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</v:shape><![endif]--><![if !vml]><img width="148" height="150" style="width:1.5416in;height:1.5625in" src="cid:image002.jpg@01DA63E2.10789E70" align="left" hspace="12" v:shapes="Picture_x0020_2"><![endif]><span style="font-family:"Calibri Light",sans-serif;color:#0D0D0D;background:white">Advancing
Trusted and Resilient Computing: ML-Based Cyberattacks Detection, Hardware Vulnerability Assessment, and PUF Authentication for IoT Security<o:p></o:p></span></p>
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<b><span style="font-family:"Calibri Light",sans-serif">Dr. Ahmed Oun<o:p></o:p></span></b></p>
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<span style="font-family:"Calibri Light",sans-serif;color:#212121">Assistant Professor</span><b><span style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></b></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:27.0pt;margin-bottom:0in;margin-left:9.0pt;margin-bottom:.0001pt">
<span style="font-family:"Calibri Light",sans-serif">Ohio Northern University<o:p></o:p></span></p>
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<span style="font-family:"Calibri Light",sans-serif"><o:p> </o:p></span></p>
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<b><span style="font-family:"Calibri Light",sans-serif">Abstract</span></b><span style="font-family:"Calibri Light",sans-serif">: Amid the escalating global threat of severe cyberattacks, integrating Machine Learning (ML) into cybersecurity has become a critical
research priority. Concurrently, the increasing trend of outsourcing the fabrication process of Integrated Circuits (ICs) to foreign foundries and hardware security threats have significantly increased. Of particular concern is the infiltration of the IC supply
chain with compromised and counterfeit chips by untrusted and dubious foundries. In recent years, programmable devices such as field-programmable gate arrays (FPGAs) have rapidly expanded. During this presentation, attention will be given to ongoing research
focused on the development of cyberattack detection of robust real-time intrusion detection systems. The primary objective is to create efficient machine learning (ML) models for intrusion detection while addressing computational challenges in multi-modeled
systems. This seminar will also highlight research on assessing the vulnerability of diverse silicon-based Physical Unclonable Functions (PUFs) types to Artificial Neural Networks (ANNs) through swarm intelligence-based modeling attacks. Additionally, the
presentation introduces the design of a novel XOR-ROPUF, engineered to counteract various machine learning modeling attacks, thereby enhancing the overall security of PUFs. Finally, an authentication scheme is proposed for the security of IoT systems using
a lightweight PUF. The proposed management scheme carries out the authentication between the verification authority, the authentication server, and the IoT devices to ensure data congeniality and integrity.<o:p></o:p></span></p>
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<span style="font-family:"Calibri Light",sans-serif"><o:p> </o:p></span></p>
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<b><span style="font-family:"Calibri Light",sans-serif">Bio</span></b><span style="font-family:"Calibri Light",sans-serif">: Dr. AHMED OUN received the M.S. degree in electrical engineering from the University of Bridgeport, Bridgeport, CT, USA, in December
2012, and the Ph.D. degree from the Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, USA, in May 2022. He is currently an Assistant Professor at the Department of Electrical and Computer Engineering and Computer Science,
Ohio Northern University. Prior to his academic studies, Ahmed served as a Project Manager with General Electric International Inc. His research interests include cybersecurity, hardware-assisted security for trusted embedded systems, VLSI, computing systems,
the secure Internet of Things (IoT) applications, and secure data communication using machine learning. His work also focused on adopting AI and developing new approaches for securing and protecting advanced computing systems against cyber-attacks, design
thefts, and hardware Trojans.<o:p></o:p></span></p>
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