NoFish; Total Anti-Phishing Protection System

Phishing attacks have been identified by researchers as one of the major cyber-attack vectors which the general public has to face today. Although software companies launch new anti-phishing products, these products cannot prevent all the phishing attacks. The proposed solution, 201C;No Fish201D; is a total anti-phishing protection system created especially for end-users as well as for organizations. In this paper, a realtime anti-phishing system, which has been implemented using four main phishing detection mechanisms, is proposed. The system has the following distinguishing properties from related studies in the literature: language independence, use of a considerable amount of phishing and legitimate data

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Software and systems complexity can have a profound impact on information security. Such complexity is not only imposed by the imperative technical challenges of monitored heterogeneous and dynamic (IP and VLAN assignments) network infrastructures, but also through the advances in exploits and malware distribution mechanisms driven by the underground economics. In addition, operational business constraints (disruptions and consequences, manpower, and end-user satisfaction), increase the complexity of the problem domain... Copyright SANS Institut

An Emerging Solution for Detection of Phishing Attacks

In this era of computer age, as more and more people use internet to carry out their day to day work so as hackers performs various security attacks on web browsers and servers to steal user’s vital data. Now Electronic mail (E-mail) is used by everyone including organizations, agency and becoming official communication for the society as a whole in day to day basis. Even though a lot of modern techniques, tools and prevention methods are being developed to secure the users vital information but still they are prone to security attacks by the fraudsters. Phishing is one such attack and its detection with high accuracy is one of the prominent research issues in the area of cyber security. Phisher fraudulently acquire confidential information like user-id, passwords, visa card and master card details through various social engineering methods. Mostly blacklist based methodology is used for detection of phishing attacks but this method has a limitation that it cannot be used for detection of white listed phishing. This chapter aims to use machine learning algorithms to classify between phishing E-mails and genuine E-mails and helps the user in detecting attacks. The architectural model proposed in this chapter is to identify phishing and use J48 decision tree classifier to classify the fake E-mail from real E-mail. The algorithm presented here goes through several stages to identify phishing attack and helps the user in a great way to protect their vital information

Jack Voltaic 3.0 Cyber Research Report

The Jack Voltaic (JV) Cyber Research project is an innovative, bottom-up approach to critical infrastructure resilience that informs our understanding of existing cybersecurity capabilities and identifies gaps. JV 3.0 contributed to a repeatable framework cities and municipalities nationwide can use to prepare. This report on JV 3.0 provides findings and recommendations for the military, federal agencies, and policy makers

Socio-Technical Aspects of Security Analysis

This thesis seeks to establish a semi-automatic methodology for security analysis when users are considered part of the system. The thesis explores this challenge, which we refer to as ‘socio-technical security analysis’. We consider that a socio-technical vulnerability is the conjunction of a human behaviour, the factors that foster the occurrence of this behaviour, and a system. Therefore, the aim of the thesis is to investigate which human-related factors should be considered in system security, and how to incorporate these identified factors into an analysis framework. Finding a way to systematically detect, in a system, the socio-technical vulnerabilities that can stem from insecure human behaviours, along with the factors that influence users into engaging in these behaviours is a long journey that we can summarise in three research questions: 1. How can we detect a socio-technical vulnerability in a system? 2. How can we identify in the interactions between a system and its users, the human behaviours that can harm this system’s security? 3. How can we identify the factors that foster human behaviours that are harmful to a system’s security? A review of works that aim at bringing social sciences findings into security analysis reveals that there is no unified way to do it. Identifying the points where users can harm a system’s security, and clarifying what factors can foster an insecure behaviour is a complex matter. Hypotheses can arise about the usability of the system, aspects pertaining to the user or the organisational context but there is no way to find and test them all. Further, there is currently no way to systematically integrate the results regarding hypotheses we tested in a security analysis. Thus, we identify two objectives related to these methodological challenges that this thesis aims at fulfilling in its contributions: 1. What form should a framework that intends to identify harmful behaviours for security, and to investigate the factors that foster their occurrence take? 2. What form should a semi-automatic, or tool-assisted methodology for the security analysis of socio-technical systems take? The thesis provides partial answers to the questions. First it defines a methodological framework called STEAL that provides a common ground for an interdisciplinary approach to security analysis. STEAL supports the interaction between computer scientists and social scientists by providing a common reference model to describe a system with its human and non-human components, potential attacks and defences, and the surrounding context. We validate STEAL in a two experimental studies, showing the role of the context and graphical cues in Wi-Fi networks’ security. Then the thesis complements STEAL with a Root Cause Analysis (RCA) methodology for security inspired from the ones used in safety. This methodology, called S·CREAM aims at being more systematic than the research methods that can be used with STEAL (surveys for instance) and at providing reusable findings for analysing security. To do so, S·CREAM provides a retrospective analysis to identify the factors that can explain the success of past attacks and a methodology to compile these factors in a form that allows for the consideration of their potential effects on a system’s security, given an attacker Threat Model. The thesis also illustrates how we developed a tool—the S·CREAM assistant— that supports the methodology with an extensible knowledge base and computer-supported reasoning

Phishing Detection: Analysis of Visual Similarity Based Approaches

Phishing is one of the major problems faced by cyber-world and leads to financial losses for both industries and individuals. Detection of phishing attack with high accuracy has always been a challenging issue. At present, visual similarities based techniques are very useful for detecting phishing websites efficiently. Phishing website looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. Visual similarity based phishing detection techniques utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet (CSS), image, and so forth, to make the decision. These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. This paper presents a comprehensive analysis of phishing attacks, their exploitation, some of the recent visual similarity based approaches for phishing detection, and its comparative study. Our survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishing attacks efficiently using visual similarity based approaches

An investigation into the prevalence and growth of phishing attacks against South African financial targets

Phishing in the electronic communications medium is the act of sending unsolicited email messages with the intention of masquerading as a reputed business. The objective is to deceive the recipient into divulging personal and sensitive information such as bank account details, credit card numbers and passwords. Attacks against financial services are the most common types of targets for scammers. Phishing attacks in South Africa have cost businesses and consumers substantial amounts of financial loss. This research investigated existing literature to understand the basic concepts of email, phishing, spam and how these fit together. The research also looks into the increasing growth of phishing worldwide and in particular against South African targets. A quantitative study is performed and reported on; this involves the study and analysis of phishing statistics in a data set provided by the South African Anti-Phishing Working Group. The data set contains phishing URL information, country code where the site has been hosted, targeted company name, IP address information and timestamp of the phishing site. The data set contains 161 different phishing targets. The research primarily focuses on the trend in phishing attacks against six South African based financial institutions, but also correlates this with the overall global trend using statistical analysis. The results from the study of the data set are compared to existing statistics and literature regarding the prevalence and growth of phishing in South Africa. The question that this research answers is whether or not the prevalence and growth of phishing in South Africa correlates with the global trend in phishing attacks. The findings indicate that certain correlations exist between some of the South African phishing targets and global phishing trends

On how zero-knowledge proof blockchain mixers improve, and worsen user privacy

One of the most prominent and widely-used blockchain privacy solutions are zero-knowledge proof (ZKP) mixers operating on top of smart contract-enabled blockchains. ZKP mixers typically advertise their level of privacy through a so-called anonymity set size, similar to k-anonymity, where a user hides among a set of $k$ other users. In reality, however, these anonymity set claims are mostly inaccurate, as we find through empirical measurements of the currently most active ZKP mixers. We propose five heuristics that, in combination, can increase the probability that an adversary links a withdrawer to the correct depositor on average by 51.94% (108.63%) on the most popular Ethereum (ETH) and Binance Smart Chain (BSC) mixer, respectively. Our empirical evidence is hence also the first to suggest a differing privacy-predilection of users on ETH and BSC. We further identify 105 Decentralized Finance (DeFi) attackers leveraging ZKP mixers as the initial funds and to deposit attack revenue (e.g., from phishing scams, hacking centralized exchanges, and blockchain project attacks). State-of-the-art mixers are moreover tightly intertwined with the growing DeFi ecosystem by offering ``anonymity mining'' (AM) incentives, i.e., mixer users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not always contribute to improving the quality of an anonymity set size of a mixer, because AM tends to attract privacy-ignorant users naively reusing addresses