讲座题目:Information Hiding Techniques Using Magic Matrix
时间:2019年10月26日 9:00-10:30

Steganography is the science of secret message delivery using cover media. A digital image is a flexible medium used to carry a secret message because the slight modification of a cover image is hard to distinguish by human eyes. In this talk, I will introduce some novel steganographic methods based on different magic matrices Among them, one method that uses a turtle shell most interesting one. magic matrix to guide cover pixels’modification in order to imply secret data is the newest and the Experimental results demonstrated that this method, in comparison with previous related works, outperforms in both visual quality of the stego image and embedding capacity. In addition, I will introduce some future research issues that derived from the steganographic method based on the magic matrix.

Professor Chang has worked on many different topics in information security, cryptography, multimedia image processing and published several hundreds of papers in international conferences and journals and over 30 books. He was cited over 24300 times and has an h-factor of 74 according to Google Scholar. Several well-known concepts and algorithms were adopted in textbooks. He also worked with the National Science Council, Ministry of Technology, Ministry of Education, Ministry of Transportation, Ministry of Economic Affairs and other Government agencies on more than 100 projects and holds 17 patents, including one in US and two in China.
He served as Honorary Professor, Consulting Professor, Distinguished Professor, Guest Professor at over 50 academic institutions and received Distinguished Alumni Award’s from his Alma Master’s. He also served as Editor or Chair of several international journals and conferences and had given almost a thousand invited talks at institutions including Chinese Academy of Sciences, Academia Sinica, Tokyo University, Kyoto University, National University of Singapore, Nanyang Technological University, The University of Hong Kong, National Taiwan University and Peking University.
Professor Chang has mentored 56 PhD students and 177 master students, most of whom hold academic positions at major national or international universities. He has been the Editor-in-Chief of Information Education, a magazine that aims at providing educational materials for middle-school teachers in computer science. He is a leader in the field of information security of Taiwan. He founded the Chinese Cryptography and Information Security Association, accelerating information security the application and development and consulting on the government policy. He is also the recipient of several awards, including the Top Citation Award from Pattern Recognition Letters, Outstanding Scholar Award from Journal of Systems and Software, and Ten Outstanding Young Men Award of Taiwan. He was elected as a Fellow of IEEE and IET in 1998 for his contribution in the area of information security.

Cybersecurity in the 5G-connected IoT World: Recent Developments and Future Trends
地点: 教学楼2C101

Due to the modern technological advancements and innovations, computers are not just limited to the desktop, laptop or portable devices, but they are proliferating into various areas of our lives and blurring the lines between reality and fiction. This fact is becoming truth due to the emergence of Internet of Things (IoT), which unites physical and virtual worlds by extending computing and connectivity capabilities to everyday things e.g. cars, refrigerators, and home appliances, etc. IoT is ushering in a new era which is transforming the way we work, live, communicate and perform businesses. The dawn of 5G with the promise of ultra-high speed, massive bandwidth, and super-low latency is the building block of making this all happen with more IoT friendly ecosystem and its applications in automotive, healthcare, energy, aerospace & defense, industrial, and consumer electronics products, etc. However, this unprecedented dependence and increased connectivity of billions of connected IoT devices could lead to unexpected cybersecurity risks and threats, which may have serious ramifications beyond our imagination. In this speech, we would dissect cybersecurity challenges and concerns in the 5G connected IoT-enabled world. Furthermore, we would explore some peculiar problems inherent in 5G and IoT ecosystem, which could exacerbate the risks of cybersecurity breaches, crimes, and attacks. Finally, we would discuss some of our research contributions as well as future trends in this domain.

Muhammad Khurram Khan is currently working as a Full Professor at the Center of Excellence in Information Assurance (CoEIA), King Saud University, Kingdom of Saudi Arabia. He is one of the founding members of CoEIA and has served as Manager R&D from March 2009 until March 2012. He, along with his team, developed and successfully managed Cybersecurity research program of CoEIA, which turned the center as one of the best centers of excellence in Saudi Arabia and in the region.
Prof. Khurram is the Editor-in-Chief of a well-reputed International journal ‘Telecommunication Systems’ published by Springer-Verlag for over 24 years with its recent impact factor of 1.542 (JCR 2017). He is the Founding Editor of ‘Bahria University Journal of Information & Communication Technology (BUJICT)’. Furthermore, he is the editor of several international journals, including, IEEE Communications Surveys & Tutorials, IEEE Communications Magazine, IEEE Access, IEEE Transactions on Consumer Electronics, Journal of Network & Computer Applications (Elsevier), IEEE Consumer Electronics Magazine, PLOS ONE, Electronic Commerce Research (Springer), IET Wireless Sensor Systems, Journal of Information Hiding and Multimedia Signal Processing (JIHMSP), and International Journal of Biometrics (Inderscience), etc. He has also played role of the guest editor of several international journals of IEEE, Springer, Wiley, Elsevier Science, and Hindawi, etc. Moreover, he is one of the organizing chairs of more than 5 dozen international conferences and member of technical committees of more than 10 dozen international conferences. In addition, he is an active reviewer of many international journals as well as national funding agencies of Switzerland, Italy, Saudi Arabia and Czech Republic.

讲座题目:BigData and Collective Intelligence
地点: 教学楼2C101

Nowadays the creation and accumulation of Big Data is an unavoidable process of wide range of scenarios. Smart environments and diverse sources of sensors, but also the content created by humans, increases the Big Data’s enormous size and specific characteristics. To making sense of data, analyze and use these data, different more and more efficient algorithms have been developing constantly. Still, the effectiveness of these algorithms depends on the very nature of Big Data: analogue, noisy, implicit, and ambiguous. On the other hand another popular research area is Collective Intelligence. It represents the capability of interconnected intelligences to collectively and more efficiently solve concrete problems than each of the single intelligences.In the presentation will be given and overview and achievements of existing research on Big Data and Collective Intelligence.
At the end the perspectives and challenges of the common directions of Big Data and Collective Intelligence will be discussed.

Mirjana Ivanovic holds the position of Full Professor at Faculty of Sciences, University of Novi Sad, Serbia. She is author or co-author of 14 textbooks, several monographs and more than 350 research papers, most of which are published in international journals and conferences. She is a member of the University Council for Informatics.
Her research interests include agent technologies, intelligent techniques (CBR, data and web mining) and their applications, effects and applications of various data mining and machine learning algorithms, programming languages and software tools, e-learning and web-based learning. She is/was a member of Program Committees of more than 250 international conferences, Program/General Chair of several international conferences, and leader of numerous international research projects.
Mirjana Ivanovic delivered several keynote speeches at international conferences, and visited numerous academic institutions all over the world as visiting researcher. Currently she is Editor-in-Chief of the Computer Science and Information Systems journal (Five-year impact factor (2016): 0.881 in the 2016 Journal Citation Reports® (Clarivate Analytics, 2017).). She is also Associate Editor of KES Journal – International Journal of Knowledge-Based and Intelligent Engineering Systems (http://www.kesinternational.org/journal/). (e-mail: mira@dmi.uns.ac.rs).

An Intrusion Detection Approach Based on Improved Deep Belief Network
地点: 教学楼2C101

With the advances and development of network technology, network attacks and intrusion methods have become increasingly complex and diverse. At present, these existing intrusion detection technologies have overfitting, low classification accuracy and high false positive rate (FPR). In this paper, an intrusion detection approach based on improved Deep Belief Network (DBN) is proposed, where the dataset is processed by Probabilistic Mass Function (PMF) encoding and Min-Max normalization method to simplify the data preprocessing. And, a combined sparse penalty term based on Kullback-Leibler (KL) divergence and non-mean Gaussian distribution is introduced in the likelihood function of the unsupervised training phase of DBN. The sparse distribution of the dataset is obtained by sparse constraints, avoiding the problem of feature homogeneity and overfitting. By using the NSL-KDD and UNSW-NB15 datasets, the experimental results show that the proposed approach has significant improvement in classification accuracy, and FPR.

Kuan-Ching Li is a Professor of Computer Science and Engineering at University of California(Irvine), the United States. He received guest and distinguished chair professorships from universities in China and other countries, and a recipient of awards and funding support from several agencies and industrial companies. He has been actively involved in many conferences and workshops in program/general/steering conference chairman positions and has organized numerous conferences related to high-performance computing and computational science and engineering. Besides the publication of research papers, he is co-author/co-editor of several technical professional books published by CRC Press, Springer, McGraw-Hill and IGI Global. He is a Fellow of IET,a life member of TACC, a senior member of the IEEE and a member of the AAAS, and Editor-in-Chief of International Journal of Computational Science and Engineering (IJCSE), International Journal of Embedded Systems (IJES), and International Journal of High-Performance Computing and Networking (IJHPCN), published by Inderscience. His research interests include GPU/many-core computing, Big Data and Cloud.

Enabling High-performance Sampling for Big Data Processing
地点: 教学楼2C101

In this talk, we aim to demonstrate how to perform sampling in today’s big data processing platforms. We enable both efficient and accurate approximations on arbitrary sub-datasets of a large dataset. Due to the prohibitive storage overhead of caching offline samples for each sub-dataset, existing offline sample based systems provide high accuracy results for only a limited number of sub-datasets, such as the popular ones. On the other hand, current online sample based approximation systems, which generate samples at runtime, do not take into account the uneven storage distribution of a sub-dataset. They work well for uniform distribution of a sub-dataset while suffer low sampling efficiency and poor estimation accuracy on unevenly distributed sub-datasets.
To address the problem, we develop a distribution aware method called Sapprox. Our idea is to collect the occurrences of a sub-dataset at each logical partition of a dataset (storage distribution) in the distributed system, and make good use of such information to facilitate online sampling. We have implemented Sapprox into Hadoop ecosystem as an example system and open sourced it on GitHub. Our comprehensive experimental results show that Sapprox can achieve a speedup by up to a factor of 20 over the precise execution.

Dr. Jun Wang is a Full Professor of Computer Engineering; and Director of the Computer Architecture and Storage Systems (CASS) Laboratory at the University of Central Florida, Orlando, FL, USA. He has authored over 120 publications in premier journals such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and leading HPC and systems conferences such as VLDB, HPDC, EuroSys, IPDPS, ICS, Middleware, FAST. He has conducted extensive research in the areas of Computer Systems and High Performance Computing. His specific research interests include massive storage and file System in local, distributed and parallel systems environment. His group has secured multi-million dollars federal research fundings in last five years. At present, his group is investigating three US National Science Foundation projects, one DARPA and one NASA project. He has graduated 13 Ph.D. students who upon their graduations were employed by major US IT corporations (e.g., Google, Microsoft, etc). In 2019, he won IEEE Transactions on Cloud Computing Editorial Excellence and Eminence (EEE) award. He has been serving on the editorial board for the IEEE transactions on parallel and distributed systems, and IEEE transactions on cloud computing. He is a general executive chair for IEEE DASC/DataCom/PIcom/CyberSciTech 2017, and has co-chaired technical programs in numerous computer systems conferences including the 2018 IEEE international conference on High Performance Computing and Communications (HPCC18), the 10th IEEE International Conference on Networking, Architecture, and Storage (NAS 2015), and 1st International Workshop on Storage and I/O Virtualization, Performance, Energy, Evaluation and Dependability (SPEED 2008) held together with HPCA.