A Deception and Continuous Training Approach for Web Attack Detection using Cyber Traps and MLOps

RESEARCH CREW

7:42 22/06/2023

With the growth and expansion of the internet, web attacks have become more powerful and pose a significant threat in the cyber world. In response to this, this paper presents a deceptive approach for gathering malicious behavior to understand the strategies used by web attackers. The harmful requests collected through cyber traps or honeypots are analyzed and used to train machine learning (ML) models for web attack detection. Additionally, we implement an ML operations (MLOps) pipeline to automate the continuous training and deployment of these ML models in defensive systems. This pipeline trains the production model with newly collected data by using predefined triggers. Our experiments on two datasets, including Fwaf and our own, demonstrate that a proactive and continuous approach to tracking adversary behavior can effectively detect zero-day attacks, such as CVE-2022-26134 in web application servers.

TIN LIÊN QUAN

An Approach of Adaptive Cyber Deception for Active Cyber-attack Defense Method based on Deep Reinforcement Learning

The diverse landscape of network models, including Software-Defined Networking (SDN), Cloud Computing (C2), and Internet of Things (IoT), is evolving to meet the demands of flexibility and performance. However, these environments face numerous security challenges due to cyber-attack complexity. Traditional defense mechanisms are no longer effective against modern attacks. Therefore,...

Multimodal Deep Learning Feedback for Generating Evasive Malware Samples against Malware Detector

As data driven-based Windows malware detectors become increasingly prevalent, the need for robust evaluation and enhancement of adversarial malware generation techniques also becomes imperative, as malicious actors will adapt and enhance their malware to evade detection. There are numerous works that introduce new techniques or enhancements for adversarial malware. One...

Medichain: A multi-chain based system for managing prescription medications using Zero Knowledge Proofs

The abuse of prescription medications has become a severe public health crisis fueled by limited coordination and oversight across healthcare systems. Current frameworks lack interoperability between doctors, pharmacies, and regulators, enabling abusive practices like doctor shopping and pharmacy hopping. To address these issues, this research proposes Medichain, a novel multichain-based...