[go: up one dir, main page]

Rahman et al., 2011 - Google Patents

Adaptive intrusion detection based on boosting and naïve Bayesian classifier

Rahman et al., 2011

View PDF
Document ID
3592868678539049900
Author
Rahman C
Farid D
Rahman M
Publication year

External Links

Snippet

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naïve Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier for classifying an unknown or known example. The …
Continue reading at 103.109.52.4:8080 (PDF) (other versions)

Classifications

    • 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
    • G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/562—Static detection
    • G06F21/563—Static detection by source code analysis
    • 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
    • H04L63/1408—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
    • 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
    • H04L63/145—Countermeasures against malicious traffic the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
    • 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
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor

Similar Documents

Publication Publication Date Title
Rahman et al. Adaptive intrusion detection based on boosting and naïve Bayesian classifier
Thakkar et al. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
Azimjonov et al. Stochastic gradient descent classifier-based lightweight intrusion detection systems using the efficient feature subsets of datasets
US11520882B2 (en) Multi factor network anomaly detection
Farid et al. Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm.
Khreich et al. An anomaly detection system based on variable N-gram features and one-class SVM
Kumar et al. Intrusion Detection System using decision tree algorithm
Rahman et al. Attacks classification in adaptive intrusion detection using decision tree
Farid et al. Adaptive network intrusion detection learning: attribute selection and classification
Neethu Adaptive intrusion detection using machine learning
de Campos et al. Network intrusion detection system using data mining
Surakhi et al. The intrusion detection system by deep learning methods: issues and challenges
Aboosh et al. Android adware detection model based on machine learning techniques
Chatterjee et al. Multi-stage intrusion detection system aided by grey wolf optimization algorithm
Dharaneish et al. Comparative analysis of deep learning and machine learning models for network intrusion detection
Narayana et al. Data mining machine learning techniques–A study on abnormal anomaly detection system
Almseidin et al. Applying intrusion detection algorithms on the kdd-99 dataset
Kumar et al. Intrusion detection using artificial neural network with reduced input features
Tripathy et al. A review of various datasets for machine learning algorithm-based intrusion detection system: advances and challenges
Abhale et al. Deep learning algorithmic approach for operational anomaly based intrusion detection system in wireless sensor networks
Shrivastava et al. A review of intrusion detection technique by soft computing and data mining approach
Nalavade Using machine learning and statistical models for intrusion detection
Nagle et al. Feature Extraction Based Classification Technique for Intrusion Detection System
Afza et al. Intrusion detection learning algorithm through network mining
Rafsanjani et al. Intrusion detection by data mining algorithms: a review