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.
The scope of this workshop is centered around the practical application of Large Language Models (LLMs) in detecting software vulnerabilities. Participants will explore the theoretical foundations of LLMs, their evolution from natural language processing to cybersecurity applications, and the latest techniques in training these models for vulnerability detection. The workshop will include hands-on sessions where attendees will work with real-world codebases, applying LLMs to identify potential security threats.Specific themes to be addressed include:
The area of research focuses on the intersection of artificial intelligence (AI) and cybersecurity, specifically leveraging Large Language Models (LLMs) for software vulnerability detection. Traditional methods of identifying software vulnerabilities often rely on rule-based systems or signature detection, which can be limited in identifying novel or sophisticated attacks, such as zero-day vulnerabilities or advanced malware. LLMs, which have been primarily utilized in natural language processing (NLP), have demonstrated remarkable capabilities in understanding and generating human-like text. Researchers are now exploring how these models can be adapted to analyze source code and software behavior, detecting subtle patterns and anomalies that may indicate security threats. By training LLMs on vast datasets of code and vulnerability examples, these models can potentially predict and identify vulnerabilities that traditional methods might miss, providing a new layer of security in software development and deployment. This research area is rapidly evolving and holds promise for significantly enhancing the effectiveness and efficiency of cybersecurity measures.
The primary goal of this research workshop is to address the growing challenge of detecting software vulnerabilities, particularly zero-day exploits and advanced malware, which traditional security tools often fail to identify. As software systems become more complex and interconnected, the potential attack surface expands, making it increasingly difficult to secure applications using conventional methods. Large Language Models (LLMs) offer a novel approach by utilizing their ability to understand and generate complex patterns, not just in natural language but also in code.
Recent advances in LLMs, such as GPT-4 and BERT, have demonstrated their capacity to process and analyze large datasets, learning intricate patterns that might escape traditional static analysis tools. By training these models on extensive codebases, along with known vulnerabilities, we can potentially create systems capable of predicting and identifying previously unknown security flaws. This approach could lead to the development of more adaptive and intelligent security solutions, capable of evolving alongside the threats they are designed to mitigate. The rationale behind this research is to leverage these advances in AI to create more proactive and effective methods for securing software, ultimately reducing the risk of cyberattacks and enhancing overall digital security.
Prospective authors are kindly invited to submit full papers that include title, abstract, introduction, tables, figures, conclusion and references. It is unnecessary to submit an abstract in advance. Please submit your papers in English.
Each paper should be no less than 4 pages. One regular registration can cover a paper of 6 pages, and additional pages will be charged. Please format your paper well according to the conference template before submission. Paper Template Download
Please prepare your paper in both .doc/.docx and .pdf format and submit your full paper by email with both formats attached directly to ws_chicago@confciap.org
Process | Date & Time |
---|---|
Paper Submission | November 13th, 2024 |
Review Process | 2 weeks |
Revise & Acceptance | 2 weeks |
Registration & Payment | 2 weeks |
Fees (VAT Included) | Amount |
---|---|
Registration and Publishing Fee (6 pages included) | $450 |
Additional Page (per extra page) | $40 |
Accepted papers will be published in Theoretical and Natural Science (TNS) (Print ISSN 2753-8818), and will be submitted to Conference Proceedings Citation Index (CPCI), Crossref, CNKI, Portico, Engineering Village (Inspec), Google Scholar, and other databases for indexing. The situation may be affected by factors among databases like processing time, workflow, policy, etc.
Title: Theoretical and Natural Science (TNS)
Press: EWA Publishing, United Kingdom
ISSN: 2753-8818, 2753-8826 (electronic)
* The papers will be exported to production and publication on a regular basis. Early-registered papers are expected to be published online earlier.
Harnessing the Power of Large Language Models to Detect Software Vulnerabilites
November 20th, 2024 (UTC -6)
The workshop titled "Harnessing the Power of LLMs to Detect Software Vulnerabilities" will explore the cutting-edge intersection of artificial intelligence and cybersecurity. This workshop is designed to provide participants with an in-depth understanding of how Large Language Models (LLMs), such as GPT and BERT, can be leveraged to enhance software vulnerability detection. Attendees will learn about the latest techniques in training LLMs to identify zero-day vulnerabilities, malware, and other security threats within software systems. The workshop will cover both theoretical frameworks and practical applications, demonstrating how LLMs can be trained to recognize patterns and anomalies in code that indicate potential security risks. Participants will engage in hands-on sessions where they will work with LLMs to analyze real-world software scenarios, gaining insights into the challenges and opportunities in this emerging field. By the end of the workshop, attendees will have a comprehensive understanding of how to harness the power of LLMs to strengthen software security, making them well-equipped to implement these advanced techniques in their own organizations. This event is ideal for cybersecurity professionals, AI researchers, and software developers interested in cutting-edge security solutions.
Illinois Institute of Technology, 10 W 35th St, Chicago, IL 60616
The workshop provides an opportunity for poster exhibition. Authors who are interested in presenting a poster, please submit your materials (prefer in .pdf) to ws_chicago@confciap.org before November 13th, 2024.
Detailed information on your presentation date and time will be confirmed closer to the workshop. If you have any questions about your presentation or the workshop, please email ws_chicago@confciap.org.
(You can refer to the instructions for more detailed information. Additional presentation guidelines may be announced by workshop committees.)
If you want to attend the workshop on-site, please email ws_chicago@confciap.org. The workshop seats are limited. Both contributors and non-contributors who wish to participate in the workshop in person need to apply to the workshop organizers.
In order to ensure the information is correct and up to date, there may be changes which we are not aware of. And different countries have different rules for the visa application. It is always a good idea to check the latest regulations in your country. This page just gives some general information of the visa application.
The B-1/B-2 visitor visa is for people traveling to the United States temporarily for business (B-1) or for pleasure or medical treatment (B-2). Generally, the B-1 visa is for travelers consulting with business associates; attending scientific, educational, professional, or business conventions/conferences; settling an estate; or negotiating contracts. The B-2 visa is for travel that is recreational in nature, including tourism; visits with friends or relatives; medical treatment; and activities of a fraternal, social, or service nature. Often, the B-1 and B-2 visas are combined and issued as one visa: the B-1/B-2.
If you apply for a business/tourist visa, you must pay your $160 application fee and submit the following:
In addition to these items, you must present an interview appointment letter confirming that you booked an appointment through this service. You may also bring whatever supporting documents you believe support the information provided to the consular officer.
Should your application be denied, the organizing committee cannot change the decision of visa officer, nor will CONF-CIAP engage in discussion or correspondence with the visa application center on behalf of the applicant. The registration fee CANNOT be refunded when the VISA application of individual being denied.