Machine Learning Security
July 12th, 2023 (CDT)
ITM Department, Illinois Institute of Technology, USA
Dr. Omar's Academic career has consistently focused on applied, industry-relevant cyber security, Data Analytics, machine learning, application of AI to cyber security and digital forensics research and education that delivers real-world results. He brings a unique combination of industry experience as well as teaching experience gained from teaching across different cultures and parts of the world. He has an established self-supporting program in machine learning application to cyber security. He has established a respectable research record in AI and cyber security exemplified in the dozens of published papers and book chapters that have gained recognition among researchers and practitioners (more than 272 Google scholar citations thus far). He is actively involved in graduate as well as undergraduate machine learning education including curriculum development and assessment.
Dr. Omar has recently published two books with Springer on Machine Learning and Cyber Security and has also published research with IEEE conference on Sematic Computing. Additionally, Dr. Omar holds numerous industry certifications including Comptia Sec+, ISACA CDPSE, EC-Council Certified Ethical Hacker, and SANS Advanced Smartphone Forensics Analyst.
Dr. Omar has been very active and productive in both academia as well as the industry and he is currently serving as an associate professor of cyber security at Illinois Institute of Technology.
Machine learning has become an essential part of many modern technologies and systems. From chatbots to autonomous vehicles, machine learning has enabled computers to learn from data and improve their performance over time. However, this technology is not without its risks. As machine learning systems are integrated into more critical areas of our lives, the security of these systems becomes paramount.
Machine learning security is a rapidly growing field that focuses on developing strategies and techniques to protect machine learning systems from attacks. These attacks can come in many forms, from adversarial attacks designed to manipulate the behavior of the system, to data poisoning attacks that aim to compromise the integrity of the training data.
The security of machine learning systems is particularly challenging because of the complexity and unpredictability of these systems. Machine learning algorithms are designed to learn and adapt to new data, which makes it difficult to identify and defend against attacks that are specifically crafted to evade existing defenses. Additionally, machine learning systems can be vulnerable to attacks that exploit vulnerabilities in the underlying hardware or software, making it essential to consider the security of the entire system, from the algorithms to the infrastructure that supports them.
To address these challenges, the field of machine learning security has developed a range of techniques and strategies. These include techniques for detecting and mitigating adversarial attacks, methods for securing the training data and models, and strategies for ensuring the overall security of the system.
As the use of machine learning continues to expand into critical areas, such as healthcare, finance, and transportation, the need for robust machine learning security will only increase. This workshop on machine learning security will bring together researchers, practitioners, and policymakers to discuss the latest developments in this rapidly evolving field and to explore strategies for protecting machine learning systems from attacks.
The primary goal of this workshop on machine learning security is to bring together experts and stakeholders from academia, industry, and government to discuss the latest research, technologies, and best practices for securing machine learning systems. The workshop will provide a platform for researchers, practitioners, and policymakers to exchange ideas and collaborate on addressing the challenges associated with machine learning security.
One of the key objectives of the workshop is to promote awareness and understanding of the risks associated with machine learning systems and the need for robust security measures. By bringing together experts from diverse backgrounds, the workshop will facilitate discussions and debates on the most pressing security challenges facing the field of machine learning and identify opportunities for collaboration and innovation.
Another goal of the workshop is to showcase the latest research and technologies in machine learning security. The workshop will provide a forum for researchers and practitioners to present their work, share their experiences, and receive feedback from their peers. This will enable attendees to learn about the latest developments in the field and identify opportunities for further research and development.
Finally, the workshop aims to facilitate collaboration between different stakeholders to develop practical solutions to the security challenges facing machine learning systems. Through panel discussions, breakout sessions, and networking opportunities, attendees will have the opportunity to connect with others who share their interests and expertise and work together to develop strategies for securing machine learning systems in practice.
Scope and Information for Participants:
1. Adversarial attacks: This includes techniques for detecting and mitigating adversarial attacks, which are designed to manipulate the behavior of machine learning systems.
2. Data poisoning attacks: This includes strategies for protecting the integrity of training data and preventing attacks that aim to compromise the accuracy of machine learning models.
3. Infrastructure security: This includes techniques for securing the underlying hardware and software that support machine learning systems, such as cloud computing platforms and GPUs.
4. Privacy and confidentiality: This includes methods for protecting the privacy of sensitive data used in machine learning, such as medical records or financial data.
5. Ethical considerations: This includes discussions around the ethical implications of machine learning security and the need to consider factors such as fairness, accountability, and transparency in the development and deployment of machine learning systems.
The workshop on machine learning security will be a successful gathering of experts and stakeholders from academia, industry, and government to discuss the latest research, technologies, and best practices for securing machine learning systems. The workshop provides a platform for attendees to exchange ideas and collaborate on addressing the challenges associated with machine learning security, with a focus on promoting awareness, showcasing the latest research, and facilitating collaboration between different stakeholders.
The workshop features presentations and panel discussions from leading experts in the field of machine learning security, covering a range of topics such as adversarial attacks, data poisoning attacks, infrastructure security, privacy and confidentiality, and ethical considerations. Attendees had the opportunity to learn about the latest developments in the field, discuss best practices, and identify opportunities for further research and development.
One of the highlights of the workshop will be the breakout sessions, where attendees will collaborate in smaller groups to discuss specific topics related to machine learning security. These sessions provide an opportunity for attendees to network with their peers and work together to develop practical solutions to the security challenges facing machine learning systems.
Illinois Institute of Technology, 10 W 35th St, Chicago, IL 60616
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