By: Gonipalli Bharath, Vel Tech University, Chennai, India,& International Center for AI and
Abstract:
Now-a-days phishing attacks merely used to be an art of simple email-based scams, these have now emerged into multi-vector threats for individuals, organizations, and even governments around the globe. And this article focuses on how to detect and combat phishing, with much emphasis on malicious activities . Reviewing the current literature, presenting methodologies, and proposing a flowchart of detecting and mitigating the attack is intended to provide insight into effective strategies against phishing. It concludes by highlighting a few key technological innovations and best practices in phishing defense.
Introduction:
The most common types of cyberattacks are those that use social engineering to deceive users into revealing confidential information. It can be as simple as receiving forged e-mails that appear to originate from trusted sources or more sophisticated methods, such as spear-phishing, which involves targeting specific individuals or organizations. While cybersecurity has evolved over time, phishing continues to be a current threat because it is ever-changing. While cybercriminals are getting more sophisticated, traditional mechanisms of defense turn out to be less effective. Therefore, modern technologies and strategies should be employed in order to effectively detect and combat phishing attacks.
Figure(a)[[1]]
Literature Review:
Literature illustrates that there are significant developments related to phishing detection techniques, mostly based on machine learning, NLP, and AI. Phishing scams, which typically involve false messaging, malicious websites, or misleading emails, are one of the main cybersecurity threats that take advantage of human weaknesses to steal confidential information [[2]].
This literature review outlines current developments and approaches in the detection and mitigation of phishing attacks. Machine learning and AI-powered models, namely NLP(A machine learning technique called natural language processing (NLP) enables computers to understand, interpret, and modify human language.), neural networks, and decision trees, have successfully identified phishing patterns in emails, URLs, and websites. Blacklisting, whitelisting, and browser extensions are some of the heuristic techniques in wide use for real-time protection [[3]].
User education and awareness programs also play an important role in reducing the likelihood of such attacks. Recent works have pointed out the possibility of AI-driven real-time monitoring systems to predict and proactively block phishing attempts [[4]]. However, the rapidly changing tactics of cybercriminals raise significant challenges, and continuous innovation with adaptive models is required to respond effectively to these threats. Current research focuses on enhancing the scalability, accuracy, and response time of phishing detection systems.
Methodology:
The methodology for detection and combating phishing would involve the following stages:
- Data Collection:
A wide dataset of phishing and legitimate emails, websites, and network traffic would be collected.
- Feature Extraction:
Key features extracted from the analyzed emails would include sender information, email content, metadata, and URLs.
- Model Training:
The machine learning models, such as SVM, Random Forest, and Neural Networks, together with deep learning algorithms, would be trained on the dataset.
- Phishing Detection:
The solution performs real-time scanning of email and web traffic for detecting anomalies or phishing attempts.
- Response and Mitigation:
Once phishing is detected, automated systems block adverse emails or websites, trigger alerts for the users, and perform corrective measures.
Flow chart representation of Phishing Detection:
Conclusion:
Phishing is still a very critical kind of cybersecurity threat, certain recent developments in machine learning, AI, and blockchain have enhanced the attempts of detection and prevention considerably. By integrating these technologies, the phishing detection has gone from efficient and quick to almost real-time responses that minimize the potential impact. But as the attackers continuously improve their tactics, continuous research and adaptation of defensive strategies will also be needed to counter phishing.
References:
- Alkhalil, Zainab, Chaminda Hewage, Liqaa Nawaf, and Imtiaz Khan. “Phishing Attacks: A Recent Comprehensive Study and a New Anatomy.” Frontiers in Computer Science 3 (March 9, 2021). https://doi.org/10.3389/fcomp.2021.563060.
- Labs, Keepnet. “What Is Baiting: Types, Examples and Protection – Keepnet.” Keepnet Labs. Accessed January 6, 2025. https://keepnetlabs.com/blog/what-is-baiting-in-cyber-security.
- Maurya, Swati, Harpreet Singh, and Anurag Jain. “Browser Extension Based Hybrid Anti-Phishing Framework Using Feature Selection.” International Journal of Advanced Computer Science and Applications 10, no. 11 (2019). https://doi.org/10.14569/IJACSA.2019.0101178.
- Nwoye, Chukwujekwu Charles, and Stephen Nwagwughiagwu. “AI-DRIVEN ANOMALY DETECTION FOR PROACTIVE CYBERSECURITY AND DATA BREACH PREVENTION” 08, no. 11 (n.d.).
- Navaneeth J. (2024) Harnessing Deep Learning for Advanced Phishing Detection, Insights2Techinfo, pp.1
- Xu, M., Peng, J., Gupta, B. B., Kang, J., Xiong, Z., Li, Z., & Abd El-Latif, A. A. (2021). Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems. IEEE Internet of Things Journal, 9(22), 22095-22108.
- Sedik, A., Hammad, M., Abd El-Samie, F. E., Gupta, B. B., & Abd El-Latif, A. A. (2022). Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Computing and Applications, 1-18.
Cite As
Bharath G. (2025) The Digital Bait Unveiled: Insights into Detecting and Combatting Phishing Attacks, Insights2Techinfo, pp.1