Advanced Adversarial Attacks on Phishing Detection Models: Identification and Mitigation

By: Mosiur Rahaman, International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan

Abstract :

Phishing detection models are now important parts of keeping your information safe from online threats. Unfortunately, these models can be broken by hostile attacks, which make them less useful. Cybersecurity is at great risk because bad guys use complex methods to make fake inputs that get past security measures. This research report investigates what these weaknesses are and how they might affect current phishing detection models. It also suggests strong ways to fix these problems and make the models more resilient. Through a few experiments, I found major problems with relying on features, having enough training data, and using static detection rules. As successful countermeasures, we recommend more advanced training methods, better models, and real-time monitoring. Results make it clear that hacking detection systems need to keep changing and getting better to keep up with new threats.

Keywords: Phishing detection, Mitigation, Machine Learning

Introduction:

Cybersecurity threats like phishing are still very common. These attacks use social engineering to trick people into giving up private information. Machine learning methods used in phishing detection models are very important for finding and stopping these threats. However, attackers have come up with complex ways to get around these models, making them less useful [1]. This article investigates advanced attacks that are aimed at phishing detection models and offers ways to make these systems stronger.

Problems with Phishing Detection Models:

Attacks by adversaries:

Attempted adversarial attacks change input data in small ways that trick machine learning models. Within the framework of phishing detection, hackers create emails or URLs that look safe to a computer model but dangerous to a person. Such changes are usually not noticeable, which makes them very hard to find and stop shown in figure 1. Multiple types of hostile strikes can be used against phishing detection models, including:

1. Evasion Attacks: Evasion attacks involve changing the email content to avoid being caught while still having a bad intention [2].
2. Poisoning Attacks: To stop a model from learning properly, poisoning attacks add bad samples to the training data [3].
3. Transfer Attacks: Using flaws in one model to attack another model that has the same design or training data [4].

Possible weaknesses:

An experiment using different adversarial attack methods helped us find possible flaws in hacking detection models. Our research shows the following serious weaknesses shown in figure 2:

Over-reliance on Specific Features: It’s easier for evasion attacks to happen on models that depend too much on certain traits, like keywords or URL structures.

Missing or Insufficient Training Data: Models that were taught on missing or incomplete data are not as resistant to changes made by adversaries.

Automatic Detection Rules: Dynamic, adaptive adversarial methods are better at getting around models that use static rules or thresholds.

Prevention Methods:

Using advanced training methods is necessary to make phishing detection models more reliable:

• For better model protection against attacks, adversarial cases are added to the training dataset.
• To cover a wider range of situations, data augmentation involves adding more phishing and real examples to the training dataset.

Model Improvements:

To reduce vulnerabilities, you can improve the model design and feature extraction processes:

Using Shared Methods: Separating an adversarial attack on a single model by using various models with different architectures.

Feature Randomization: By adding randomness to feature extraction, attackers will have a harder time predicting and taking advantage of model behavior.

Real-time updates and monitoring

Implementing real-time monitoring and adaptive methods can make phishing detection models much more reliable.

Dynamic Thresholds: To fight adaptive adversarial methods, changing detection thresholds based on continuous analysis of incoming data.

Continuous Learning: Allowing models to gather new information all the time, including known adversarial samples, so they can keep up with changing attack tactics.

Conclusion:

Phishing detection models are important parts of modern defense because they protect users from adversaries who want to take advantage of them. But because adversarial attacks are getting smarter, model creation and defense need to be proactive and flexible. This study has found some major problems with the way phishing is currently detected. These include relying too much on certain features, not having enough training data, and using static detection rules without thinking about what else could be happening. Due to these flaws, models can be easily attacked by escape, poisoning, and transfer, all of which can greatly reduce their usefulness.

To fight these threats, we’ve come up with a multifaceted plan that includes strong training methods, improved models, and real-time tracking. Adversarial training and adding more data are important for getting models ready to spot and reject adversarial inputs. Adding ensemble methods and feature randomization to model design makes the detection systems even better at stopping possible breaches. Using dynamic thresholds and learning mechanisms that keep learning makes sure that models can change instantly to the constantly changing world of phishing attacks.

References:

1. A. Mughaid, S. AlZu’bi, A. Hnaif, S. Taamneh, A. Alnajjar, and E. A. Elsoud, “An intelligent cyber security phishing detection system using deep learning techniques,” Cluster Comput, vol. 25, no. 6, pp. 3819–3828, Dec. 2022, doi: 10.1007/s10586-022-03604-4.
2. Ö. Aslan, S. S. Aktuğ, M. Ozkan-Okay, A. A. Yilmaz, and E. Akin, “A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions,” Electronics, vol. 12, no. 6, Art. no. 6, Jan. 2023, doi: 10.3390/electronics12061333.
3. S. Wang, K. Fan, K. Zhang, H. Li, and Y. Yang, “Data complexity-based batch sanitization method against poison in distributed learning,” Digital Communications and Networks, vol. 10, no. 2, pp. 416–428, Apr. 2024, doi: 10.1016/j.dcan.2022.12.001.
4. Z. Yin, Y. Zhuo, and Z. Ge, “Transfer adversarial attacks across industrial intelligent systems,” Reliability Engineering & System Safety, vol. 237, p. 109299, Sep. 2023, doi: 10.1016/j.ress.2023.109299.
5. Jain, A. K., & Gupta, B. B. (2018). Two-level authentication approach to protect from phishing attacks in real time. Journal of Ambient Intelligence and Humanized Computing, 9(6), 1783-1796.
6. Almomani, A., et al. Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24.
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Cite As

Rahaman M. (2024) Advanced Adversarial Attacks on Phishing Detection Models: Identification and Mitigation, Insights2Techinfo, pp.1

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