised..

[Audio] The process of extracting a leaked watermark from the data involves locating the leaked data within the dataset by comparing it with the original source and identifying any differences or alterations. Once the leaked data has been identified, the next step is to extract the watermark using specialized algorithms designed to detect and isolate it from the rest of the data. This extraction process is crucial in determining the source of the leaked data and the potential consequences, allowing for further investigation and measures to prevent future leaks..

[Audio] Implementing watermarking techniques and detection systems can significantly enhance data security. This is achieved by protecting personal information, intellectual property, and confidential business data from unauthorized access and leaks. Organizations can ensure compliance with various data protection regulations, such as GDPR, HIPAA, and others, thereby avoiding legal penalties and maintaining their reputation. Furthermore, safeguarding sensitive information enables organizations to build trust with their customers and stakeholders, leading to increased customer loyalty and a positive reputation in the market. Moreover, preventing data leaks can save organizations from significant financial losses associated with breaches, including fines, legal fees, and loss of business..

[Audio] The implementation of our data leak detection system using watermarking techniques has yielded a robust solution for safeguarding sensitive information. By embedding unique identifiers into data and employing advanced detection methods, we have enhanced data security, ensured regulatory compliance, and fostered trust among stakeholders. Our innovative approach not only mitigates the risk of data breaches but also contributes to the broader field of cybersecurity, promoting further research and development. Ultimately, this project underscores the importance of proactive measures in protecting valuable digital assets in an increasingly interconnected world..

[Audio] Future work will focus on refining our watermarking techniques to resist tampering and unauthorized alterations. We plan to integrate AI-driven anomaly detection to boost leak prediction and detection precision. Furthermore, we intend to implement blockchain technology for secure storage of access logs, thereby strengthening overall security. Additional enhancements will include simplifying the user experience with a more intuitive interface, supporting additional file formats for watermarking, and extending the system's capabilities to detect leaks in multimedia content like images and videos. By incorporating these advancements, our system will continue to evolve as a potent tool for preventing unauthorized data disclosures..

[Audio] The proposed system has been designed to detect data leaks efficiently by utilizing watermarking techniques. Unique identifiers are embedded within the data, allowing for the tracking of data movement and identification of potential leaks. This approach builds upon research studies by various authors, including Barni et al., Boneh and Shaw, Chandramouli and Subbalakshmi, Doe, Gentry, Holambe et al., and Jain et al. Their contributions have led to the development of robust watermarking techniques, which have been incorporated into our system. Simulations have demonstrated the system's effectiveness in detecting simulated data leaks and identifying their sources..

[Audio] The data leaks detection system has been designed to provide a comprehensive solution for identifying and preventing unauthorized data sharing. Unique watermarks embedded into user-specific data allow for accurate tracking and identification of leakers. Machine learning algorithms enable swift action against potential threats by detecting and analyzing patterns in data usage. Real-time monitoring and automated leak detection provide a robust defense against data breaches. References [8], [12] have contributed significantly to the development of this technology, providing insights into watermarking techniques and their applications in data security. Further research and development are necessary to ensure the continued effectiveness of this system in the face of evolving cyber threats..

[Audio] Mercy Praba C. and Satyavathi G.'s paper, titled "A Technical Review on Data Leakage Detection and Prevention Approaches", published in 2023, provides a comprehensive review of different approaches for detecting and preventing data leaks. Nayak S. K. and Ojha A. C.'s paper from 2020, titled "Data Leakage Detection and Prevention: Review and Research Directions", discusses the current state of data leakage detection and prevention and suggests future research directions. Patel A. et al.'s 2013 paper on "A survey of intrusion detection systems in cloud" explores different intrusion detection systems used in cloud computing. Popescu A. C. and Farid H.'s paper from 2005 on "Exposing digital forgeries by detecting duplicated image regions" presents a technique for detecting digital forgeries by identifying duplicated image regions. These papers have been instrumental in our project and have provided valuable insights in the field of data leakage detection and prevention..

[Audio] The International Conference on Innovations in Engineering, Management and Science, ICIEMS-2025, is being organized by the Research & Development Cell and Department of Computer Science and Engineering jointly. The conference will take place on January 31st and February 1st. We are presenting our project report on the Data Leaks Detection System at this prestigious conference. Our project, developed by Jana Vikram K M S, Rajasekaran K, and Satheeshkumar V under the guidance of Dr. A. Anandh, focuses on detecting data leaks. We are excited to be a part of this conference and hope to contribute to advancements in engineering, management and science..

Conference Certificate. 40. RSP CONFERENCE 11 UB CERTIFICATE OF ACHIEVEMENT This is to Certify that Dr.A.Anandh iiiiiä Associate Professor, Department Of Computer Science And Engineering, Kamaraj College Of Engineering And Technology, Tamilnadu, India. won the Best paper presentation under the UG Category award for the research paper entitled Data Leaks Detection Using Cloud Computing at the "International Conference on Innovations in Engineering, Management and Science (ICIEMS)-2025" - Organized by Research & Development Cell & Department of CSE; Harcourt Butler Technical University (HBTU), Kanpur, Uttar Pradesh, India & Event Organizer: RSP Conference Hub, Coimbatore, Tamil Nadu, India on 31st January & 01st February 2025. Dr. S.V.A.R. Sastry Associate Dean, Research & Development, HBTU. Kanpur, Uttar Pradesh, India. Certificate No: Prof. Raghuraj Singh Dean, Research & Development, HBTU, Kanpur, Uttar Pradesh, India. HBTU & RSP ICIEMS 2025-8020.

[Audio] Mr. K M S Jana Vikram, a student from the Department of Computer Science and Engineering at Kamaraj College of Engineering and Technology, Tamil Nadu, India, won the Best Paper Presentation under the UG Category award for his research paper titled "Data Leaks Detection Using Cloud Computing" at the International Conference on Innovations in Engineering, Management and Science (ICIEMS)-2025. The conference was organized by the Research & Development Cell and Department of CSE at Harcourt Butler Technical University (HBTU) in collaboration with RSP Conference Hub, Coimbatore, Tamil Nadu, India. The certificate bears the signature of Dr. S V A R Sastry, Associate Dean, Research & Development, HBTU, and Prof. Raghuraj Singh, Dean, Research & Development, HBTU. Certificate Number HBTU & RSP ICIEMS 2025-8017..

[Audio] Mr. K. Rajasekaran, a student from the Department of Computer Science and Engineering, Kamaraj College of Engineering and Technology, Tamil Nadu, India, was awarded the Best Paper Presentation under the UG category for his research paper titled "Data Leaks Detection Using Cloud Computing" at the International Conference on Innovations in Engineering, Management and Science (ICIEMS) - 2025. The conference was organized by Research & Development Cell and Department of CSE, Harcourt Butler Technical University (HBTU), Kanpur, Uttar Pradesh, India, and Event Organizer: RSP Conference Hub, Coimbatore, Tamil Nadu, India. The award was presented by Dr. S V A R Sastry, Associate Dean, Research & Development, HBTU, Kanpur, Uttar Pradesh, India, and Prof. Raghuraj Singh, Dean, Research & Development, HBTU, Kanpur, Uttar Pradesh, India. The certificate number is HBTIU & RSP ICIEMS 2025-8018..

[Audio] Mr. V. Satheeshkumar, a student from the Department of Computer Science and Engineering at Kamaraj College of Engineering and Technology in Tamil Nadu, India, received the Best Paper Presentation under the UG Category award for his research paper titled "Data Leaks Detection Using Cloud Computing" at the International Conference on Innovations in Engineering, Management and Science (ICIEMS)-2025. The conference was organized by the Research & Development Cell and Department of CSE at Harcourt Butler Technical University (HBTU) in Kanpur, Uttar Pradesh, India, along with the Event Organizer, RSP Conference Hub, in Coimbatore, Tamil Nadu, India. The ceremony took place on January 31st and February 1st, 2025. The certificate was issued by Dr. S V A R Sastry, Associate Dean of Research & Development at HBTU, and Prof. Raghuraj Singh, Dean of Research & Development at HBTU. The certificate number is HBTU & RSP ICIEMS 2025-8019..

[Audio] The speech has been written based solely on the provided slides and given context, without utilizing any external resources or conducting research. Its purpose is to offer a clear and concise summary of the system's capabilities and features. Review it thoroughly before presenting it..

Journal Publication. March 2025 | 1 1 10 | ISSN: 2349-6002 Data Leaks Detection Using Cloud.