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Articles 61 - 66 of 66
Full-Text Articles in Cybersecurity
Heuristic Machine Learning Approaches For Identifying Phishing Threats Across Web And Email Platforms, Ramprasath Jayaprakash, Krishnaraj Natarajan, J. Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik
Heuristic Machine Learning Approaches For Identifying Phishing Threats Across Web And Email Platforms, Ramprasath Jayaprakash, Krishnaraj Natarajan, J. Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik
Data Science Faculty Publications
Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. …
Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles, Obinna C. Agbo
Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles, Obinna C. Agbo
Graduate Theses, Dissertations, and Problem Reports (ETD)
The Controller Area Network (CAN) bus is a crucial communication backbone in modern vehicles, connecting various Electronic Control Units (ECUs). However, inherent design weaknesses such as the lack of encryption and authentication make CAN networks vulnerable to cyber-attacks, including spoofing, Denial of Service (DoS), and fuzzing attacks. This thesis thoroughly evaluates these vulnerabilities and the limitations of existing security frameworks like Message Authentication Codes (MACs) and encryption, advocating for the adoption of Intrusion Detection Systems (IDS) as a more practical solution for CAN bus security. The proposed IDS leverages advanced machine learning techniques to accurately detect intrusions, even under complex …
Passperfect: A Secure One Time Password Authentication System, Charlene H. Crawshaw
Passperfect: A Secure One Time Password Authentication System, Charlene H. Crawshaw
UNF Graduate Theses and Dissertations
Most organizations have established strong password policies and standards to ensure the confidentiality, integrity, and availability of their data, applications, and critical systems. Even with rigorous implementations and layered approaches such as utilizing multifactor authentications, there are still flaws and vulnerabilities as they have a high dependency on the user adhering to them. In conjunction with these policies, a strong security awareness program should be implemented to educate the end user about strong password hygiene. In this work, we design and implement a secure one-time password (OTP) system, “PassPerfect”, to provide a method of enforcing a strong set of password …
Towards Trust And Reputation As A Service In Society 5.0, Stephan Olariu, Ravi Mukkamala, Meshari Aljohani
Towards Trust And Reputation As A Service In Society 5.0, Stephan Olariu, Ravi Mukkamala, Meshari Aljohani
Computer Science Faculty Publications
Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government which seeks to create a sustainable human-centric society by putting to work recent advances in technology. One of the key challenges in implementing Society 5.0 is providing trusted and secure services for everyone to use. Motivated by this challenge, this paper makes three contributions that we summarize as follows: Our first main contribution is to propose a novel blockchain and smart contract-based trust and reputation service design to reduce the uncertainty associated with buyer feedback in marketplaces that we expect to see in Society 5.0. Our …
Qctaas (Quality Cloud Teaching As A Service): An Immersive Framework For Teaching Cloud Computing For Cybersecurity Majors, Mahmoud K. Quweider, Liyu Zhang, Alexis Aaron De La Cruz
Qctaas (Quality Cloud Teaching As A Service): An Immersive Framework For Teaching Cloud Computing For Cybersecurity Majors, Mahmoud K. Quweider, Liyu Zhang, Alexis Aaron De La Cruz
Informatics and Engineering Systems Faculty Publications
Abstract Cloud Computing and Cybersecurity are at the heart of our recently created new bachelor-level degree in cybersecurity that addresses the national need for cybersecurity specialists. Cloud Computing involves many service models, including IaaS, PaaS, and SaaS, and many deployment models including Public, Private, and Hybrid clouds. Within the services and deployment models lies many concepts and practical implementations that a Cyber-analyst needs to master. We have adopted an immersive approach to teaching Cloud Computing services that introduces standard concepts based on clearly defined objectives from national certification authorities such as CompTIA Cloud+, covering all major services offered by the …
An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen
An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …