Cybersecurity Threats and Defenses

The field of cybersecurity is rapidly evolving, with a growing focus on addressing the unique challenges of various environments, such as maritime systems. Research is shifting towards understanding the human-centric issues and systemic problems that contribute to cybersecurity threats. Innovative approaches, including the application of natural language processing and machine learning techniques, are being explored to enhance malware classification and detection. Furthermore, there is a growing emphasis on improving the robustness of PDF malware classifiers and developing more effective methods for malicious URL detection. However, some studies suggest that traditional methods, such as anti-phishing training, may be ineffective, highlighting the need for alternative approaches. Noteworthy papers include:

  • A study on maritime cybersecurity, which investigated how maritime system operators perceive and navigate cybersecurity challenges, revealing systemic and human-centric issues.
  • A paper on malware classification, which proposed a novel approach using NLP-based n-gram analysis and machine learning techniques, achieving significantly improved accuracy.
  • A research on PDF malware analysis, which introduced a new approach using intermediate representation and language models, demonstrating strong adversarial robustness.
  • A study on malicious URL detection, which presented a detection framework using bidirectional fusion of HTML subgraphs and multi-scale convolutional BERT, achieving significant improvements over state-of-the-art baselines.

Sources

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy

Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model

WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT

Anti-Phishing Training Does Not Work: A Large-Scale Empirical Assessment of Multi-Modal Training Grounded in the NIST Phish Scale

Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks

Measuring Modern Phishing Tactics: A Quantitative Study of Body Obfuscation Prevalence, Co-occurrence, and Filter Impact

PhishKey: A Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction

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