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Peng, Tianrui, Harris, Ian, Sawa, Yuki.  2018.  Detecting Phishing Attacks Using Natural Language Processing and Machine Learning. 2018 IEEE 12th International Conference on Semantic Computing (ICSC). :300–301.
Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.
Bagui, Sikha, Nandi, Debarghya, Bagui, Subhash, White, Robert Jamie.  2019.  Classifying Phishing Email Using Machine Learning and Deep Learning. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—2.

In this work, we applied deep semantic analysis, and machine learning and deep learning techniques, to capture inherent characteristics of email text, and classify emails as phishing or non -phishing.

Baykara, Muhammet, Gürel, Zahit Ziya.  2018.  Detection of Phishing Attacks. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1-5.

Phishing is a form of cybercrime where an attacker imitates a real person / institution by promoting them as an official person or entity through e-mail or other communication mediums. In this type of cyber attack, the attacker sends malicious links or attachments through phishing e-mails that can perform various functions, including capturing the login credentials or account information of the victim. These e-mails harm victims because of money loss and identity theft. In this study, a software called "Anti Phishing Simulator'' was developed, giving information about the detection problem of phishing and how to detect phishing emails. With this software, phishing and spam mails are detected by examining mail contents. Classification of spam words added to the database by Bayesian algorithm is provided.

Qbeitah, M. A., Aldwairi, M..  2018.  Dynamic malware analysis of phishing emails. 2018 9th International Conference on Information and Communication Systems (ICICS). :18–24.

Malicious software or malware is one of the most significant dangers facing the Internet today. In the fight against malware, users depend on anti-malware and anti-virus products to proactively detect threats before damage is done. Those products rely on static signatures obtained through malware analysis. Unfortunately, malware authors are always one step ahead in avoiding detection. This research deals with dynamic malware analysis, which emphasizes on: how the malware will behave after execution, what changes to the operating system, registry and network communication take place. Dynamic analysis opens up the doors for automatic generation of anomaly and active signatures based on the new malware's behavior. The research includes a design of honeypot to capture new malware and a complete dynamic analysis laboratory setting. We propose a standard analysis methodology by preparing the analysis tools, then running the malicious samples in a controlled environment to investigate their behavior. We analyze 173 recent Phishing emails and 45 SPIM messages in search for potentially new malwares, we present two malware samples and their comprehensive dynamic analysis.

Lakhita, Yadav, S., Bohra, B., Pooja.  2015.  A review on recent phishing attacks in Internet. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). :1312–1315.

The development of internet comes with the other domain that is cyber-crime. The record and intelligently can be exposed to a user of illegal activity so that it has become important to make the technology reliable. Phishing techniques include domain of email messages. Phishing emails have hosted such a phishing website, where a click on the URL or the malware code as executing some actions to perform is socially engineered messages. Lexically analyzing the URLs can enhance the performance and help to differentiate between the original email and the phishing URL. As assessed in this study, in addition to textual analysis of phishing URL, email classification is successful and results in a highly precise anti phishing.