Phishing in email and instant messaging

Abstract. Phishing is a constantly evolving threat in the world of information security that affects everyone, no matter if you’re a retail worker or the head of IT in a large organisation. Because of this, this thesis aims to give the reader a good overview of what phishing is, and due to its prevalence in email and instant messaging, focuses on educating the reader on common signs and techniques used in phishing in the aforementioned forms of communication. The chosen research method is literature review, as it is the ideal choice for presenting an overview of a larger subject. As a result of the research, many common phishing signs and techniques in both email and instant messaging are presented. Some of these signs include strange senders, fake domain names and spellings mistakes. With this thesis, anyone looking to improve their understanding about phishing can do so in a way that is easy to understand. Some suggestions for future research are also presented based on this thesis’ shortcomings, namely the lack of studies on phishing in instant messaging

PhishReplicant: A Language Model-based Approach to Detect Generated Squatting Domain Names

Domain squatting is a technique used by attackers to create domain names for phishing sites. In recent phishing attempts, we have observed many domain names that use multiple techniques to evade existing methods for domain squatting. These domain names, which we call generated squatting domains (GSDs), are quite different in appearance from legitimate domain names and do not contain brand names, making them difficult to associate with phishing. In this paper, we propose a system called PhishReplicant that detects GSDs by focusing on the linguistic similarity of domain names. We analyzed newly registered and observed domain names extracted from certificate transparency logs, passive DNS, and DNS zone files. We detected 3,498 domain names acquired by attackers in a four-week experiment, of which 2,821 were used for phishing sites within a month of detection. We also confirmed that our proposed system outperformed existing systems in both detection accuracy and number of domain names detected. As an in-depth analysis, we examined 205k GSDs collected over 150 days and found that phishing using GSDs was distributed globally. However, attackers intensively targeted brands in specific regions and industries. By analyzing GSDs in real time, we can block phishing sites before or immediately after they appear.Comment: Accepted at ACSAC 202

Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence

Open-source software (OSS) supply chain enlarges the attack surface, which makes package registries attractive targets for attacks. Recently, package registries NPM and PyPI have been flooded with malicious packages. The effectiveness of existing malicious NPM and PyPI package detection approaches is hindered by two challenges. The first challenge is how to leverage the knowledge of malicious packages from different ecosystems in a unified way such that multi-lingual malicious package detection can be feasible. The second challenge is how to model malicious behavior in a sequential way such that maliciousness can be precisely captured. To address the two challenges, we propose and implement Cerebro to detect malicious packages in NPM and PyPI. We curate a feature set based on a high-level abstraction of malicious behavior to enable multi-lingual knowledge fusing. We organize extracted features into a behavior sequence to model sequential malicious behavior. We fine-tune the BERT model to understand the semantics of malicious behavior. Extensive evaluation has demonstrated the effectiveness of Cerebro over the state-of-the-art as well as the practically acceptable efficiency. Cerebro has successfully detected 306 and 196 new malicious packages in PyPI and NPM, and received 385 thank letters from the official PyPI and NPM teams

Teaching Tip: Hook, Line, and Sinker – The Development of a Phishing Exercise to Enhance Cybersecurity Awareness

In this paper, we describe the development of an in-class exercise designed to teach students how to craft social engineering attacks. Specifically, we focus on the development of phishing emails. Providing an opportunity to craft offensive attacks not only helps prepare students for a career in penetration testing but can also enhance their ability to detect and defend against similar methods. First, we discuss the relevant background. Second, we outline the requirements necessary to implement the exercise. Third, we describe how we implemented the exercise. Finally, we discuss our results and share student feedback