ABSTRACT: Software is increasingly playing a key role in all infrastructure and application domains we may think of. Unfortunately, as we all know, software systems are still often insecure, despite the fact the “problem of software security” had been known to the industry and research communities for decades.  In this talk, I'll first present my view on the current state of software security and provide examples from mobile applications, open-source software, and emerging domains.  I'll then discuss the role of AI in software security and other factors that today complicate the problem of software security - a notable factor being the software supply chain. We then discuss "what it takes" to convince all parties involved in the software ecosystem to address the problem of software insecurity and outline research directions.

BIO:Elisa Bertino is Samuel Conte professor of Computer Science at Purdue University. She serves as Director of the Purdue Cyberspace Security Lab (Cyber2Slab). Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory in San Jose (now Almaden), at Rutgers University, at Telcordia Technologies. She has also held visiting professor positions at the Singapore National University and the Singapore Management University.  Her recent research focuses on security and privacy of cellular networks and IoT systems, and on edge analytics for cybersecurity.  Elisa Bertino is a Fellow member of  IEEE, ACM, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award for “For outstanding contributions to database systems and database security and advanced data management systems”, the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems”, the 2019-2020 ACM Athena Lecturer Award, and the 2021 IEEE 2021 Innovation in Societal Infrastructure Award. She received a Honorary Doctorate from Aalborg University in 2021 and a Research Doctorate in Computer Science from the University of Salerno in 2023.

ABSTRACT:As the variety of products and services continues to increase, recommender systems play a critical role in assisting customers by presenting products or services that are likely to be of interest to them. In the era of big data, is an abundance of data available from various sources, encompassing different modalities. In addition to user rating information on products, other relevant data sources can include social networks, knowledge bases, product descriptions and reviews, as well as contextual and temporal information. Furthermore, the rapid development of deep learning, especially in graph neural networks (GNNs) and Large Language Models (LLMs), has significantly advanced the machine learning capabilities, offering new opportunities to improve recommender systems. In this talk, our focus is on utilizing GNNs and LLMs to develop large multi-modal models through broad learning to fuse information from multiple sources of diverse modalities and perform synergistic deep recommendation tasks across these fused sources in a unified manner. We examine the various heterogeneous information sources and explore ways to enhance the effectiveness of recommendation systems by leveraging large multimodal models to harness the power of deep and broad learning.

BIO:Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from ICDM in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,600 referred conference and journal papers cited more than 182,500 times with an H-index of 191. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM TKDD (2011-2017) and IEEE TKDE (2001-2004).

ABSTRACT:This talk will first illustrate how to use AI techniques to enhance cyber security of various systems. There are several ways to apply AI to cyber security areas. This talk will use prediction-based AI technics to enhance the total security of the V2X (Vehicle-to-Everything) communication system. The talk takes serious considerations of latency while implementation the data encryption for V2X communication systems. Furthermore, the talk will discuss deep reinforcement learning to protect the security of V2X system without scarifying safety of the vehicles. Examples and experimental results will be given to show the detailed techniques on applying AI techniques to enhance cyber security of vehicles, with the potential of implementing them to various cyber-physical systems.

BIO:Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received a Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, He is a full professor and director of AI enhanced Cyber Security Lab of Augusta University, USA. He is an ACM Distinguished Member. He is also the Highly Cited Researcher in 2021 from Web of Science and IEEE Distinguished Visitor in 2021-2023. He is the Chair of IEEE Smart Computing Technical Committee.  Till now his Google scholar citation is 22800+ and H-index 102. He is ranked among the top 1000 computer scientists in the world. Meikang has published 20+ books, 600+ peer-reviewed journal and conference papers, including ICML, IJCAI, ACM CCS and 100+ IEEE/ACM Transactions papers. His research interests include Cyber Security, Big Data Analysis, Cloud Computing, Smarting Computing, Intelligent Data, etc. His paper on Tele-health system has won IEEE System Journal 2018 Best Paper Award. He also won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. He has won another 10+ Conference Best Paper Awards in recent years. He is/was an Associate Editor of 10+ international journals, including IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Big Data, and IEEE Transactions on SMC. He is the General Chair of the top conference: IEEE international conference on Data Mining (ICDM) 2023.

ABSTRACT:Cybersecurity has emerged as one of the foremost priorities on the global research and development agenda today. The urgent need for new and innovative cybersecurity technologies capable of effectively addressing this pressing danger cannot be overstated. Software security is paramount to maintaining the integrity of modern software applications. Given the broad spectrum of real-world applications, different security challenges are evaluated based on the specific use case.
In this presentation, we will dissect a variety of security issues that have arisen in diverse applications, examining both the associated challenges and effective strategies in software security. We will delve into the technique of fuzzing, an efficient and effective automated process vital for software testing. Additionally, we will explore strategies for detecting security vulnerabilities in software. We will also scrutinize security considerations in binary code applications, including those in IoT devices and Windows low-level components.

BIO:Professor Yang Xiang received his PhD in Computer Science from Deakin University, Australia. He is currently a full professor and the Dean of Digital Research, Swinburne University of Technology, Australia. In the past 20 years, he has been working in the broad area of cyber security, which covers software, network, system, and application security. He has published more than 300 research papers in many international conferences and journals in Cybersecurity, such as ACM CCS, IEEE S&P, Usenix Security, NDSS, IEEE TDSC, and IEEE TIFS. He is the Editor-in-Chief of the SpringerBriefs on Cyber Security Systems and Networks. He serves as the Associate Editor of the ACM Computing Surveys. He served as the Associate Editor of IEEE Transactions on Dependable and Secure Computing, IEEE Internet of Things Journal, IEEE Transactions on Computers, and IEEE Transactions on Parallel and Distributed Systems. He is the Coordinator, Asia for IEEE Computer Society Technical Committee on Distributed Processing (TCDP). He is a Fellow of the IEEE.

ABSTRACT:The primitives of Verifiable Database (VDB) and Verifiable Data Streaming (VDS) enable a resource-limited client to outsource huge data to an untrusted server while supporting public integrity verification and efficient updates. In this talk, we give the state-of-the-art techniques of VDB/VDS.

BIO:Xiaofeng Chen is a full professor at Xidian Univeristy, China. His research interests include applied cryptography and cloud computing security. He has published more than 300 research papers in refereed international conferences and journals. His work has been cited more than 15 000 times at Google Scholar. He is in the editorial board of the IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Knowledge and Data Engineering, International Journal of Foundations of Computer Science etc. He has served as the program/general chair or program committee member in more than 30 international conferences. He has been the highly cited scholar of Clarivate for the past five years.

ABSTRACT:As a new paradigm for 6G communications, reconfigurable intelligent surface (RIS) has admirable properties that enable the dynamic control of electromagnetic waves, thereby attracting significant attention from both industry and academia. Simultaneously, user-centric network highlights the personalized allocation of network resources to meet each mobile individual's requirements, which sheds light on future mobile transmission and corresponding service delivery. Due to this characteristic, RIS is expected to play a crucial role in the evolution of user-centric 6G network systems. However, tailoring RIS into these systems to meet user requirements is still an open challenge. Therefore, this talk aims to provide a comprehensive vision of realizing user-centric 6G network with RIS, specifically focusing on how to provide customizable and sustainable communication for a user-centric 6G. Regarding the design and optimization of RIS, several technical perspectives are considered, e.g., single-BS, multi-BS, and user-diversity. Further, a technical study is detailed in the user-centric RIS design and optimization for a virtual reality scenario. Finally, future research challenges and potential solutions are discussed in RIS-enabled 6G communications.

BIO:Mianxiong Dong received B.S., M.S. and Ph.D. in Computer Science and Engineering from The University of Aizu, Japan. He is the Vice President and Professor of Muroran Institute of Technology, Japan. His research interests include Wireless Networks, Cloud Computing, and Cyber-physical Systems. He is the recipient of The 12th IEEE ComSoc Asia-Pacific Young Researcher Award 2017, Funai Research Award 2018, NISTEP Researcher 2018 (one of only 11 people in Japan) in recognition of significant contributions in science and technology, The Young Scientists’ Award from MEXT in 2021, SUEMATSU-Yasuharu Award from IEICE in 2021, IEEE TCSC Middle Career Award in 2021. He is Clarivate Analytics 2019, 2021, 2022, 2023 Highly Cited Researcher (Web of Science) and Foreign Fellow of EAJ.