Now showing items 1-5 of 5

    • Analysing Facebook user risk using machine learning algorithm 

      Barua, Arnab; Adnan, Fahim; Ghosh, Ananna (Brac University, 2020-04)
      Now-a-days people exchange their personal information and interact with companions and close relatives in a way which is revolutionized. In any case, the majority of them don’t have the foggiest idea how to utilize, where ...
    • Data security model using deep learning and edge computing for Internet of Things (IoT) in smart city 

      Aziz, Md. Yasin; Kabbo, Tanjid Alam; Tahsin, Tasnuva; Zumme, Nadia Haque; Tahsin, Mohammad Sadman (Brac University, 2021-09)
      In the current ongoing world of the IoT (Internet of Things) devices, it is absolutely vital to have a safe, secure and reliable cyberspace. A cyberspace or network where it is free from all sorts of unethical activities ...
    • Malware Detection Using Neural Network 

      Kayum, Syed Irfan; Hossain, Humaira; Tasnim, Nafisa; Paul, Arja; Rohan, Alim Aldin (Brac University, 2021-01)
      One of the great and major issues facing the Internet today is a large amount of data and files that need to be analyzed for possible malicious purposes. Malicious software also referred to as an attacker’s malware is ...
    • PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework 

      Rahman, Tahsinur; Ahmed, Nusaiba; Monjur, Shama; Haque, Fasbeer Mohammad; Kabir, Naweed (Brac University, 2023-01)
      As the world is moving more and more towards a digital era, a great majority of data is transferred through a famous format known as PDF. One of its biggest obstacles is still the age-old problem: malware. Even though ...
    • Performance analysis of machine learning algorithms for Malware classification 

      Bushra, Raisa Hasan; Alam, Md Taukir; Saha, Aniruddho; Fahim, Nazmus Sakib; Binty, Nabila Mourium (Brac University, 2022-09-29)
      Malware detection research has been popular over the years as the variations and complexity of malware attacks are increasing daily. Using variously Supervised and Unsupervised machine learning algorithms to detect, ...