Dhingra et al., 2021 - Google Patents
Detection of denial of service using a cascaded multi-classifierDhingra et al., 2021
- Document ID
- 16558371681877374254
- Author
- Dhingra A
- Sachdeva M
- Publication year
- Publication venue
- International Journal of Computational Science and Engineering
External Links
Snippet
The paper proposes a cascaded multi-classifier two-phase intrusion detection (TP-ID) approach that can be trained to monitor incoming traffic for any suspicious data. It addresses the issue of efficient detection of intrusion in traffic and further classifies the suspicious traffic …
- 238000001514 detection method 0 title abstract description 54
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
- H04L63/1416—Event detection, e.g. attack signature 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
- H04L63/1425—Traffic logging, e.g. anomaly detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
- G06K9/6284—Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- H04L63/1433—Vulnerability analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/14—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Al-Janabi et al. | Intrusion Detection Systems, Issues, Challenges, and Needs. | |
Hodo et al. | Shallow and deep networks intrusion detection system: A taxonomy and survey | |
Jha et al. | Intrusion detection system using support vector machine | |
Miah et al. | Improving detection accuracy for imbalanced network intrusion classification using cluster-based under-sampling with random forests | |
Haq et al. | Development of PCCNN-based network intrusion detection system for EDGE computing. | |
Saxena et al. | Intrusion detection in KDD99 dataset using SVM-PSO and feature reduction with information gain | |
Sharma et al. | An improved network intrusion detection technique based on k-means clustering via Naïve bayes classification | |
Singh Samom et al. | Distributed denial of service (DDoS) attacks detection: A machine learning approach | |
Farhan et al. | Hybrid feature selection approach to improve the deep neural network on new flow-based dataset for NIDS | |
Mughaid et al. | Utilizing machine learning algorithms for effectively detection iot ddos attacks | |
Alsumaidaie et al. | Intelligent detection of distributed denial of service attacks: A supervised machine learning and ensemble approach | |
Hariprasad et al. | Detection of DDoS Attack in IoT Networks Using Sample Selected RNN-ELM. | |
Mukhaini et al. | A systematic literature review of recent lightweight detection approaches leveraging machine and deep learning mechanisms in Internet of Things networks | |
Al-Bakaa et al. | A new intrusion detection system based on using non-linear statistical analysis and features selection techniques | |
Yousef et al. | Detection of non-periodic low-rate denial of service attacks in software defined networks using machine learning | |
Maseer et al. | Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges | |
Ravipati et al. | A survey on different machine learning algorithms and weak classifiers based on KDD and NSL-KDD datasets | |
Wu et al. | An active learning framework using deep Q-network for zero-day attack detection | |
Kumar et al. | Attack and Anomaly Detection in IIoT Networks Using Machine Learning Techniques | |
Aburomman et al. | Evolutionof Intrusion Detection Systems Based on Machine Learning Methods | |
Dhingra et al. | Detection of denial of service using a cascaded multi-classifier | |
Janardhana et al. | Detecting security and privacy attacks in iot network using deep learning algorithms | |
Huynh et al. | On the performance of intrusion detection systems with hidden multilayer neural network using DSD training | |
Kavitha et al. | Machine learning techniques for detecting DDoS attacks in SDN | |
Sharma et al. | Machine Learning-Based Anomaly Detection in the Internet of Things |