The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security-sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models' performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNN's accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.

The primary goal of the authentic learning approach is to engage and motivate students in a learning environment that encourages all students in learning. This approach provides students with hands-on experiences in solving real-world security problems. We designed and developed ten learning modules based on 10 cybersecurity cases with different ML solutions. Each learning module consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. All portable labs are made available on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will engage students in learning concepts and getting more experience for hands-on problem-solving skills. In this paper, we adopt Neural Network Algorithms for Network Denial of Service (DOS) Detection where we apply the KDDCup 1999 datasets contain a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Our primary goal of this lab is to show whether a link is a malicious or safe connection. Our demonstration shows an achieved accuracy of 99.89%.

Quality of software is determined by analyzing the source code metrics and defects. Prediction of pre-and post-release defects and fixing them when they are raised will improve overall quality of software systems. The main aim of this work is to reduce the cost incurred in identifying defects which is a challenging task. Over a decade, the academia and industry addressed the problem of software defect prediction using machine learning. This work emphasizes on the selection of static code metrics and process metrics for defect prediction. Metrics are mined from different open source projects hosted on GitHub. For our experiments, we have chosen projects with at least 10k commits. This paper is establishing a hypothesis that, as the number of commits increase, the bugs are also likely to increase, as our experiments indicate. The software metrics are carefully chosen to identify defects across software projects to help the development and testing teams.

Ride-hailing and ride-sharing applications have recently gained popularity as a convenient alternative to traditional modes of travel. Current research into autonomous vehicles is accelerating rapidly and will soon become a critical component of a ride-hailing platform’s architecture. Implementing an autonomous vehicle ride-hailing platform proves a difficult challenge due to the centralized nature of traditional ride-hailing architectures. In a traditional ride-hailing environment the drivers operate their own personal vehicles so it follows that a fleet of autonomous vehicles would be required for a centralized ride-hailing platform to succeed. Decentralization of the ride-hailing platform would remove a roadblock along the way to an autonomous vehicle ride-hailing platform by allowing owners of autonomous vehicles to add their vehicle to a community-driven fleet when not in use. Blockchain technology is an attractive choice for this decentralized architecture due to its immutability and fault tolerance. This thesis proposes a framework for developing a decentralized ride-hailing architecture that is verifiably secure. This framework is implemented on the Hyperledger Fabric blockchain platform. The evaluation of the implementation is done by applying known security models, utilizing a static analysis tool, and performing a performance analysis under heavy network load.

The Aviation Industry has always been adopting superior technology and state-of-art concept that includes Aero-dynamics, Avionics, and Jet Engine Technologies, etc. There is, however, a new frontier in the Aviation Industry, development of the “Connected Aircraft” or so-called “Smart Aircraft”. However, as with any other new frontier, there are possibilities of pitfalls that need to be discussed and addressed. This paper discusses Wi-Fi connectivity on Smart Aircraft, the technologies involved, and the threats to security. The research was conducted via several sources including online research, scholarly papers, pilot interviews, and consultation with various aviation academies that train pilots focus only on aircraft/airliner Wi-Fi connectivity from within the cabin during the flight. Findings reveal the industry as emerging and in infancy, there are still scores of issues related to security and cost that aviation organizations must work extensively towards secure and safe Smart Aircraft.

In recent years, security has been a major concern to everyone. Some devices like energy meters, health appliances have been a life changing technology for smart city. These devices have assisted in connecting several different services and in turn have made life better for people living in smart city. Other services such as emergency responder, water system, disaster management, sanitation and infrastructure have evolved with the birth of a smart city. The rapid advancements in services provided in a smart city are now drawing major security concerns. One of the major security concerns are integrated systems. The purpose of this paper is to educate the audience of the vulnerabilities that are associated with the devices of a smart city. We demonstrate an example of hands-on lab hacking a CCTV camera and some suggestions to prevent attacks on smart city.

Mobile Health (mhealth) applications are widely used applications to monitor our health, well being and daily activities. The privacy and security of personal information while using mHealth apps are of great concerns. In this paper, we propose a method based on HIPAA privacy rule and security rule to analyze mHealth applications. There are total four parts in this method: privacy policy analysis, static analysis, dynamic analysis, and HTTP analysis. Through our analysis, we identify that mHealth applications are not as safe as they are shown in the posted privacy policy. Many mhealth apps could cause damage to personal privacy.

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

One of the current challenges that faces individual organizations that are attempting to utilize blockchain in making more secure and transparent transaction between organizations are standards. Not all platforms, however, are suitable. In this paper, we provide a comparison of blockchain development platforms for developers to choose the best platform based on their need.

Web Application Firewalls (WAFs) are deployed to protect web applications and they offer in depth security as long as they are configured correctly. A problem arises when there is over-reliance on these tools. A false sense of security can be obtained with the implementation of a WAF. In this paper, we provide an overview of traffic filtering models and some suggestions to avail the benefit of web app firewall.

This paper is addressing the challenges of incorporating networking and security concepts into effective teaching and learning platform (PLab) that highlights real-world technical issues. PLab is an innovative portable learning platform that allows network applications to be safely tested. The isolated network without the need for a server promotes learning at anytime and anywhere. The strong connection between academic subjects and reality and digital-native students' everyday lives engages students in learning this emerging field, and better prepares students for the high industrial demands in mobile application development workforce. Moreover, this pedagogical model will help faculty develop expertise in the latest development of networking and security. The modular labware is designed to offer faculty the flexibility to implement it in many existing courses. Curricular materials will be delivered on Google cloud for its sustainability. This "ready-to-adopt" model will greatly save resources and time for enhancing modern networking and security education to meet the emerging workforce.