Current communication networks are increasingly becoming pervasive, complex, and ever-evolving due to factors like enormous growth in the number of Internet users, the continuous appearance of network applications, increasing amount of data transferred, and diversity of user behaviors. Understanding and measuring traffic in such networks is a challenging yet vital task for network management but recently also for cybersecurity purposes. Network traffic measurement and monitoring can, for example, enable the analysis of the spreading of malicious software and its capabilities or can help to understand the nature of various network threats, including those that exploit users' behavior and other user's sensitive information. On the other hand, network traffic investigation can also help to assess the effectiveness of the existing countermeasures or contribute to building new, better ones. Recently, traffic measurements have been utilized in the area of economics of cybersecurity e.g. to assess ISP “badness” or to estimate the revenue of cyber criminals. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and industry in the field of cybersecurity and understand how traffic measurements can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. This workshop presents some of the most relevant ongoing research in cybersecurity seen from the traffic measurements perspective .

Advances in technology come with countless benefits for society, but these advances sometimes introduce new risks as well. Various characteristics of technology, including its increasing complexity, may present novel challenges in understanding its impact and addressing its risks. Enforcement agencies have broad jurisdiction to protect consumers against certain harmful practices (typically called "deceptive and unfair" practices in the United States), but sophisticated technical analysis may be necessary to assess practices, risks, and more. Moreover, consumer protection covers an incredibly broad range of issues, from substantiation of claims that a smartphone app provides advertised health benefits to the adequacy of practices for securing sensitive customer data.
The Workshop on Technology and Consumer Protection (ConPro ’20) will explore computer science topics with an impact on consumers. This workshop has a strong security and privacy emphasis, with an overall focus on ways in which computer science can prevent, detect, or address the potential for technology to deceive or unfairly harm consumers. Participants will consist heavily of academic and industry researchers but are also expected to include researchers from the Federal Trade Commission—the U.S. government’s primary consumer protection body—and other government agencies with a consumer protection mission. Research advances and ideas presented at the workshop may help improve the lives of consumers, and discussions at the event may help researchers understand how their work can best promote consumer welfare given laws and norms surrounding consumer protection.

Deep learning and security have made remarkable progress in the last several years. On the one hand, neural networks have been recognized as a promising tool for security in academia and industry. On the other hand, the security of deep learning has gained focus in research, and the robustness of neural networks has recently been called into question. This workshop strives for bringing these two complementary views together by (a) exploring deep learning as a tool for security as well as (b) investigating the security of deep learning. Deep learning for security: In the past, machine learning and security researchers have lacked suitable collaborative outlets and opportunities for knowledge transfer. This has hindered progress in fundamental challenges surrounding the detection and analysis of malicious code and security threats. As a result, the research pace in this area has been limited.
The workshop aims at bringing practitioners from both fields together and exploring novel opportunities for improving the detection and analysis of attacks as well as applying novel learning architectures and concepts. Security of deep learning: Deep learning has not been originally designed with security in mind, and many learning algorithms suffer from vulnerabilities that enable thwarting their successful application. As a consequence, the workshop will also investigate the security of deep learning and discuss novel attack and defense mechanisms. Only if both views are jointly considered, the application of deep learning can have a long-lasting effect on security and bring forth novel paradigms in security applications.
The workshop is aimed at academic and industrial researchers interested in deep learning and security. We are planning to invite distinguished speakers who will present recent advances in both areas and pinpoint promising directions for research. We believe that closer interaction between both research areas will overcome current joint problems and ultimately enable a bidirectional and fruitful knowledge transfer.

Current computer software that processes electronic data such as documents, images, videos, and messages is vulnerable to maliciously crafted input data. The Language-theoretic approach (LangSec) offers a connection between fundamental computability principles and practical systems vulnerabilities when handling untrusted inputs. LangSec posits that the only path to trustworthy computer software that takes untrusted inputs is treating all valid or expected inputs as a formal language, and the respective input-handling routine as a parser for that language. The parsing must be feasible, and the parser must match the language in required computation power and convert the input for the consumption of subsequent computation.
For some collected history and background on LangSec, please see the www.langsec.org site. The 6th installation of the workshop will continue the tradition and further focus on methodologies (1) that can infer formal language specifications from samples of electronic data, (2) that can generate secure parsers from formal specifications of electronic data, and (3) that describe the complexity hierarchy of verifying parser implementations.

The Internet of Things has become increasingly popular and innovative. With the rise of connected devices, we have an opportunity to significantly improve the safety of legacy systems. For instance, insights from data across systems can be exploited to reduce accidents, improve air quality and support disaster events. IoT based cyber-physical systems (CPS) also bring new risks that arise due to the unexpected interaction between systems and the larger number of attack vectors on these systems. These safety risks can arise in the context of use of medical devices, smart home appliance control, smart car design or conflicts in policy execution at a societal scale.
The Workshop on the Internet of Safe Things seeks to bring together researchers to create solutions for the development of safe cyber-physical systems. As safety is inherently linked with the security and privacy of a system, we also seek contributions in these areas that address safety concerns. We seek to develop a community that systematically dissects the vulnerabilities and risks exposed by these emerging CPSs, and creates tools, algorithms, frameworks, and systems that help in the development of safe systems. We seek contributions across domains - autonomous vehicles, smart homes, medical devices, smart grid; and across disciplines - systems, control, human-computer interaction, security, reliability, machine learning, and verification.

The SADFE (Systematic Approaches to Digital Forensic Engineering) International Workshop brings together researchers and practitioners focused on the state-of-the-art and emerging topics of interest in digital forensics. Namely, the workshop focuses on the topics of AI-generated falsified media (e.g. DeepFakes), cloud forensics, emerging and non-traditional methods (e.g. ), digital currency forensics, contact tracing, digital evidence management and analysis, forensics of embedded and non-traditional forensics, and related legal, ethical and technical challenges.
The workshop promotes systematic approaches to digital forensic investigation on the vulnerabilities of today’s cyber systems and networks, digital exploitation and manipulation, and on the emerging methods for how to detect, track, and prevent digital threats. SADFE embraces Digital Forensic Engineering (DFE) advancement as a disciplined and holistic scientific practice.

The Workshop on Offensive Technologies (WOOT) aims to present a broad picture of offense and its contributions, bringing together researchers and practitioners in all areas of computer security. Offensive security has changed from a hobby to an industry. No longer an exercise for isolated enthusiasts, offensive security is today a large-scale operation managed by organized, capitalized actors. Meanwhile, the landscape has shifted: software used by millions is built by startups less than a year old, delivered on mobile phones and surveilled by national signals intelligence agencies. In the field's infancy, offensive security research was conducted separately by industry, independent hackers, or in academia. Collaboration between these groups could be difficult. Since 2007, the Workshop on Offensive Technologies (WOOT) has aimed to bring those communities together.