Barbhuiya et al., 2021 - Google Patents
Linear Regression Based DDoS Attack DetectionBarbhuiya et al., 2021
- Document ID
- 9247625329381466471
- Author
- Barbhuiya S
- Kilpatrick P
- S. Nikolopoulos D
- Publication year
- Publication venue
- Proceedings of the 2021 13th International Conference on Machine Learning and Computing
External Links
Snippet
DDoS attacks are increasing alongside the growth of web-based services. Existing research proposes a number of anomaly-based techniques which analyse network traffic to detect such attacks. However, these techniques typically raise a number of false positives …
- 238000001514 detection method 0 title abstract description 63
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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/26—Monitoring arrangements; Testing arrangements
- H04L12/2602—Monitoring arrangements
-
- 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
- H04L63/1458—Denial of Service
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing packet switching networks
- H04L43/08—Monitoring based on specific metrics
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing packet switching networks
- H04L43/02—Arrangements for monitoring or testing packet switching networks involving a reduction of monitoring data
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106341414B (en) | A multi-step attack security situation assessment method based on Bayesian network | |
Lazarevic et al. | A comparative study of anomaly detection schemes in network intrusion detection | |
Joshi et al. | A review of network traffic analysis and prediction techniques | |
US8418247B2 (en) | Intrusion detection method and system | |
Palmieri et al. | A distributed approach to network anomaly detection based on independent component analysis | |
Wang | A multinomial logistic regression modeling approach for anomaly intrusion detection | |
US20090245109A1 (en) | Methods, systems and computer program products for detecting flow-level network traffic anomalies via abstraction levels | |
Aborujilah et al. | Cloud‐Based DDoS HTTP Attack Detection Using Covariance Matrix Approach | |
Kato et al. | An intelligent ddos attack detection system using packet analysis and support vector machine | |
Soleimani et al. | Multi-layer episode filtering for the multi-step attack detection | |
Dhakar et al. | A novel data mining based hybrid intrusion detection framework | |
Niknami et al. | Entropy-kl-ml: Enhancing the entropy-kl-based anomaly detection on software-defined networks | |
Kornyo et al. | Botnet attacks classification in AMI networks with recursive feature elimination (RFE) and machine learning algorithms | |
Angelini et al. | An attack graph-based on-line multi-step attack detector | |
Brandao et al. | Log Files Analysis For Network Intrusion Detection | |
Alhaidari et al. | Network traffic anomaly detection based on Viterbi algorithm using SNMP MIB data | |
Li et al. | Distributed threat intelligence sharing system: a new sight of P2P botnet detection | |
Sait et al. | Multi-level anomaly detection: Relevance of big data analytics in networks | |
Barbhuiya et al. | Linear Regression Based DDoS Attack Detection | |
Chakir et al. | An efficient method for evaluating alerts of Intrusion Detection Systems | |
Maharaj et al. | A comparative analysis of different classification techniques for intrusion detection system | |
Nehinbe | Log Analyzer for Network Forensics and Incident Reporting | |
Farid et al. | Learning intrusion detection based on adaptive bayesian algorithm | |
Rastogi et al. | Network anomalies detection using statistical technique: a chi-square approach | |
Kumar et al. | Intrusion detection system using stream data mining and drift detection method |