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Intrusion detection has focused primarily on detecting cyberattacks at the event-level. Since there is such a large volume of network data and attacks are minimal, machine learning approaches have focused on improving accuracy and reducing…

Machine learning datasets are powerful but unwieldy. Despite the fact that large datasets commonly contain problematic material--whether from a technical, legal, or ethical perspective--datasets are valuable resources when handled carefully…

Serious games are increasingly being used in cybersecurity education to engage and educate users. Several studies with cybersecurity serious games have shown that they are successful in educating users and the users also find them both fun…

In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this…

Solving cybersecurity issues requires a holistic understanding of components, factors, structures and their interactions in cyberspace, but conventional modeling approaches view the field of cybersecurity by their boundaries so that we are…

We consider data poisoning attacks, a class of adversarial attacks on machine learning where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier satisfy certain objectives. While…

Virtual Laboratories (V Labs) have in the recent past become part and parcel of remote teaching in practical hands-on approaches, particularly in Cybersecurity distance courses. Their potential is meant to assist learners with hands-on…

Quality education, one of the seventeen sustainable development goals (SDGs) identified by the United Nations General Assembly, stands to benefit enormously from the adoption of artificial intelligence (AI) driven tools and technologies.…

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning…

Adversary thinking is an essential skill for cybersecurity experts, enabling them to understand cyber attacks and set up effective defenses. While this skill is commonly exercised by Capture the Flag games and hands-on activities, we…

Dependence on information, including for some of the world's largest organisations such as governments and multi-national corporations, has grown rapidly in recent years. However, reports of information security breaches and their…

The emergence of AI tools in cybersecurity creates many opportunities and uncertainties. A focus group with advanced graduate students in cybersecurity revealed the potential depth and breadth of the challenges and opportunities. The…

Research involving at-risk users -- that is, users who are more likely to experience a digital attack or to be disproportionately affected when harm from such an attack occurs -- can pose significant safety challenges to both users and…

Young people are increasingly exposed to adverse effects of data-driven profiling, recommending, and manipulation on social media platforms, most of them without adequate understanding of the mechanisms that drive these platforms. In the…

Datasets have played a foundational role in the advancement of machine learning research. They form the basis for the models we design and deploy, as well as our primary medium for benchmarking and evaluation. Furthermore, the ways in which…

As internet related challenges increase such as cyber-attacks, the need for safe practises among users to maintain computer system's health and online security have become imperative, and this is known as cyber-hygiene. Poor cyber-hygiene…

Multi-institutional studies are critical for advancing discipline-based education research (DBER) because they allow us to determine where and for whom research findings are applicable. Despite this benefit, such studies remain relatively…

Artificial Intelligence (AI) relies heavily on deep learning - a technology that is becoming increasingly popular in real-life applications of AI, even in the safety-critical and high-risk domains. However, it is recently discovered that…

It is undeniable that we are witnessing an unprecedented digital revolution. However, recent years have been characterized by the explosion of cyberattacks, making cybercrime one of the most profitable businesses on the planet. That is why…

In this article I describe a research agenda for securing machine learning models against adversarial inputs at test time. This article does not present results but instead shares some of my thoughts about where I think that the field needs…