Analysis and Prevention of AI-Based Phishing Email Attacks

被引:0
|
作者
Eze, Chibuike Samuel [1 ]
Shamir, Lior [1 ]
机构
[1] Kansas State Univ, Dept Comp Sci, Manhattan, KS 66506 USA
关键词
phishing; cybersecurity; SPAM;
D O I
10.3390/electronics13101839
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.
引用
下载
收藏
页数:13
相关论文
共 50 条
  • [21] Optimizing Personalized Email Filtering Thresholds to Mitigate Sequential Spear Phishing Attacks
    Zhao, Mengchen
    An, Bo
    Kiekintveld, Christopher
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 658 - 664
  • [22] AI-based System for the Detection and Prevention of COVID-19
    Chokri, Sofien
    Ben Daoud, Wided
    Hanini, Wasma
    Mahfoudhi, Sami
    Makhlouf, Amel
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (01) : 582 - 591
  • [23] Baiting the Hook: Exploring the Interaction of Personality and Persuasion Tactics in Email Phishing Attacks
    Lawson, Patrick A.
    Crowson, Aaron D.
    Mayhorn, Christopher B.
    PROCEEDINGS OF THE 20TH CONGRESS OF THE INTERNATIONAL ERGONOMICS ASSOCIATION (IEA 2018), VOL V: HUMAN SIMULATION AND VIRTUAL ENVIRONMENTS, WORK WITH COMPUTING SYSTEMS (WWCS), PROCESS CONTROL, 2019, 822 : 401 - 406
  • [24] AI-Based Cyberbullying Prevention in 5G Networks
    Ramezanian, Sara
    Meskanen, Tommi
    Niemi, Valtteri
    INTERNATIONAL JOURNAL OF EMBEDDED AND REAL-TIME COMMUNICATION SYSTEMS (IJERTCS), 2020, 11 (04): : 1 - 20
  • [25] AI-based video analysis for traffic monitoring
    Tung, Bui Son
    Ngoc, Phung The
    Thanh, Do Duy
    Thinh, Nguyen Hong
    PROCEEDINGS OF 2022 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2022, : 2035 - 2040
  • [26] AI-Based Kinematic Analysis for Track Athletes
    Habibi, Mostafa
    Nourani, Mehrdad
    Nourani, Mohammad
    2024 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING, SMARTCOMP 2024, 2024, : 338 - 343
  • [27] MPMPA: A Mitigation and Prevention Model for Social Engineering Based Phishing attacks on Facebook
    Jamil, Abid
    Asif, Kashif
    Ghulam, Zikra
    Nazir, Muhammad Kashif
    Alam, Syed Mudassar
    Ashraf, Rehan
    2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 5040 - 5048
  • [28] Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection
    Ali, Shamshair
    Rehman, Saif Ur
    Imran, Azhar
    Adeem, Ghazif
    Iqbal, Zafar
    Kim, Ki-Il
    ELECTRONICS, 2022, 11 (23)
  • [29] Defending AI-Based Automatic Modulation Recognition Models Against Adversarial Attacks
    Tang, Haolin
    Catak, Ferhat Ozgur
    Kuzlu, Murat
    Catak, Evren
    Zhao, Yanxiao
    IEEE ACCESS, 2023, 11 : 76629 - 76637
  • [30] Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning
    Alzu’bi, Ahmad
    Albashayreh, Amjad
    Abuarqoub, Abdelrahman
    Alfawair, Mai A.M.
    Computers, Materials and Continua, 2024, 80 (03): : 3785 - 3802