Related papers: Survey and Analysis of DNS Filtering Components

Content-Centric Networking (CCN) is an emerging networking paradigm being considered as a possible replacement for the current IP-based host-centric Internet infrastructure. In CCN, named content becomes a first-class entity. CCN focuses on…

Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed…

Stream deinterleaving is an important problem with various applications in the cybersecurity domain. In this paper, we consider the specific problem of deinterleaving DNS data streams using machine-learning techniques, with the objective of…

Named Data Networking (NDN) is a prominent realization of the vision of Information-Centric Networking. The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates…

The significance of the DDoS problem and the increased occurrence, sophistication and strength of attacks has led to the dawn of numerous prevention mechanisms. Each proposed prevention mechanism has some unique advantages and disadvantages…

In this paper, we study the performance of encrypted DNS protocols and conventional DNS from thousands of home networks in the United States, over one month in 2020. We perform these measurements from the homes of 2,693 participating…

In cybersecurity, allow lists play a crucial role in distinguishing safe websites from potential threats. Conventional methods for compiling allow lists, focusing heavily on website popularity, often overlook infrequently visited legitimate…

In the era of data proliferation, efficiently sifting through vast information to extract meaningful insights has become increasingly crucial. This paper addresses the computational overhead and resource inefficiency prevalent in existing…

We explored leveraging state-of-the-art deep learning, big data, and natural language processing to enhance the detection of vulnerable web server versions. Focusing on improving accuracy and specificity over rule-based systems, we…

Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter the network prediction…

In recent years, significant progress has been made in deep model-based automatic speech recognition (ASR), leading to its widespread deployment in the real world. At the same time, adversarial attacks against deep ASR systems are highly…

Previous attacks that link the sender and receiver of traffic in the Tor network ("correlation attacks") have generally relied on analyzing traffic from TCP connections. The TCP connections of a typical client application, however, are…

Deep learning solutions are instrumental in cybersecurity, harnessing their ability to analyze vast datasets, identify complex patterns, and detect anomalies. However, malevolent actors can exploit these capabilities to orchestrate…

Botnet is most widespread and occurs commonly in today's cyber attacks, resulting in serious threats to our network assets and organization's properties. Botnets are collections of compromised computers (Bots) which are remotely controlled…

In contrast to today's IP-based host-oriented Internet architecture, Information-Centric Networking (ICN) emphasizes content by making it directly addressable and routable. Named Data Networking (NDN) architecture is an instance of ICN that…

The Domain Name System (DNS) is involved in practically all web activity, translating easy-to-remember domain names into Internet Protocol (IP) addresses. Due to its central role on the Internet, DNS exposes user web activity in detail. The…

Firewalls are security devices that perform network traffic filtering. They are ubiquitous in the industry and are a common method used to enforce organizational security policy. Security policy is specified on a high level of abstraction,…

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating…

In a DDoS attack (Distributed Denial of Service), an attacker gains control of many network users through a virus. Then the controlled users send many requests to a victim, leading to its resources being depleted. DDoS attacks are hard to…

Spam filters are a crucial component of modern email systems, as they help to protect users from unwanted and potentially harmful emails. However, the effectiveness of these filters is dependent on the quality of the machine learning models…