Deep Anomaly Detection on Attributed Networks: A Comprehensive Review

Cybersecurity is a critical concern in today’s digital age. With the increasing sophistication of attacks, traditional security measures are no longer sufficient. Machine learning (ML) has emerged as a promising solution to detect intrusion attempts and anomalies in network communication. This article provides an overview of ML applications in cybersecurity, highlighting their effectiveness and challenges.
The survey covers various ML models, including traditional ones (such as decision trees and support vector machines) and deep learning (DL) models (like convolutional neural networks and recurrent neural networks). These models have been used to analyze cybersecurity-related data, including network communication, to detect attacks and anomalies.
One of the key challenges in ML for cybersecurity is the lack of labeled data, which hinders model training. To overcome this challenge, researchers have employed unsupervised learning techniques, such as k-means clustering and distance-based anomaly detection.
Another significant issue is the complexity of ML models themselves. As a result, interpreting the results and understanding the reasoning behind the predictions can be challenging. To address this problem, visualization tools and explainable AI (XAI) techniques have been developed to provide insights into the decision-making process of ML models.
The article also discusses future directions in ML for cybersecurity, including the integration of domain knowledge and the development of hybrid models that combine different ML approaches. By leveraging these advancements, cybersecurity can be significantly enhanced, providing a more robust defense against emerging threats.
In summary, this article provides an in-depth analysis of the applications and challenges of ML in cybersecurity. By understanding the strengths and limitations of these techniques, researchers and practitioners can work towards developing more effective and efficient security measures to protect our digital assets.