Enhancing Phishing Detection with KnowPhish

Keskeiset käsitteet

The authors propose KnowPhish, a large-scale multimodal brand knowledge base, and the KnowPhish Detector (KPD) to improve phishing detection by addressing limitations in existing RBPDs.

Tiivistelmä

KnowPhish introduces a novel approach to enhance phishing detection by combining logo-based and text-based methods. The study demonstrates significant improvements in effectiveness and efficiency compared to state-of-the-art baselines. The field study on local webpages validates the robustness and accuracy of KnowPhish and KPD in real-world scenarios.

Phishing attacks pose a significant threat to individuals and businesses, necessitating advanced automated detection methods. Existing RBPDs face limitations due to manual brand knowledge bases, leading to false negatives. KnowPhish addresses these issues by automating knowledge collection and introducing a multimodal approach for improved detection.

The study highlights the importance of incorporating text-based analysis alongside traditional logo-based methods for comprehensive phishing detection. By leveraging a large-scale brand knowledge base, KnowPhish significantly enhances the performance of RBPDs, particularly in identifying logo-less phishing webpages.

In a field study on local web traffic, KnowPhish outperforms commercial detectors like URLScan, demonstrating its effectiveness in real-world settings. The results showcase the potential of multimodal approaches like KPD for accurate and efficient phishing detection across diverse contexts.

Tilastot

20k brands contained in KnowPhish
10k webpages from SG-SCAN dataset
$1.026 trillion lost in scams in 2023

Lainaukset

"KnowPhish introduces a novel approach to enhance phishing detection."
"The study demonstrates significant improvements in effectiveness and efficiency compared to state-of-the-art baselines."

Tärkeimmät oivallukset

KnowPhish

by Yuexin Li,Ch... klo arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.02253.pdf

Syvällisempiä Kysymyksiä

How can RBPDs be further improved beyond the capabilities of KnowPhish?

RBPDs can be enhanced by incorporating more advanced AI techniques, such as deep learning models like Transformers or GPT (Generative Pre-trained Transformer) models. These models have shown significant success in natural language processing tasks and could potentially improve the text-based analysis aspect of RBPDs. Additionally, integrating real-time data sources for brand information updates and leveraging anomaly detection algorithms to identify new phishing patterns could further enhance RBPD performance. Furthermore, exploring multi-modal approaches that combine image analysis with text understanding could provide a more comprehensive detection capability.

What are the potential implications of relying solely on image-based phishing detection methods?

Relying solely on image-based phishing detection methods may lead to limitations in detecting logo-less phishing webpages or variations in logos used by attackers. This approach might result in higher false negative rates when malicious actors use different visual elements or no logos at all to deceive users. Moreover, maintaining an extensive database of accurate logo images for every brand can be challenging and time-consuming, making it difficult to keep up with evolving phishing tactics. Over-reliance on image-based methods may also overlook crucial textual cues present in HTML content that could indicate fraudulent intent.

How might advancements in AI impact the future landscape of cybersecurity?

Advancements in AI are poised to revolutionize cybersecurity by enabling more proactive threat detection and response mechanisms. AI-powered tools can analyze vast amounts of data quickly and accurately, helping organizations identify anomalies indicative of cyber threats before they escalate into full-blown attacks. Machine learning algorithms can adapt and learn from new attack patterns, enhancing their ability to detect sophisticated threats like zero-day exploits or polymorphic malware. Additionally, AI technologies like neural networks and reinforcement learning hold promise for developing autonomous security systems capable of self-healing and self-optimizing defenses against cyber threats.