CyberDD 2017 will include two research tracks:
- Detecting and Mining Terrorist Online Content
- Advances in Data Science for Cyber Security and Risk on the Web
Detecting and Mining Terrorist Online Content
This track welcomes original research on detection, search, and mining methods that focus on the particularly challenging and idiosyncratic terrorist and violent extremist content on the Web (including the social media and the dark Web). Such content appears in multiple languages and media (e.g. text, images, video, and audio), it is highly volatile, often with short longevity, and it may be covert, even when it is publicly available. The proposed methods and techniques will allow for the development of effective and efficient systems and tools that are of particular interest to major search engines and social media platforms in their efforts to detect terrorist and violent extremist online content that may appear on their services, whilst striving to protect though fundamental citizens rights.
The topics include, but are not limited to, the following areas:
- Discovery and detection of terrorist online content: crawling the Web, social media, and darknets
- Data, entity, and relationship extraction from terrorism-related multimedia content
- Machine learning and multimedia data mining for intelligence purposes
- Classification and clustering of Web terrorist content
- Geolocation analysis of terrorist-related Web data
- Attribution of discovered terrorist content
- Semantic multimedia analysis for terrorism-related content understanding
- Multimedia forensics for detecting manipulations on online content
- Multilingual search and mining for terrorism-related Web information
- User profiling, persona modelling, and activity mining
- Social network analysis for terrorism communities detection and key player identification
- Pattern recognition in online terrorism-related activities
- Search interaction log analysis for terrorism detection
- Search and mining of multimedia Web resources about terrorism-related topics (e.g. homemade explosives recipes)
- Experiments and evaluation in Web search and data mining of terrorist online content
- Credibility of discovered terrorism-related online information
- Predictive modelling and early warning for terrorist threats
- Sentiment analysis in discovered Web content for predicting terrorist threats
- Summarisation of terrorist Web content and activity
- Web data visualisation (including the social and the dark Web)
- Privacy, security, and civil liberties implications
- Ethical and legal issues in detecting and analysing terrorism-related online content
Our main goals are: (i) presenting the most recent Web search and data mining methods for the detection, extraction, and analysis of Web content related to terrorism and violent extremism, (ii) bringing together researchers from the WSDM and security informatics communities, as well as industry representatives from search engines and social media platforms, to share ideas and experiences in designing and implementing search and mining techniques and tools to address terrorist online content, while also providing the opportunity to interact with representatives of law enforcement agencies, who will provide their perspectives on what constitutes terrorist and violent extremist content and also their insights on their efforts to prevent, investigate, and mitigate terrorism on the Web, (iii) evaluating the effectiveness, efficiency, and maturity of the Web search and data mining techniques for applications in real problems of terrorism detection, (iv) raising awareness of the privacy, legal, and ethical implications of the proposed methods, techniques, and overall efforts to fight terrorism and extremist violence.
Advances in Data Science for Cyber Security and Risk on the Web
This workshop will bring together scholars from multiple disciplines to discuss advances in Data Science and associated methods of quantitative analysis for the purposes of better understanding and forecasting threats to cyber security emanating from the Web. This essentially means any interactive socio-technical system that is underpinned by networked protocols including, but not limited, social networking sites, command and control Web servers, SMS, Web-linked sensor devices, email, Web logs and wikis etc. These systems have been theorized as Social Machines by some, and the workshop will include a keynote on Social Machines and their implications for Cyber Security. The substantive focus of the workshop will be to blend Criminology with Data Science for the study of cybercrime/security, and submissions will be required to contribute to both communities – as this is necessary to gain true insight based on theory, method and data. There will be a second keynote framing the day around the research challenges facing interdisciplinary scholars in this area, and will present some of the latest findings on this topic, encouraging others to pursue an interdisciplinary approach to addressing some of the grand challenges.
Topics of Interest include Web mining, Machine Learning and Statistical modelling for:
- Malware classification and clustering
- Cybercrime in distributed systems
- Understanding interactive social systems and implications for cyber security
- Web, IoT and Cyber Security
- Modelling deviant behaviour on the Web
- Criminological theory adapted to the Web