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Microsoft’s announcement this week that it is adding support for two new security standards in Exchange Online is seen by experts as encouraging news — as long as CISOs and [...]

Nowadays, domain names are becoming crucial digital assets for any business. However, the media never stopped reporting phishing and identity theft attacks held by third-party entities that rely on domain names to mislead Internet users. Thus, Palo Alto Networks revealed in their studies 20 largely cyber-squatted domain names targeting popular brands. Based on their behavior, domain names appear in public lists that objectively evaluate their reputation. Blacklists contain domain names that have previously committed suspicious acts, whereas whitelists include the most popular and trustworthy domain names. For a long time, this listing technique has been used as a reactive approach to counter domain name-based attacks. However, it suffers from the limitation of responding late to attacks. Nowadays, techniques tend to be much more proactive. They operate before any attack occurs. As part of the CSNET conference, we published a short paper that describes a plethora of domain name attacks and their associated detection techniques using their lexical features (Hamroun et al. 2022). In this paper, we present an extended version of the original one which discusses the previously mentioned points in more detail and adds some elements of understanding when it comes to malicious domain name detection. Hence, we provide a literature review of malicious domain name detection techniques that use only the lexical features of domain names. These features are available, privacy-preserving, and highly improve detection results. The review covers recent works that report relevant performance categorized according to a new taxonomy. Moreover, we introduce a new criterion for comparing all the existing works based on targeted maliciousness type before discussing the limitations and the newly emerging research directions in this field. [ABSTRACT FROM AUTHOR]

The vast global Domain Name System (DNS) is so fundamental to the way the web works that service providers and their customers are sure to configure and manage it carefully. [...]

Introduction to The Problem: The problem of the study was that the Jordanian legislative system is devoid of any special legal regulation or even a system that defines domain names in terms of their nature and means of legal protection for them. There is only the "Registration Policy" that the Ministry of Digital Economy and Entrepreneurship is implementing to register national domain names. This policy only addresses the technical and procedural aspect of domain name registration without specifying its legal nature. Purpose/Objective Study: The purpose of the study is to illustrate the topic's uniqueness and theoretical and practical significance. Due to the lack of specific regulations in many countries and the rise of cases handled by courts in this area, it presents several practical and legal issues. Therefore, the study aims to shed light on this phenomenon and try to find the best solutions to it in light of Jordanian legislation and the rules of the Unified Policy for Resolving Domain Name Disputes issued by ICANN. Design/Methodology/Approach: In its preparation, the study relies on the descriptive and analytical approach by describing the case, citing relevant legal texts, analyzing them, and applying them to reality. This is done by analyzing the texts of the Jordanian national domain name registration policy and comparing it with the legal texts contained in the rules of the Unified Policy for Resolving Domain Name Disputes issued by the ICANN under study. Findings: The study recommended a number of recommendations, the most important of which is the need to enact legislation specific to national domain names to determine the nature of these names and their legal nature. This legislation also includes provisions for their legal protection, stipulating appropriate legal ways and means to confront the assault on them, and provisions for liability resulting from them. [ABSTRACT FROM AUTHOR]

Abstract Domain name generation algorithm (DGA) classification is an essential but challenging problem. Both feature-extracting machine learning (ML) methods and deep learning (DL) models such as convolutional neural networks and long short-term memory have been developed. However, the performance of these approaches varies with different types of DGAs. Most features in the ML methods can characterize random-looking DGAs better than word-looking DGAs. To improve the classification performance on word-looking DGAs, subword tokenization is employed for the DL models. Our experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking DGAs. We then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the DGAs. Results show that the integrated scheme outperformed existing ML and DL methods, and also the subword DL methods.

Domain name generation algorithm (DGA) classification is an essential but challenging problem. Both feature-extracting machine learning (ML) methods and deep learning (DL) models such as convolutional neural networks and long short-term memory have been developed. However, the performance of these approaches varies with different types of DGAs. Most features in the ML methods can characterize random-looking DGAs better than word-looking DGAs. To improve the classification performance on word-looking DGAs, subword tokenization is employed for the DL models. Our experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking DGAs. We then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the DGAs. Results show that the integrated scheme outperformed existing ML and DL methods, and also the subword DL methods. [ABSTRACT FROM AUTHOR]