Palmieri et al., 2014 - Google Patents

A distributed approach to network anomaly detection based on independent component analysis

Palmieri et al., 2014

Document ID
6803507897990335677
Author
Palmieri F
Fiore U
Castiglione A
Publication year
Publication venue
Concurrency and Computation: Practice and Experience

External Links

Snippet

Network anomalies, circumstances in which the network behavior deviates from its normal operational baseline, can be due to various factors such as network overload conditions, malicious/hostile activities, denial of service attacks, and network intrusions. New detection …
Continue reading at onlinelibrary.wiley.com (other versions)

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425Traffic logging, e.g. anomaly detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416Event detection, e.g. attack signature detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55Detecting local intrusion or implementing counter-measures
    • G06F21/552Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/57Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
    • G06F21/577Assessing vulnerabilities and evaluating computer system security
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1433Vulnerability analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/14Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/20Network architectures or network communication protocols for network security for managing network security; network security policies in general
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/02Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
    • H04L63/0227Filtering policies
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/16Network management using artificial intelligence
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00Data switching networks
    • H04L12/02Details
    • H04L12/26Monitoring arrangements; Testing arrangements
    • H04L12/2602Monitoring arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00Arrangements for monitoring or testing packet switching networks

Similar Documents

Publication Publication Date Title
Palmieri et al. A distributed approach to network anomaly detection based on independent component analysis
US12407712B2 (en) Artificial intelligence cyber security analyst
Sangkatsanee et al. Practical real-time intrusion detection using machine learning approaches
Folino et al. Ensemble based collaborative and distributed intrusion detection systems: A survey
Li et al. Data fusion for network intrusion detection: a review
Pektaş et al. A deep learning method to detect network intrusion through flow‐based features
CN108566364B (en) Intrusion detection method based on neural network
Repalle et al. Intrusion detection system using ai and machine learning algorithm
Yehezkel et al. Network anomaly detection using transfer learning based on auto-encoders loss normalization
Guo et al. GLD‐Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion
Vinolia et al. Machine learning and deep learning based intrusion detection in cloud environment: A review
Aminanto et al. Combating threat-alert fatigue with online anomaly detection using isolation forest
Albahar et al. Deep autoencoders and feedforward networks based on a new regularization for anomaly detection
Dou et al. Pc 2 a: predicting collective contextual anomalies via lstm with deep generative model
Singh et al. Network anomaly detection using autoencoder on various datasets: A comprehensive review
Atli Anomaly-based intrusion detection by modeling probability distributions of flow characteristics
Liao et al. A multi-step attack identification and correlation method based on multi-information fusion
Selim et al. DAE-BILSTM: A fog-based intrusion detection model using deep learning for IoT
Li et al. An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data
Almazmomi Advanced persistent threat detection using optimized and hybrid deep learning approach
Nnodi et al. Leveraging artificial intelligence for detecting insider threats in corporate networks
Yusof et al. Network-level behavioral malware analysis model based on Bayesian network
Divya Machine Learning for Intrusion Detection
Hogan et al. A study of data fusion for predicting novel activity in enterprise cyber-security
Gouveia et al. Deep learning for network intrusion detection: an empirical assessment