Advancements in Cybersecurity and Artificial Intelligence

The field of cybersecurity is rapidly evolving, with a growing emphasis on the development of adaptive and sophisticated intrusion detection systems. Recent research has focused on leveraging deep learning techniques to improve the accuracy and efficiency of these systems, with notable success. The use of risk profiling and selective training strategies has also shown promise in enhancing the resilience of deep neural networks against evasion attacks. Furthermore, the integration of federated semi-supervised learning and contrastive learning has led to the development of more effective and robust intrusion detection frameworks. Additionally, advancements in reinforcement learning have enabled the creation of adaptive security policy management systems, capable of dynamically adjusting to evolving threats and minimizing resource impact. Notable papers in this area include:

  • A paper on risk profiling-based defenses against evasion attacks, which proposed a novel risk profiling framework to selectively train static defenses, resulting in a recall increase of up to 27.5% with minimal impact on precision.
  • A paper on a contrastive federated semi-supervised learning intrusion detection framework, which demonstrated superior performance and robustness compared to existing federated semi-supervised and fully supervised methods.
  • A paper on adaptive security policy management using reinforcement learning, which achieved higher intrusion detection rates and substantially reduced incident detection and response times compared to static policies.

Sources

Intrusion Detection System Using Deep Learning for Network Security

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs

A Contrastive Federated Semi-Supervised Learning Intrusion Detection Framework for Internet of Robotic Things

Towards the Three-Phase Dynamics of Generalization Power of a DNN

Intelligent Load Balancing Systems using Reinforcement Learning System

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks

Towards Understanding Deep Learning Model in Image Recognition via Coverage Test

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems

Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning

Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems

Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification

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