Al Shorman et al., 2020 - Google Patents
Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detectionAl Shorman et al., 2020
- Document ID
- 9553085908319222901
- Author
- Al Shorman A
- Faris H
- Aljarah I
- Publication year
- Publication venue
- Journal of Ambient Intelligence and Humanized Computing
External Links
Snippet
Recently, the number of Internet of Things (IoT) botnet attacks has increased tremendously due to the expansion of online IoT devices which can be easily compromised. Botnets are a common threat that takes advantage of the lack of basic security tools in IoT devices and can …
- 238000001514 detection method 0 title abstract description 65
Classifications
-
- 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
- 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
- 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
- H04L63/1416—Event detection, e.g. attack signature detection
-
- 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
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
-
- 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
- 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
- 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
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Al Shorman et al. | Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection | |
Asif et al. | MapReduce based intelligent model for intrusion detection using machine learning technique | |
Alsaedi et al. | TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems | |
Talukder et al. | A dependable hybrid machine learning model for network intrusion detection | |
Kilincer et al. | Machine learning methods for cyber security intrusion detection: Datasets and comparative study | |
Li et al. | Data fusion for network intrusion detection: a review | |
Wanda et al. | DeepFriend: finding abnormal nodes in online social networks using dynamic deep learning | |
Gaber et al. | Industrial internet of things intrusion detection method using machine learning and optimization techniques | |
Zhong et al. | A Survey on Graph Neural Networks for Intrusion Detection Systems: Methods, Trends and Challenges | |
Wanda et al. | DeepOSN: Bringing deep learning as malicious detection scheme in online social network | |
Thakkar et al. | A review on challenges and future research directions for machine learning-based intrusion detection system | |
Salam | Intelligent system for IoT botnet detection using SVM and PSO optimization | |
Mvula et al. | A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning | |
Sharma et al. | Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review | |
Pandithurai et al. | DDoS attack prediction using a honey badger optimization algorithm based feature selection and Bi-LSTM in cloud environment | |
Mukhaini et al. | A systematic literature review of recent lightweight detection approaches leveraging machine and deep learning mechanisms in Internet of Things networks | |
Djenouri et al. | Interpretable intrusion detection for next generation of Internet of Things | |
Maseer et al. | Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges | |
Prabhakaran et al. | mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system | |
Padmavathi et al. | An efficient botnet detection approach based on feature learning and classification | |
Manivannan | Recent endeavors in machine learning-powered intrusion detection systems for the Internet of Things | |
Pakmehr et al. | DDoS attack detection techniques in IoT networks: a survey | |
Qu et al. | Towards intrusion detection in fog environments using generative adversarial network and long short-term memory network | |
Mittal et al. | Graph-ensemble fusion for enhanced IoT intrusion detection: leveraging GCN and deep learning | |
Saied et al. | A comparative analysis of using ensemble trees for botnet detection and classification in IoT |