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Malbot-Drl: Malware Botnet Detection Using Deep Reinforcement Learning In Iot Networks, Mohammad Al-Fawa'reh, Jumana Abu-Khalaf, Patryk Szewczyk, James J. Kang
Malbot-Drl: Malware Botnet Detection Using Deep Reinforcement Learning In Iot Networks, Mohammad Al-Fawa'reh, Jumana Abu-Khalaf, Patryk Szewczyk, James J. Kang
Research outputs 2022 to 2026
In the dynamic landscape of cyber threats, multi-stage malware botnets have surfaced as significant threats of concern. These sophisticated threats can exploit Internet of Things (IoT) devices to undertake an array of cyberattacks, ranging from basic infections to complex operations such as phishing, cryptojacking, and distributed denial of service (DDoS) attacks. Existing machine learning solutions are often constrained by their limited generalizability across various datasets and their inability to adapt to the mutable patterns of malware attacks in real world environments, a challenge known as model drift. This limitation highlights the pressing need for adaptive Intrusion Detection Systems (IDS), capable …
Intrusion Detection Based On Bidirectional Long Short-Term Memory With Attention Mechanism, Yongjie Yang, Shanshan Tu, Raja Hashim Ali, Hisham Alasmary, Muhammad Waqas, Muhammad Nouman Amjad
Intrusion Detection Based On Bidirectional Long Short-Term Memory With Attention Mechanism, Yongjie Yang, Shanshan Tu, Raja Hashim Ali, Hisham Alasmary, Muhammad Waqas, Muhammad Nouman Amjad
Research outputs 2022 to 2026
With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. …
Camdec: Advancing Axis P1435-Le Video Camera Security Using Honeypot-Based Deception, Leslie F. Sikos, Craig Valli, Alexander E. Grojek, David J. Holmes, Samuel G. Wakeling, Warren Z. Cabral, Nickson M. Karie
Camdec: Advancing Axis P1435-Le Video Camera Security Using Honeypot-Based Deception, Leslie F. Sikos, Craig Valli, Alexander E. Grojek, David J. Holmes, Samuel G. Wakeling, Warren Z. Cabral, Nickson M. Karie
Research outputs 2022 to 2026
The explosion of online video streaming in recent years resulted in advanced services both in terms of efficiency and convenience. However, Internet-connected video cameras are prone to exploitation, leading to information security issues and data privacy concerns. The proliferation of video-capable Internet of Things devices and cloud-managed surveillance systems further extend these security issues and concerns. In this paper, a novel approach is proposed for video camera deception via honeypots, offering increased security measures compared to what is available on conventional Internet-enabled video cameras.