Khan et al., 2024 - Google Patents
DivaCAN: Detecting in-vehicle intrusion attacks on a controller area network using ensemble learningKhan et al., 2024
- Document ID
- 15743794653169959231
- Author
- Khan M
- Javed A
- Iqbal Z
- Asim M
- Awad A
- Publication year
- Publication venue
- Computers & Security
External Links
Snippet
The controller area network (CAN) protocol is a critical communication mechanism in vehicular systems. However, the widespread adoption of this protocol has introduced vulnerabilities to in-vehicle communication channels, making them susceptible to various …
- 238000000034 method 0 abstract description 86
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
-
- 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
- G06K9/6279—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
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
-
- 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
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hasan et al. | Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches | |
Koroniotis et al. | A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework | |
Drewek-Ossowicka et al. | A survey of neural networks usage for intrusion detection systems | |
Halim et al. | An effective genetic algorithm-based feature selection method for intrusion detection systems | |
Sohi et al. | RNNIDS: Enhancing network intrusion detection systems through deep learning | |
Hu et al. | A multiple-kernel clustering based intrusion detection scheme for 5G and IoT networks | |
Imran et al. | An intelligent and efficient network intrusion detection system using deep learning | |
Khan et al. | Long short-term memory neural network-based attack detection model for in-vehicle network security | |
Carrasco et al. | Unsupervised intrusion detection through skip-gram models of network behavior | |
Khan et al. | DivaCAN: Detecting in-vehicle intrusion attacks on a controller area network using ensemble learning | |
Sharma et al. | Explainable artificial intelligence for cybersecurity | |
Yang et al. | Application of meta-learning in cyberspace security: A survey | |
Imran et al. | A performance overview of machine learning-based defense strategies for advanced persistent threats in industrial control systems | |
Wang et al. | Anomaly detection in Internet of medical Things with Blockchain from the perspective of deep neural network | |
El-Ghamry et al. | Optimized and efficient image-based IoT malware detection method | |
Alsoufi et al. | Anomaly intrusion detection systems in IoT using deep learning techniques: a survey | |
Kuppa et al. | Learn to adapt: Robust drift detection in security domain | |
Perumal et al. | VBQ-Net: a novel vectorization-based boost quantized network model for maximizing the security level of IoT system to prevent intrusions | |
Aravamudhan | A novel adaptive network intrusion detection system for internet of things | |
Dib et al. | Machine learning-based ransomware classification of Bitcoin transactions | |
Sadia et al. | Intrusion Detection System for Wireless Sensor Networks: A Machine Learning based Approach | |
Beshah et al. | Drift Adaptive Online DDoS Attack Detection Framework for IoT System | |
Arreche et al. | XAI-IDS: Toward Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection Systems | |
Alkhonaini et al. | Hybrid Sine-Cosine Chimp optimization based feature selection with deep learning model for threat detection in IoT sensor networks | |
Soflaei et al. | Enhancing network intrusion detection: a dual-ensemble approach with CTGAN-balanced data and weak classifiers |