Monday June 29
Workshop on Dependable and Secure Machine Learning
15:00-15:05 (CEST). Welcome to DSN-DSML 2020 Homa Alemzadeh, University of Virginia
15:05-15:45 (CEST). Session 1: Keynote Talk by Michael Lyu, Chinese University of Hong Kong
Although artificial intelligence has advanced the state-of-the-art in many domains, its interpretability, dependability, and security remain unsatisfactory, hindering the rapid deployment in many safety-critical scenarios. Among these characteristics, interpretability is at the core since the human trust builds upon the interpretability of model predictions and understanding of unexpected behaviors (e.g., error predictions, adversarial attacks). In this talk, I will introduce some of our recent investigations on model interpretability in both natural language processing and computer vision domains. Besides, I will illustrate our recent attempts on dependable and secure machine learning from the interpretability perspective. Finally, I will share some thoughts on the related research directions.
Michael R. Lyu is a Professor and the Chairman in the Computer Science & Engineering Department at the Chinese University of Hong Kong. He received a B.S. in Electrical Engineering from the National Taiwan University, an M.S. in Electrical and Computer Engineering from University of California, Santa Barbara, and a Ph.D. in Computer Science from University of California, Los Angeles. His research interests include software reliability engineering, dependable computing, machine learning, artificial intelligence, and distributed systems. He published a widely cited McGraw-Hill Handbook of Software Reliability Engineering, and a Wiley book on Software Fault Tolerance. He is a Fellow of the IEEE, a Fellow of ACM, a Fellow of AAAS, and an IEEE Reliability Society Engineer of the Year. He also appears in The AI 2000 Most Influential Scholars Annual List in 2020.
15:55-16:40 (CEST). Session 2: Attacks
16:50-17:35 (CEST). Session 3: Validation, Verification, and Defense
17:45-18:15 (CEST). Session 4: Keynote Talk by Rajarshi Gupta, Avast Security
Recent years have seen heavy utilization of AI in security, but the complexities of a massively scalable production-quality security pipeline is often hard to grasp. In this seminar, we will discuss state-of-the-art AI techniques used to deter daily attacks, by drawing from experience of protecting 435M users (across PCs, mobiles, IoTs) at Avast. We will also identify gaps that exist between academic research in AI-Security, and the daily challenges of real-world attacker-defender contests. Finally, we suggest ways to bridge those gaps, to make the academic research more viable and valuable in real deployments.
Rajarshi Gupta is the Head of AI at Avast Software, the largest consumer security companies in the world. He has a PhD in EECS from UC Berkeley and has built a unique expertise at the intersection of Artificial Intelligence, Cybersecurity and Networking. Prior to joining Avast, Rajarshi worked for many years at Qualcomm Research, where he created ‘Snapdragon Smart Protect’, the first ever product to achieve On-Device Machine Learning for Security. Rajarshi has authored over 200 issued U.S. Patents, and is featured on the wikipedia page for most prolific inventors in history.
18:15-18:30 (CEST). Discussion and Closing
Workshop on Data-Centric Dependability and Security
15:00-15:05 (CEST). Welcome DCDS 2020Ibéria Medeiros, University of Lisboa
15:05-15:45 (CEST). Session 1: Keynoteby Jilles Vreeken, CISPA, Saarland University, GermanySession chair: Michael Kamp, Monash University
Jilles Vreeken is the leader of the Independent Research Group on Exploratory Data Analysis at the Helmholtz Center for Information Security. In addition, he is a Senior Researcher in D5, the Databases and Information Systems group at the Max Planck Institute for Informatics, and a Professor in the Department of Computer Science of Saarland University. His research interests include data mining and machine learning, exploratory data analysis, causal inference, and pattern mining. He is particularly interested in developing well-founded theory and efficient methods for extracting informative models and characteristic patterns from large data, and putting these to good use. He has authored over 60 conference and journal papers, 3 book chapters, won the 2010 ACM SIGKDD Doctoral Dissertation Runner-Up Award, and two best (student) paper awards. He is tutorial chair for SIAM SDM 2017, was program co-chair for ECML PKDD 2016, publicity co-chair for IUI 2015, sponsorship co-chair for ECML PKDD 2014, workshop co-chair of IEEE ICDM 2012. He co-organised eight workshops and four tutorials. He is a member of the editorial board of Data Mining and Knowledge Discovery (DAMI) and of the ECML PKDD Journal Track Guest Editorial Board, in addition he regularly reviews for TKDD, KAIS, TKDE, as well as for KDD, ICDM, SDM, ECML PKDD. He obtained his M.Sc. in Computer Science from Universiteit Utrecht, the Netherlands. He pursued his Ph.D. at the same university under supervision of Arno Siebes, and defended his thesis ‘Making Pattern Mining Useful’ in 2009. Between 2009 and 2013 he was a post-doctoral researcher at the University of Antwerp, supported by a Post-doctoral Fellowship of the Research Foundation – Flanders (FWO).
16:00-16:30 (CEST). Session 2: Network Security & Privacy Session chair: Ibéria Medeiros, University of Lisboa
Workshop on High-performance computing platforms for dependable autonomous systems
15:00-16:00 (CEST). HPCDS #1 : Hardware Platforms
16:00-16:30 (CEST). HPCDS #2 : Software Platforms
16:30-17:30 (CEST). HPCDS #3: Certification Challenges
Workshop on Safety and Security of Intelligent Vehicles
15:05-15:50 (CEST). SSIV #1: AI and adaptive systems
chaired by Michaël Lauer
15:55-16:40 (CEST). SSIV #2: Dependability and security analysis
chaired by Joao Cunha
16:45-17:30 (CEST). SSIV #3: Architecture and deployment
chaired by Kalinka Branco
17:40-18:30 (CEST). SSIV #4: Panel and closing remarks
"Future Challenges in Safety and Security of Intelligent Vehicle"
Cross-Layer Soft-Error Resilience Analysis of Computing Systems
In a world with computation at the epicenter of every activity, computing systems must be highly resilient to errors even if miniaturization makes the underlying hardware unreliable. Techniques able to guarantee high reliability are associated to high costs. Early resilience analysis has the potential to support informed design decisions to maximize system-level reliability while minimizing the associated costs. This tutorial focuses on early cross-layer (hardware and software) resilience analysis considering the full computing continuum (from IoT/CPS to HPC applications) with emphasis on soft errors. The tutorial will guide attendees from the definition of the problem down to the proper modeling and design exploration strategies considering the full system stack (i.e., from circuit to software).
Students, researchers and practitioners working on computing systems hardware and software design, with concerns about the impact of hardware faults on the full system level operation.
It is expected a basic understanding of computing systems hardware and software such as: logic design, computer architecture and microarchitecture, operating systems and programming. Some basic background on hardware defect mechanisms, fault and error modeling.
The tutorial is organized in an incremental manner. It starts with an introduction to reliability and cross-layer techniques followed by the main techniques applied at each abstraction level (e.g., circuits, architecture and software). The last part is focused on the most advanced concepts of stochastic cross-layer modelling, analysis and optimization. The agenda will be:
The handouts for this tutorial can be downloaded from here.
Tutorial video with Q&A
Into the Unknown: Unsupervised ML Algorithms for Anomaly-Based Intrusion Detection
One of the open challenges of past and recent systems is to identify errors before they escalate into failures. To such extent, most of the Error Detectors or enterprise Intrusion Detection Systems adopt signature-based detection algorithms, which consist of looking for predefined patterns (or "signatures") in the monitored data in order to detect an error or an ongoing attack. Data is usually seen as a flow of data points, which represent observations of the values of the indicators at a given time. Signature-based approaches usually score high detection capabilities and low false positive rates when experimenting known errors or attacks, but they cannot effectively adapt their behaviour when systems evolve or when their configuration is modified. As an additional consequence, signature-based approaches are not meant to detect zero day attacks, which are novel attacks that cannot be matched to any known signature. Moreover, when a zero-day attack that exploit newly added or undiscovered system vulnerabilities is identified, its signature needs to be derived and added as a new rule to the IDS.
To deal with unknowns, research moved to techniques suited to detect unseen, novel attacks. Anomaly detectors are based on the assumption that an attack generates observable deviations from an expected – normal – behaviour. Briefly, they aim at finding patterns in data that do not conform to the expected behaviour of a system: such patterns are known as anomalies. Once an expected behaviour is defined, anomaly detectors target deviations from such expectations, protecting against known attacks, zero-day attacks and emerging threats. To such extent, most of the anomaly detection algorithms are unsupervised, suiting the detection, among others, of unknown errors or zero-day attacks, without requiring labels in training data
The primary learning objectives of the tutorial are to demonstrate the capability of unsupervised learning algorithm to detect cyber-attacks and in particular zero-day attacks, and to instruct the attendees on the process to perform a well-crafted evaluation campaign.
In fact, after showing the current threat landscape as expanded by technical reports of agencies as ENISA, we will introduce anomaly detection, which is acknowledged as the most reliable answer to the detection of unknown errors or attacks. The participants will understand and use unsupervised algorithms that are particularly suited for anomaly detection, the main families and the differences in the way they decide if a data point is anomalous or normal. Participants will be involved in an hands-on session by using the RELOAD tool, which allows executing unsupervised anomaly detection algorithms and observing metric scores they provide on different datasets. This hands-on session, which can be conducted individually or in groups, will originate the final session which will constitute the final takeover of the tutorial, based both on participants activities and organizers’ experience in the domain.
The RELOAD tutorial targets anyone who is interested in the application of unsupervised ML algorithms for intrusion detection, with PhD students or young researchers as primary target audience. Consequently, we expect a remarkable amount of conference attendees to be interested in the topics of this tutorial, which targets beginners, with some content for intermediate. In fact, the tool to be used in the hand-on session will allow PhD students, researchers and practitioners who are starting to explore the discipline to get their first quantitative estimation of attack detection capabilities of algorithms, hiding implementation details which may be difficult to control at a first stage.
The tutorial will be composed by the following blocks.
Tutorial videos with Q&A
Into the Unknown: Unsupervised ML Algorithms for Anomaly-Based Intrusion Detection - Part 1
Into the Unknown: Unsupervised ML Algorithms for Anomaly-Based Intrusion Detection - Part 2
The InterPlanetary File System and the filecoin network
The InterPlanetary File System (IPFS) is a peer-to-peer content-addressable distributed file
IPFS resembles past and present efforts to build and deploy Information-Centric Networking
The main objective of this tutorial is to let researchers, developers, and users understand IPFS
More specifically, participants will:
The attendees do not need to have prior knowledge of IPFS, libp2p or filecoin and basic
Tutorial videos with Q&A
The InterPlanetary File System and the filecoin network - Part 1
The InterPlanetary File System and the filecoin network - Part 2
The InterPlanetary File System and the filecoin network - Part 3
15:00-15:15 (CEST). Welcome
by Sara Bouchenak
15:15-16:00 (CEST). Keynote: The Hard Path to Excellence or…why excellence is about details Paulo Verissimo, University of Luxembourg chaired by Saman Zonouz
Top-level research is a highly competitive environment: funding; recruiting; publishing; impact … If you move in the first division, academia is like a premier league, and top researchers are high-level competition athletes. Is that too stressing? Where is the fun? Depends on the perspective. There is no unique recipe, but I’ll share my own experience and hope to show that it can be a unique life, if you do the right things.
If you manage the balance between freedom, self-responsibility, and perseverance, chances are you will go far, and have moments you'll never forget. How far? Well, if you are aiming for the gold, the nice secret of this talk is that excellence … is about details.
Paulo Esteves-Veríssimo is a professor and FNR PEARL Chair at the University of Luxembourg FSTM and SnT, and Head of the CritiX lab (https://wwwen.uni.lu/snt/research/critix). He is adjunct Professor of the ECE Dept., Carnegie Mellon University. Previously, he has been a professor of the Univ. of Lisbon (PT). He is the representative of UNILU-SnT in ECSO, the European Cyber Security Organisation, and member of its Scientific & Technical Committee (STC). He was Chair of the IFIP WG 10.4 on Dependable Computing and Fault-Tolerance and vice-Chair of the Steering Committee of the IEEE/IFIP DSN conference.
He is Fellow of the IEEE and Fellow of the ACM, and associate editor of IEEE Trans. on Emerging Topics in Computing (TETC). He is currently interested in architectures, middleware and algorithms for resilient modular and distributed computing, in areas like: SDN-based infrastructures; autonomous vehicles from earth to space; digital health and genomics; or blockchain and cryptocurrencies. He is author or co-author of over 200 peer-refereed int’l publications and
16:15-17:00 (CEST). Session 1
chaired by Isabelly Rocha
17:15-18:15 (CEST). Session 2
chaired by Amy Babay
Wednesday, July 1
Thursday, July 2