[go: up one dir, main page]

Duraz et al., 2023 - Google Patents

Explainability-based metrics to help cyber operators find and correct misclassified cyberattacks

Duraz et al., 2023

View PDF
Document ID
12752491303627977590
Author
Duraz R
Espes D
Francq J
Vaton S
Publication year
Publication venue
Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking

External Links

Snippet

Machine Learning (ML)-based Intrusion Detection Systems (IDS) have shown promising performance. However, in a human-centered context where they are used alongside human operators, there is often a need to understand the reasons of a particular decision …
Continue reading at imt-atlantique.hal.science (PDF) (other versions)

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416—Event detection, e.g. attack signature detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • G06F11/0775—Content or structure details of the error report, e.g. specific table structure, specific error fields
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441—Countermeasures against malicious traffic

Similar Documents

Publication Publication Date Title
Haq et al. Insider threat detection based on NLP word embedding and machine learning
Du et al. Digital Forensics as Advanced Ransomware Pre‐Attack Detection Algorithm for Endpoint Data Protection
Liu et al. Your model trains on my data? Protecting intellectual property of training data via membership fingerprint authentication
CN119254489B (en) Information network security self-defense method and system based on trusted computing
Avalappampatty Sivasamy et al. A dynamic intrusion detection system based on multivariate Hotelling’s T2 statistics approach for network environments
Lin et al. Collaborative alert ranking for anomaly detection
Almarshad et al. Detection of android malware using machine learning and siamese shot learning technique for security
Duraz et al. Explainability-based metrics to help cyber operators find and correct misclassified cyberattacks
Bhusal et al. Sok: Modeling explainability in security analytics for interpretability, trustworthiness, and usability
Mohammadi et al. Anomaly-based Web Attack Detection: The Application of Deep Neural Network Seq2Seq With Attention Mechanism.
Brindavathi et al. An Analysis of AI-based SQL Injection (SQLi) Attack Detection
CN119760756A (en) Data security protection system and fragmentation encryption method for energy operation platform
Moradpoor et al. The threat of adversarial attacks against machine learning-based anomaly detection approach in a clean water treatment system
Jia et al. Cluster Detection Method of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network.
CN119109614A (en) Access control method and system for data security protection
Seada et al. A Machine Learning Approach to SQL Injection Detection in Web Applications
CN117714184A (en) Industrial Internet safety monitoring method, device and equipment
Alshammari et al. Internet of things attacks detection and classification using tiered hidden Markov model
Huang et al. Network Security Perception System Integrating Improved CNN Algorithm and Improved GRU Algorithm
Fuhnwi et al. A Hybrid Anomaly Detection Approach for Obfuscated Malware
Suhaimi et al. Data Leakage Detection in Cloud Computing Environment
Ahmad et al. Detection of the cyber network attack using robust random forest in a big data environment
Zhao et al. An integrated graph data privacy attack framework based on graph neural networks in IoT
Philip et al. Deep Learning for Web Intrusion Detection
Nazir et al. Ensemble Learning Techniques for the Detection of IoT Botnets