相关论文: Maybenot: A Framework for Traffic Analysis Defense…

Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation. However, cybersecurity attacks pose a threat to VANETs and the…

According to recent studies, the vulnerability of state-of-the-art Neural Networks to adversarial input samples has increased drastically. A neural network is an intermediate path or technique by which a computer learns to perform tasks…

Vehicular Ad Hoc Networks (VANETs) improves traffic management and reduce the amount of road accidents by providing safety applications. However, VANETs are vulnerable to variety of security attacks from malicious entities. An…

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to…

Botnet is one of the numerous attacks ravaging the networking environment. Its approach is said to be brutal and dangerous to network infrastructures as well as client systems. Since the introduction of botnet, different design methods have…

The availability of sophisticated technologies and methods of perpetrating criminogenic activities in the cyberspace is a pertinent societal problem. Darknet is an encrypted network technology that uses the internet infrastructure and can…

Vehicular social networking is an emerging application of the promising Internet of Vehicles (IoV) which aims to achieve the seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular…

While the evolution of the Internet was driven by the end-to-end model, it has been challenged by many flavors of middleboxes over the decades. Yet, the basic idea is still fundamental: reliability and security are usually realized…

Anonymity systems like Tor are vulnerable to Website Fingerprinting (WF) attacks, where a local passive eavesdropper infers the victim's activity. Current WF attacks based on deep learning classifiers have successfully overcome numerous…

The Internet traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and device manufacturers, and often shared with their third parties to maintain and enhance user services.…

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems, such as their overwhelming complexity and propensity for error propagation. Autonomous driving transcends conventional traffic…

Tor onion services rely on long-lived introduction circuits to support anonymous rendezvous between clients and services. Although Tor incorporates defenses against traffic analysis, the introduction protocol retains deterministic routing…

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance,…

The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of techniques, such as machine learning and deep learning…

From face recognition systems installed in phones to self-driving cars, the field of AI is witnessing rapid transformations and is being integrated into our everyday lives at an incredible pace. Any major failure in these system's…

Cryptocurrency off-chain networks such as Lightning (e.g., Bitcoin) or Raiden (e.g., Ethereum) aim to increase the scalability of traditional on-chain transactions. To support nodes in learning about possible paths to route their…

Intrusion detection has attracted a considerable interest from researchers and industries. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of…

Extensive research has highlighted the vulnerability of graph neural networks (GNNs) to adversarial attacks, including manipulation, node injection, and the recently emerging threat of backdoor attacks. However, existing defenses typically…

Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent…

Traffic forecasting based network operation optimization and management offers enormous promise but also presents significant challenges from traffic forecasting perspective. While deep learning models have proven to be relatively more…