The 6th IEEE International Conference on Big Data Security on Cloud

(BigDataSecurity 2020)

The 6th IEEE International Conference on High Performance and Smart Computing

(IEEE HPSC 2020)

The 5th IEEE International Conference on Intelligent Data and Security

(IEEE IDS 2020)

May 25-27, 2020 Baltimore, USA

Conference Program and Information Booklet

Organized By

IEEE BIGDATASECURITY/HPSC/IDS 2020 Committee Sponsored By

IEEE IEEE Computer Society,

IEEE TCSC IEEE STC Smart Computing

Columbia University North America Chinese Talents Association

Longxiang High Tech

Welcome to IEEE Computer Society Smart Computing Special Technical Community (SCSTC) IEEE SCSTC is built up for changing people’s future work and life; attracting intelligent computing talents in smart computing field; producing high quality research work and services in human-centric technologies to change the world; leading the research of smart computing by solving challenging problems; and expanding the smart computing community in a self-sustainable financial way. Two main layers are involved in the concept of smart: one is the traditional optimization; the other one is the intelligent living.

Vision: IEEE Computer Society Smart Computing STC is to enable smart life with smart data, smart cloud, and smart security and become a community leader in these technical fields.

We will create a smart computing society for changing people’s future work and life; attract intelligent computing talents in smart computing field; produce high quality research work and services in human-centric technologies to change the world; lead the research of smart computing by solving challenging problems; and expand the smart computing community in a self-sustainable financial way. Two main layers are involved in the concept of smart: one is the traditional optimization; the other one is the intelligent living.

Mission: IEEE Computer Society Smart Computing STC is to utilize smarting computing technologies to increase humans’ life by integrating smart data, smart cloud, and smart security in both optimizations and intelligences. We will build up the largest professional and academic community in smart computing and aim to enhance humans’ life by utilizing smart computing technologies. This expected community will be providing an integrative research platform for global researchers who are interested in smart computing that covers both optimizations and intelligent living. The target area is a convergence of three novel dimensions at the collaborative application layer, namely smart data, smart cloud, and smart security. This is a social network-based community that is planned to be a long-term self-sustaining organization.

Purpose: The main purpose of this proposed STC is to serve the smart computing research community and advance the research by covering three dimensions, including smart data, smart cloud, and smart security. Current existing STCs cannot satisfy the demands of research interests in convergences of multiple disciplines, which include data, cloud computing, and security. Most existing STCs only have isolative focus in one specific field. However, data, cloud computing, and security are becoming strongly tied techniques, which are hard to separately considered for many contemporary researches or future technical development. Therefore, building up a STC in Smart Computing has an urgent demand for both smart computing research and professional practices.

Scope: the scope of Smart Computing STC is a technical group within the Computer Society. Term Smart in “Smart Computing” mainly covers two aspects, including optimizations and intelligence, by which smart concept will be adopted for new networking-oriented technologies. We are looking for intelligent approaches gaining optimal performances by high-speed data mining and data analysis throughout all aspects in distributed computing and integrated systems. Both aspects are strongly relevant to the performance of the system at the application layer during the process of data transmissions within the distributed environment. This concentration emphasizes the optimizations and intelligences of networking performances and empowers the capabilities of the connected computing devices in distributed systems, which distinguishes from other societies or communities.

Activities: IEEE Computer Society Smart Computing STC organizes a bunch of research community-oriented activities. We aim to unionize scholars or students who have similar or relevant research interests in smart computing and grow the research community globally. Our memberships owners will have a great opportunity to build up an active social network and strengthen the knowledge scope throughout the following activities:

• Improve communications and interconnections between peers. • Explore the theory, applications, implementations, and research of smart computing. • Publish whitepapers, reports, technical manual, and handbooks on research, policies, standards,

products, services, and applications. • Organize conferences and workshops that are related to smart computing. • Release newsletters with updated news regularly. • Host academic publications focusing on smart computing. • Develop smart computing standards. • Standardize the mechanisms, operating principles, and industrial manual guidelines.

Official Permanent Site: https://stc.computer.org/smart-stc/

About IEEE SCSTC

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Tuesday, May. 26th, 2020

Room A Room B

8:45 – 9:00 Opening

9:00 – 10:10 Keynote 1

10:10 – 10:20 Break

10:20 – 11:10 Keynote 2

11:10 – 11:20 Break

11:20 – 12:20 BDS1 BDS2

12:20 – 13:30 Break

13:30 – 14:30 BDS 3 IDS 1

14:30 – 15:30 BDS 4 HPSC 1

15:30 – 15:50 Break

15:50 – 16:50 BDS 5 IDS 2

16:50 – 17:50 BDS 6 BDS 7

17:50 – 18:50 IDS 3

Registration: Online Registration System (http://www.cloud-conf.net/datasec/2020/registration.html) Presentation Online Rooms: Zoom (https://zoom.us/) Virtual Conference Link: https://us02web.zoom.us/j/5911036727?pwd=bTZ0ZTNuOGFreGgrdUNWREFrdlVNZz09 Important Notice: Due to the outbreak of COVID-19, this year the IEEE BigDataSecurity/HPSC/IDS will be a virtual conference online. For all participants, please do notice all the time mentioned in this booklet is based on the time zone of east USA which is Eastern Daylight Time (EDT), UTC -4.

IEEE BigDataSecurity/HPSC/IDS 2020 Program at a Glance

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HPCC/ICESS/CSS Keynote

May 26th, 2020, 9:00 AM, Eastern Daylight Time (EDT), UTC -4

Topic: From Deep Learning to Internal and

Explainable Learning applicable to XAI

Prof. Sun-Yuan Kung Princeton University, USA

Bio: S.Y. Kung, Life Fellow of IEEE, is a Professor at Department of Electrical Engineering in Princeton University. His research areas include machine learning, data mining, systematic design of (deep-learning) neural networks, statistical estimation, VLSI array processors, signal and multimedia information processing, and most recently compressive privacy. He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society. He was elected to Fellow in 1988 and served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a recipient of IEEE Signal Processing Society's Technical Achievement Award for the contributions on "parallel processing and neural network algorithms for signal processing" (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994); a recipient of IEEE Signal Processing Society's Best Paper Award for his publication on principal component neural networks (1996); and a recipient of the IEEE Third Millennium Medal (2000). Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. He served as the first Associate Editor in VLSI Area (1984) and the first Associate Editor in Neural Network (1991) for the IEEE Transactions on Signal Processing. He has authored and co-authored more than 500 technical publications and numerous textbooks including "VLSI Array Processors", Prentice-Hall (1988); "Digital Neural Networks", Prentice-Hall (1993) ; "Principal Component Neural Networks", John-Wiley (1996); "Biometric Authentication: A Machine Learning Approach", Prentice-Hall (2004); and "Kernel Methods and Machine Learning”, Cambridge University Press (2014).

Abstract: Deep Learning (NN/AI 2.0) depends solely on Back-propagation (BP), now classic learning paradigm whose supervision is exclusively accessed via the external interfacing nodes (i.e. input/output neurons). Hampered by BP's external learning paradigm, Deep Learning has been limited to the parameter training of the neural nets (NNs), while the task of optimizing the net structure is left to trial and error. It is important that the next generation of NN technology may fully address the issue of simultaneously training both parameter and structure of NNs. In addition, it should support Internal Neuron's Explainablility, championed by DARPA's Explainable AI (XAI) or AI3.0. For both purposes, we propose an internal learning paradigm to facilitate a notion of structural gradient critical for structural learning models. In order to effectively rank the trained neurons (i.e. the hidden nodes), we propose an Explainable Neural Networks (Xnet) comprising (1) internal teacher labels (ITL) and (2) internal optimization metrics (IOM). WE then develop a joint parameter/structure training paradigm for Deep Learning Networks by combining both external and internal learning. Xnet can simultaneously compress the net and raise the net’s accuracy. Pursuant to our simulation studies, it appears to outperform existing pruning/compression methods. Furthermore, Xnet opens up promising research fronts on (1) explainable learning models for XAI and (2) machine-to-machine mutual learning in the soon-coming 5G era.

IEEE BigDataSecurity/HPSC/IDS 2020 Keynotes

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May 26th, 2020, 10:00 AM, Eastern Daylight Time (EDT), UTC -4

Topic: Data to Knowledge: Modernizing Political Event Data

Prof. Latifur Khan University of Texas at Dallas, USA

Bio: Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. Dr. Khan obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh in November of 1993 with First class Honors. He was a recipient of Chancellor Awards from the President of Bangladesh. Dr. Khan is an ACM Distinguished Scientist and received IEEE Big Data Security Senior Research Award, in May 2019, and Fellow of SIRI (Society of Information Reuse and Integration) award in Aug, 2018. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics and IBM Faculty Award (research) 2016. Dr. Latifur Khan has published over 300 papers in premier journals such as VLDB, Journal of Web Semantics, IEEE TDKE, IEEE TDSC, IEEE TSMC, and AI Research and in prestigious conferences such as AAAI, IJCAI, CIKM, ICDE, ACM GIS, IEEE ICDM, IEEE BigData, ECML/PKDD, PAKDD, ACM Multimedia, ACM WWW, ICWC, ACM SACMAT, IEEE ICSC, IEEE Cloud, and INFOCOM. He has been invited to give keynotes and invited talks at a number of conferences hosted by IEEE and ACM. In addition, he has conducted tutorial sessions in prominent conferences such as SIGKDD 2017, 2016, IJCAI 2017, AAAI 2017, SDM 2017, PAKDD 2011 & 2012, DASFAA 2012, ACM WWW 2005, MIS2005, and DASFAA 2007. Currently, Dr. Khan’s research area focuses on big data management and analytics, data mining and its application over cyber security, complex data management including geo-spatial data and multimedia data. His research has been supported by grants from NSF, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM and HPE. More details can be found at: www.utdallas.edu/~lkhan/

Abstract: Political event data record interactions among social and political actors. Researchers use these data to understand relations among actors, predict outcomes of interest, and forecast trends. As automated technologies have become better able to extract events from text, event data projects and repositories have increased in number. The main goal of this tutorial is to integrate and expand our end-to-end cyberinfrastructure for robust creation, validation, access, and analysis of political event data. We focus on political and social events about conflict and cooperation between governments, individuals, non-governmental organizations, rebel groups, and others. Natural language processing tools along with ontologies/dictionaries will be utilized to code event data by annotating the kinds of political events. In the talk we will show how to scrape contemporaneous news reports in English and Spanish, and automatically encode relevant political events for data analysts. Multiple challenges will be presented and addressed them in the talk: (1) additional extensions of multilingual framework with additional languages and types of events; (2) smoother updates to political actor dictionaries; (3) robust data querying and linking mechanisms, and analytic tools for the broader research community; and (4) improved methods for focus location extraction across languages and resolutions. This is a collaborative work with political scientists, Dr. Patrick Brandt and Dr. Jennifer Holmes, funded by NSF.

IEEE BigDataSecurity/HPSC/IDS 2020 Keynotes

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Technical Program

The 6th IEEE nternational Conference on Big Data Security on Cloud (IEEE BigDataSecurity 2020)

BIGDATASECURITY 1: Big Data Security 26/05/2020, Online Conference Session Chair:

• Adithya Bandi, Karuna Joshi and Varish Mulwad. Affinity Propagation Initialisation Based Proximity Clustering For Labeling in Natural Language Based Big Data Systems

• Bhavani Thuraisingham. Multi-Generational Database Inference Controllers • Yundan Liang, Zhengdong Ren, Jiawei Liao, Peipei Jin, Yu Huang, Guangxian Lv

and Yiming Lu. Research on Maintenance Strategy of Distribution Network based on Monte Carlo Tree

• Dongxiao Jiang, Chenggang Li, Linxin Ma, Xiaoyu Ji, Yanjiao Chen and Li Bo. ABInfer: A Novel Field Boundaries inference Approach for Protocol Reverse Engineering

BIGDATASECURITY 2: Smart Applications and Security 26/05/2020, Online Conference Session Chair:

• Qian Wang, Jinan Sun, Chen Wang, Shikun Zhang, Sisi Xuanyuan and Bin Zheng. Access Control Vulnerabilities Detection for Web Application Components

• Sai Sree Laya Chukkapalli, Aritran Piplai, Sudip Mittal, Maanak Gupta and Anupam Joshi. A Smart-Farming Ontology for Attribute Based Access Control

• Sofia Dutta, Sai Sree Laya Chukkapalli, Madhura Sulgekar, Swathi Krithivasan, Prajit Das and Anupam Joshi. Context Sensitive Access Control in Smart Home Environments

• Weipeng Cao, Pengfei Yang, Zhong Ming, Shubin Cai and Jiyong Zhang. An Improved Fuzziness based Random Vector Functional Link Network for Liver Disease Detection

BIGDATASECURITY 3: Intrusion and Malware Detection 26/05/2020, Online Conference Session Chair:

• Aritran Piplai, Sai Sree Laya Chukkapalli and Anupam Joshi. NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion

• Aidong Xu, Lin Chen, Xiaoyun Kuang, Huahui Lv, Hang Yang, Yixin Jiang and Li Bo. A Hybrid Deep Learning Model for Malicious Behavior Detection

• Lin Chen, Aidong Xu, Xiaoyun Kuang, Huahui Lv, Hang Yang, Yiwei Yang and Li Bo. Detecting Advanced Attacks Based on Linux Logs

• Prasanthi Sreekumari. Malware Detection Techniques Based on Deep Learning

BIGDATASECURITY 4: Blockchain Systems 26/05/2020, Online Conference Session Chair:

• Wenxuan Pan and Meikang Qiu. Application of Blockchain in Asset-Backed Securitization

• Yihang Wei. Blockchain-Based Data Traceability Platform Architecture for Supply Chain Management

• Abhishek Mahindrakar and Karuna Joshi. Automating GDPR Compliance Using Policy Integrated Blockchain

Technical Program

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BIGDATASECURITY 5: Intelligent Security 26/05/2020, Online Conference Session Chair:

• Han Qiu and Meikang Qiu. Review on Image Processing Based Adversarial Example Defenses in Computer Vision

• Zhuoyi Wang, Yigong Wang, Pracheta Sahoo, Kevin Hamlen, Latifur Khan and Bo Dong. Adaptive Margin Based Deep Adversarial Metric Learning

• Yongzhi Wang, Chengli Xing, Jinan Sun, Shikun Zhang, Sisi Xuanyuan and Long Zhang. Solving the Dependency Conflict of Java Components: A Comparative Empirical Analysis

• Jennifer Sleeman, Tim Finin and Milton Halem. Temporal Understanding of Cybersecurity Threats

BIGDATASECURITY 6: Intelligent Algorithms 26/05/2020, Online Conference Session Chair:

• Xinxin Liu, Hua Huang and Hao Wu. Intelligent generation algorithm of ceramic decorative pattern

• Navneet Kaur, Ali Azari, Josephine Namayanja, Vasundhara Misal, Suraksha Shukla and Vandana Janeja. Imbalanced Learning in Massive Phishing Datasets

• Wenhui Hu, Long Zhang, Xueyang Liu, Yu Huang, Minghui Zhang and Liang Xing. Research on Automatic Generation and Analysis Technology of Network Attack Graph

• Aidong Xu, Lin Chen, Yixin Jiang, Huahui Lv, Hang Yang and Li Bo. Finding Gold in the Sand: Identifying Anomaly Indicators Though Huge Amount Security Logs

BIGDATASECURITY 7: Cloud Computing and Security 26/05/2020, Online Conference Session Chair:

• Dharitri Tripathy, Rudrarajsinh Gohil and Talal Halabi. Detecting SQL Injection Attacks in Cloud SaaS using Machine Learning

• Vrushang Patel, Seungho Choe and Talal Halabi. Predicting Future Malware Attacks on Cloud Systems using Machine Learning

• Jian Li, Hao Jiang, Wei Jiang and Jing Wu. SDN-based Stateful Firewall for Cloud • James Kok Konjaang and Lina Xu. Cost Optimised Heuristic Algorithm (COHA)

for Scientific Workflow Scheduling in IaaS Cloud Environment • Zahir Alsulaimawi. Gaussian Privacy Protector for Online Data Communication in

a Public World

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The 6th IEEE International Conference on High Performance and Smart Computing (IEEE HPSC 2020)

HPSC 1: 26/05/2020, Online Conference Session Chair:

• Yunqing Hong and Si Xu. A Navigation Satellite Selection Method Based on Optimized DPSO Algorithm

• Xueying Yang, Zhonghua Lu, Meikang Qiu and Yonghong Hu. A Parallel Integer Relative Robust Mean-RCVaR Model for Portfolio Optimization

• Disheng Pan, Xizi Zheng, Weijie Liu, Mengya Li, Meng Ma, Zhou Ying, Li Yang and Ping Wang. Multi-label Classification for Clinical Text with Feature-level Attention

• Sathish Kumar and Brown. Reflective Neural Network Based Learning Framework for Intelligent Physical Systems

• Deepti Gupta, Smriti Bhatt, Maanak Gupta, Olumide Kayode and Ali Saman Tosun. Access Control Model for Google Cloud IoT

The 5th IEEE International Conference on Intelligent Data and

Security (IEEE IDS 2020)

IDS 1: 26/05/2020, Online Conference Session Chair:

• Timilehin Sobola, Pavol Zavarsky and Sergey Butakov. Experimental Study of ModSecurity Web Application Firewalls

• Muhammad Ali Fauzi, Bian Yang and Edlira Martiri. PassGAN Based Honeywords System for Machine-Generated Passwords Database

• Ronald Doku and Danda Rawat. iFLBC: On the Edge Intelligence Using Federated Learning Blockchain Network

• Sushant Sharma and Pavol Zavarsky. Machine Learning Intrusion Detection System for Web-Based Attacks

IDS 2: 26/05/2020, Online Conference Session Chair:

• Victor Ajiri, Sergey Butakov and Pavol Zavarsky. Detection Efficiency of Static Analyzers against Obfuscated Android Malware

• Krishna Gondaliya and Sergey Butakov. SLA as a mechanism to manage risks related to chatbot services

• Osborn Nyasore, Pavol Zavarsky, Bobby Swar, Raphael Naiyeju and Shubham Dabra. Deep Packet Inspection in Industrial Automation Control System to Mitigate Attacks Exploiting Modbus/TCP Vulnerabilities.

• Sayeed Salam, Lamisah Khan, Patrick Brandt and Jennifer Holmes. Automatic Event Coding Framework for Spanish Political News Articles

IDS 3: 26/05/2020, Online Conference

• Xiongbo Huang, Ou Ruan and Hao Mao. An Efficient Private Set Intersection Protocol for the Cloud Computing Environments

• Andrii Kashliev. Storage and Querying of Large Provenance Graphs Using NoSQL DSE

• Julie Harvey and Sathish Kumar. A Survey of Intelligent Transportation Systems Security: Challenges and Solutions

Technical Program