In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands their usage on a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs’ security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs.

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 attacks against Industrial Control Systems are one of the major concerns for worldwide manufacturing companies. With the growth of emerging technologies, protecting large-scale Critical Infrastructures has become a considerable research topic in the past decade. Nowadays, software used to monitor Industrial Control Systems might be malicious and cause harm not only to physical processes but also to people working in industrial environments. To that end, integrating safety and security in Industrial Control Systems requires a well-developed understanding of malware-based cyber attacks. In this paper, we present a comparative analysis framework of ICS Malware in a bi-layered approach: A cyber threat intelligence layer based on the ICS cyber kill chain and a hybrid analysis layer based on a static and dynamic analysis of ICS malware. We evaluated our proposed method by experimenting five well-known ICS malware: Stuxnet, Havex, BlackEnergy2, CrashOverride, and TRISIS. Our comparative analysis results show different and similar strategies used by each ICS malware to disrupt the ICS environment.

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. In this paper, we present a threat modeling framework for Industrial Control Systems malware across two different levels: the Extraction Level and the Modeling Level. We evaluate the effectiveness of our model by analyzing the TRISIS cyber attack as a use case. A complex malware developed to cause operational disruption to industrial plants. Our solution outperforms existing malware threat modeling techniques for the ICS environment, and provides useful mitigation strategies to counter malicious activities.