Artificial Intelligence (AI) has become a fundamental tool for cybersecurity researchers and practitioners. It is frequently used to address major security problems such as supply chain attacks, ransomware threats, and social engineering. In this context, integrating AI into cybersecurity workflows requires incorporating AI-driven approaches into the educational training of the cybersecurity workforce. This paradigm shift in academic settings will introduce the necessary skills for cybersecurity professionals to operate modern AI-based systems. Yet, the current cybersecurity curriculum still suffers from the absence of AI resources, particularly the detailed understanding of the appropriate AI mechanisms. Such absence leaves skill gaps for future professionals and practitioners in the industry. To address this, we designed an academic lecture module on AI covering both theory and practice. Then, we taught the module across six cybersecurity courses in our institution. To assess the effectiveness of integrating AI materials into cybersecurity education, we collected data by presenting two surveys before and after the lecture (concluding 81 participants per survey). Specifically, we utilized widely accepted models for unbiased analysis of our data. Our experimental results show positive AI knowledge improvement by 30% of the participants, demonstrating the beneficial impact of the lecture. Then, we observed a high similarity score between the survey responses and the lecture content, reaching 84%. Moreover, our sentiment analysis results reflect positive feedback from the participants with a positive score of 0.50. Overall, our study serves as a reference for instructional designers for developing educational curricula aiming to integrate AI into cybersecurity education.

Abstract In the past few years, the popularity and wide use of video conferencing software enjoyed exponential growth in market size. This technology enables participants in different geographic regions to have a virtual face-to-face meeting. Additionally, it allows participants to utilize virtual backgrounds to hide their real environment with privacy concerns or to reduce distractions, particularly in professional settings. In scenarios where the users should not hide their actual locations, they may mislead other participants into assuming that the displayed virtual backgrounds are real. In this paper, we propose a new publicly-available dataset of virtual and real backgrounds in video conferencing software (e.g., Zoom, Google Meet, Microsoft Teams). The presented archive was evaluated by an exhaustive series of tests and scenarios using two well-known features extraction methods: CRSPAM1372 and six co-mat. The first verification scenario considers the case where the detector is unaware of manipulated frames (i.e., the forensically-edited frames are not part of the training set). A model trained on zoom frames that were tested with Google Meet frames can detect real background images from virtual ones in video conferencing software with 99.80% detection accuracy. Furthermore, it is possible to distinguish virtual from real backgrounds in videos created for videoconferencing software at a high detection rate of approximately 99.80%. According to our conclusions, the proposed method greatly enhanced the detection accuracy and resistance against diverse adversarial conditions, making it a reliable technique for classifying actual as opposed to virtual backgrounds in video communications. Given the described dataset provided and some preliminary experiments that we performed, we expect that it will lead to more future research in this domain.

Recent advancements in drone technology have shown that commercial off-the-shelf Micro Aerial Drones are more effective than large-sized drones for performing flight missions in narrow environments, such as swarming, indoor navigation, and inspection of hazardous locations. Due to their deployments in many civilian and military applications, safe and reliable communication of these drones throughout the mission is critical. The Crazyflie ecosystem is one of the most popular Micro Aerial Drones and has the potential to be deployed worldwide. In this paper, we empirically investigate two interference attacks against the Crazy Real Time Protocol (CRTP) implemented within the Crazyflie drones. In particular, we explore the feasibility of experimenting two attack vectors that can disrupt an ongoing flight mission: the jamming attack, and the hijacking attack. Our experimental results demonstrate the effectiveness of such attacks in both autonomous and non-autonomous flight modes on a Crazyflie 2.1 drone. Finally, we suggest potential shielding strategies that guarantee a safe and secure flight mission. To the best of our knowledge, this is the first work investigating jamming and hijacking attacks against Micro Aerial Drones, both in autonomous and non-autonomous modes.

In the past decades, the rise of artificial intelligence has given us the capabilities to solve the most challenging problems in our day-to-day lives, such as cancer prediction and autonomous navigation. However, these applications might not be reliable if not secured against adversarial attacks. In addition, recent works demonstrated that some adversarial examples are transferable across different models. Therefore, it is crucial to avoid such transferability via robust models that resist adversarial manipulations. In this paper, we propose a feature randomization-based approach that resists eight adversarial attacks targeting deep learning models in the testing phase. Our novel approach consists of changing the training strategy in the target network classifier and selecting random feature samples. We consider the attacker with a Limited-Knowledge and Semi-Knowledge conditions to undertake the most prevalent types of adversarial attacks. We evaluate the robustness of our approach using the well-known UNSW-NB15 datasets that include realistic and synthetic attacks. Afterward, we demonstrate that our strategy outperforms the existing state-of-the-art approach, such as the Most Powerful Attack, which consists of fine-tuning the network model against specific adversarial attacks. Further, we demonstrate the practicality of our approach using the VIPPrint dataset through a comprehensive set of experiments. Finally, our experimental results show that our methodology can secure the target network and resists adversarial attack transferability by over 60%.

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications, including computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1.5-Class (cmb-classifier) classifier and is constructed using a final dense classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and cmb-classifier architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the cmb-classifier.

Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by the attacker. This major property is known as transferability, and makes CNNs ill-suited for security applications. In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. Furthermore, we investigate whether the transferability property issue holds in computer networks applications. In our experiments, we first consider five different attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS (L-BFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then, we perform these attacks against three well-known datasets: the Network-based Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA) dataset, and the RIPE Atlas dataset. Our experimental results show clearly that the transferability happens in specific use cases for the I-FGSM, the JSMA, and the LBFGS attack. In such scenarios, the attack success rate on the target network range from 63.00% to 100%. Finally, we suggest two shielding strategies to hinder the attack transferability, by considering the Most Powerful Attacks (MPAs), and the mismatch LSTM architecture.

Cyber-physical attacks against plants and Critical Infrastructures (CIs) are among the most significant concerns in the 21st century and can lead to devastating consequences. In particular, with the convergence between the Operational Technology (OT) network and the traditional IT network, malware threats for Industrial Control Systems (ICSs) are gradually increasing. In these scenarios, we need to identify potential cyber threats by developing innovative modeling techniques. However, existing malware-based cyber threats modeling techniques are not fully designed for industrial environment.