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Articles 1 - 5 of 5
Full-Text Articles in Cybersecurity
Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand
Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand
LSU Master's Theses
File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …
Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem
Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem
School of Cybersecurity Faculty Publications
The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …
Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington
Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington
School of Cybersecurity Faculty Publications
Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …
Characterizing Location-Based Electromagnetic Leakage Of Computing Devices Using Convolutional Neural Networks To Increase The Effectiveness Of Side-Channel Analysis Attacks, Ian C. Heffron
Theses and Dissertations
SCA attacks aim to recover some sort of secret information, often in the form of a cipher key, from a target device. Some of these attacks focus on either power-based leakage, or EM-based leakage. Neural networks have recently gained in popularity as tools in SCA attacks. Near-field EM probes with high-spatial resolution enable attackers to isolate physical locations above a processor. This enables attackers to exploit the spatial dependencies of algorithms running on said processor. These spatial dependencies result in different physical locations above a chip emanating different signal strengths. The strengths of different locations can be mapped using the …
Isolation Of Ddos Attacks And Flash Events In Internet Traffic Using Deep Learning Techniques, Alex Frank Mongi
Isolation Of Ddos Attacks And Flash Events In Internet Traffic Using Deep Learning Techniques, Alex Frank Mongi
Tanzania Journal of Engineering and Technology (TJET)
The adoption of network function visualization (NFV) and software defined radio (SDN) has created a tremendous increase in Internet traffic due to flexibility brought in the network layer. An increase in traffic flowing through the network poses a security threat that becomes tricky to detect and hence selects an appropriate mitigation strategy. Under such a scenario occurrence of the distributed denial of service (DDoS) and flash events (FEs) affect the target servers and interrupt services. Isolating the attacks is the first step before selecting an appropriate mitigation technique. However, detecting and isolating the DDoS attacks from FEs when happening simultaneously …