[go: up one dir, main page]

Krishnaveni et al., 2021 - Google Patents

Ensemble approach for network threat detection and classification on cloud computing

Krishnaveni et al., 2021

Document ID
15356616689743580756
Author
Krishnaveni S
Prabakaran S
Publication year
Publication venue
Concurrency and Computation: Practice and Experience

External Links

Snippet

As Network traffic rises and attacks become more widespread and complicated, we must come across Innovative ways to enrich Intrusion Detection Systems in Cloud Computing. This paper proposes the Ensemble approaches for Network Intrusion Detection and …
Continue reading at onlinelibrary.wiley.com (other versions)

Classifications

    • 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
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Krishnaveni et al. Ensemble approach for network threat detection and classification on cloud computing
Khraisat et al. A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
Aljawarneh et al. Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model
Moustafa et al. An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things
Tufan et al. Anomaly-based intrusion detection by machine learning: A case study on probing attacks to an institutional network
Vinayakumar et al. Scalable framework for cyber threat situational awareness based on domain name systems data analysis
US10742669B2 (en) Malware host netflow analysis system and method
Sabottke et al. Vulnerability disclosure in the age of social media: Exploiting twitter for predicting {Real-World} exploits
Hoque et al. An implementation of intrusion detection system using genetic algorithm
Alazab et al. Using feature selection for intrusion detection system
Niakanlahiji et al. Phishmon: A machine learning framework for detecting phishing webpages
Wang et al. Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks
Haddadi et al. Benchmarking the effect of flow exporters and protocol filters on botnet traffic classification
Lappas et al. Data mining techniques for (network) intrusion detection systems
Alshehri et al. Attacking and protecting tunneled traffic of smart home devices
Xiao et al. From patching delays to infection symptoms: Using risk profiles for an early discovery of vulnerabilities exploited in the wild
Fallah et al. Android malware detection using network traffic based on sequential deep learning models
Garcıa Identifying, modeling and detecting botnet behaviors in the network
Jiang et al. Sok: Applying machine learning in security-a survey
Feng et al. BotFlowMon: Learning-based, content-agnostic identification of social bot traffic flows
Brandao et al. Log files analysis for network intrusion detection
BACHAR et al. Towards a behavioral network intrusion detection system based on the SVM model
Elekar Combination of data mining techniques for intrusion detection system
Sadeghpour et al. Unsupervised ML based detection of malicious web sessions with automated feature selection: Design and real-world validation
Shinan et al. BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning.