Learning To Detect Phishing Emails

There are an increasing number of emails purporting to be from a trusted entity that attempt to deceive users into providing account or identity information, commonly known as phishing emails. Traditional spam filters are not adequately detecting these undesirable emails, and this causes problems for both consumers and businesses wishing to do business online. From a learning perspective, this is a challenging problem. At first glance, the problem appears to be a simple text classification problem, but the classification is confounded by the fact that the class of phishing emails is nearly identical to the class of real emails. We propose a new method for detecting these malicious emails called PILFER. By incorporating features specifically designed to highlight the deceptive methods used to fool users, we are able to accurately classify over 92% of phishing emails, while maintaining a false positive rate on the order of 0.1%. These results are obtained on a dataset of approximately 860 phishing emails and 6950 non-phishing emails. The accuracy of PILFER on this dataset is significantly better than that of SpamAssassin, a widely-used spam filter.

Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats. Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks Help your business or organization avoid costly damage from phishing sources Gain insight into machine-learning strategies for facing a variety of information security threats

This book constitutes the refereed proceedings of the 17th European Symposium on Computer Security, ESORICS 2012, held in Pisa, Italy, in September 2012. The 50 papers included in the book were carefully reviewed and selected from 248 papers. The articles are organized in topical sections on security and data protection in real systems; formal models for cryptography and access control; security and privacy in mobile and wireless networks; counteracting man-in-the-middle attacks; network security; users privacy and anonymity; location privacy; voting protocols and anonymous communication; private computation in cloud systems; formal security models; identity based encryption and group signature; authentication; encryption key and password security; malware and phishing; and software security.

Masterarbeit aus dem Jahr 2020 im Fachbereich Mathematik - Sonstiges, Note: 1,3, Universität Ulm, Sprache: Deutsch, Abstract: Phishing is a form of identity theft that combines social engineering techniques and sophisticated attack vectors to fraudulently gain confidential information of unsuspecting consumers. To prevent successful phishing attacks, there are several approaches to detect and block phishing emails. In this work, we apply a number of modern transformer based machine learning methods for phishing email detection. Typically, phishing messages imitate trustworthy sources and request information via some form of electronic communication. The most frequent attack route is via email where phishers often try to persuade the email recipients to perform an action. This action may involve revealing confidential information (e.g. passwords) or inadvertently providing access to their computers or networks (e.g. through the installation of malicious software).

Softcomputing techniques play a vital role in the industry. This book presents several important papers presented by some of the well-known scientists from all over the globe. The main techniques of soft computing presented include ant-colony optimization, artificial immune systems, artificial neural networks, Bayesian models. The book includes various examples and application domains such as bioinformatics, detection of phishing attacks, and fault detection of motors.

The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning algorithms to detect phishing attack did increase the predictive accuracy of these learning algorithms. The same research also recommended further investigation of the impact of including an expanded set of email headers on the predictive accuracy of machine learning algorithms. In addition, research has shown that the cost of misclassifying legitimate emails as phishing attacks--false positives--was far higher than that of misclassifying phishing emails as legitimate--false negatives, while the opposite was true in the case of fraud detection. Consequently, they recommended that cost sensitive measures be taken in order to further improve the weighted predictive accuracy of machine learning algorithms. Motivated by the potentially high impact of the inclusion of email headers on the predictive accuracy of machines learning algorithms and the significance of enabling cost-sensitive measures as part of the learning process, the goal of this research was to quantify the impact of including an extended set of email headers and to investigate the impact of imposing penalty as part of the learning process on the number of false positives. It was believed that if email headers were included and cost-sensitive measures were taken as part of the learning process, than the overall weighted, predictive accuracy of the machine learning algorithm would be improved. The results showed that adding email headers as features did improve the overall predictive accuracy of machine learning algorithms and that cost-sensitive measure taken as part of the learning process did result in lower false positives.

This book constitutes the refereed proceedings of the Fourth VLDB 2007 International Workshop on Secure Data Management, SDM 2007, held in Vienna, Austria, September 23-24, 2007 in conjunction with VLDB 2007. The 11 revised full papers presented were carefully reviewed and selected from 29 submissions. The papers are organized in topical sections on Access Control, Database Security, Privacy Protection and Positon Papers.

Learn how to detect, analyze, and respond to phishing emails, the top infection vector used by cybercriminals. The repeatable process described in this book has been cultivated and tested in real-life incidents and validated across multiple threat landscapes and environments. Every organization and individual with an email account is susceptible to deceptive emails sent by attackers with nefarious intentions. This activity, known as phishing, involves an attacker attempting to lure individuals into providing sensitive information or performing a predetermined action. Attacks vary in sophistication, but the core skills and process to detect, analyze, and respond to a suspicious message does not change. Attackers have preyed on victims with convincing and not-so-convincing phishing emails to gain initial footholds into networks around the world for over 30 years. This attack method has been rapidly growing in popularity and continues to be the number one method that organizations and individuals struggle to defend against. Regardless of what any vendor or organization will tell you, no infallible tool exists to eliminate this threat completely. This book teaches you how to analyze suspicious messages using free tools and resources. You will understand the basics of email, tactics used by attackers, and a repeatable process to systematically analyze messages and respond to suspicious activity. You Will Learn How to: Safely save email messages as attachments for analysis Identify what information is in an email header Review header information and extract key indicators or patterns used for detection Identify signs of a suspicious or malicious email message Detect the tactics that attackers use in phishing emails Safely examine email links and attachments Use a variety of free and simple tools to analyze email messages.

This two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011. The second volume includes 37 papers from one symposium and three workshops held together with ICA3PP 2011 main conference. These are 16 papers from the 2011 International Symposium on Advances of Distributed Computing and Networking (ADCN 2011), 10 papers of the 4th IEEE International Workshop on Internet and Distributed Computing Systems (IDCS 2011), 7 papers belonging to the III International Workshop on Multicore and Multithreaded Architectures and Algorithms (M2A2 2011), as well as 4 papers of the 1st IEEE International Workshop on Parallel Architectures for Bioinformatics Systems (HardBio 2011).