Data Science & Machine Learning for Cybersecurity, IoT & Digital Forensics

Goals of the workshop:

This workshop aims to stimulate dynamic, progressive, potentially paradigm-altering innovative practices and methodologies for cybersecurity, IoT, and digital forensics that leverage state-of-the-art data science and machine learning approaches to facilitate or automate the detection, prevention, and mitigation process.

Workshop theme:

Security is the topmost concern in both the physical and digital worlds. The challenges in ensuring security and privacy in cyberspace are ever-evolving, with a multidisciplinary scope. In addition, cyber adversaries are becoming more sophisticated in creating and launching their attacks. Individuals, government, corporate, and non-profit organizations are equally vulnerable to cyber-attacks. These attacks uncover individual data, vital government resources, and corporate insider facts. Consequently, they impact individual well-being and bring more significant risks. It is of prime importance that the cybersecurity and digital forensics community develops resources, methods, and best practices that can identify security and privacy issues and support case investigations in various aspects of computing: software, database, operating systems, network, and so on.

While cybersecurity risks are higher than ever with increased reliance on technologies with the advent of the Covid-19 pandemic, various surveys and workforce data published by the government, public institutions, and private organizations indicate a prominent skill and talent gap in the cybersecurity and digital forensics workforce. Therefore, it is intrinsic to rely on designing and building predictive models to lessen reliance on human experts and address this gap. This workshop aims at exploring the application of data science and machine learning approaches in the area of cybersecurity and digital forensics and highlights the outcomes from current security incidents, including the notion of the threats, the overall status of vulnerabilities, and the conceivable outcomes of security drawbacks.

This workshop encourages academics and practitioners to share strategies and methodologies, business innovations, security measurements, risk assessment practices, guidelines and policies, physical security challenges and solutions, and novel tools and technologies to develop secure systems and manage privacy.

Scope of the workshop:

The workshop invites high-quality manuscripts encompassing proven research, prototypes, proof of concepts, novel ideas, experimental results, technical papers, and methodologies addressing different attributes of cybersecurity, IoT, and digital forensics. Submitted work

should focus on the interest of multi-disciplinary stakeholders of cybersecurity and digital forensics with a focus on overcoming discrimination and appraising the interrelationships among components that involve an advanced cybersecurity framework, computational architecture, programming paradigm, policies, and individuals.

The topics of interest addressing challenges include but are not limited to:

• Security Lifecycle: Secure Design and Development
• Defensive algorithms
• Protecting privacy and security in digital communication
• Managing privacy and security with heterogeneous platforms (e.g., Cloud, IoT, Social Networks)
• Role of governments, laws, and policies
• Digital Identity: Trust, Authority, and Authorization
• Insider threats and network activities
• Risk and Vulnerability Analysis
• Defender response: tools and strategies
• Cybersecurity Education: tools and methodologies
• Tools for digital forensics
• Data sciences for digital forensics
• Machine learning, deep learning models for predicting digital forensics events