Search for: All records

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin 215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. 

This survey paper provides an overview of the current state of Artificial Intelligence (AI) attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques. 

The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities 

This paper describes the design, implementation, and results of an NSF funded Summer Academy from 2016 to 2018, which engaged, on an annual basis, 30 to 60 rising 10th and 11th grade high school science students in an innovative, technology enriched Project Based Learning (PBL) environment. This Academy emphasized how tech gadgets work and the impact that technology can have on improving communities by immersing students in the exploration of one such device that is a growing phenomenon, the “aerial drone.” In this Academy, the students learned various operations of the drone through Python programming language, and some cybersecurity issues and solutions. The student teams, under the guidance of diverse mentors, comprehensively fortified their STEM problem-solving skills and critical thinking. Both formative and summative evaluations for this Academy showed that it helped students improve their critical thinking ability and motivated them to pursue careers in STEM-related disciplines, specifically in information technology and cybersecurity areas. 

As mobile computing is now becoming more and more popular, the security threats to mobile applications are also growing explosively. Mobile app flaws and security defects could open doors for hackers to break into them and access sensitive information. Most vulnerabilities should be addressed in the early stage of mobile software development. However, many software development professionals lack awareness of the importance of security vulnerability and the necessary security knowledge and skills at the development stage. The combination of the prevalence of mobile devices and the rapid growth of mobile threats has resulted in a shortage of secure software development professionals. Many schools offer mobile app development courses in computing curriculum; however, secure software development is not yet well represented in most schools' computing curriculum. This paper addresses the needs of authentic and active pedagogical learning materials for SSD and challenges of building Secure Software Development (SSD) capacity through effective, engaging, and investigative approaches. In this paper, we present an innovative authentic and active SSD learning approach through a collection of transferrable learning modules with hands-on companion labs based on the Open Web Application Security Project (OWASP) recommendations. The preliminary feedback from students is positive. Students have gained hands-on real world SSD learning experiences with Android mobile platform and also greatly promoted self-efficacy and confidence in their mobile SSD learning. 

The security threats to mobile application are growing explosively. Mobile app flaws and security defects could open doors for hackers to easily attack mobile apps. Secure software development must be addressed earlier in the development lifecycle rather than fixing the security holes after attacking. Early eliminating against possible security vulnerability will help us increase the security of our software, and militate the consequence of damages of data loss caused by potential malicious attacking. However, many software developer professionals lack the necessary security knowledge and skills at the development stage and Secure Mobile Software Development (SMSD) is not yet well represented in current computing curriculum. In this paper we present a static security analysis approach with open source FindSecurityBugs plugin for Android Studio IDE. We categorized the common mobile vulnerability for developers based on OWASP mobile security recommendations and developed detectors to meet the SMSD needs in industry and education.