Shafiq et al., 2022 - Google Patents
Analyzing IoT attack feature association with threat actorsShafiq et al., 2022
View PDF- Document ID
- 14217634785321344005
- Author
- Shafiq M
- Gu Z
- Nazir S
- Yadav R
- Publication year
- Publication venue
- Wireless Communications and Mobile Computing
External Links
Snippet
Internet of Things (IoT) refers to the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. These devices can be controlled remotely, which makes them susceptible to exploitation or even takeover by an …
- 238000001514 detection method 0 abstract description 42
Classifications
-
- 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
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/34—User authentication involving the use of external additional devices, e.g. dongles or smart cards
-
- 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
- G06F21/70—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer
- G06F21/82—Protecting input, output or interconnection devices
- G06F21/83—Protecting input, output or interconnection devices input devices, e.g. keyboards, mice or controllers thereof
-
- 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
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
-
- 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
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/316—User authentication by observing the pattern of computer usage, e.g. typical user behaviour
-
- 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
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Reddy et al. | Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities | |
Awotunde et al. | Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection | |
Mahbooba et al. | Explainable artificial intelligence (XAI) to enhance trust management in intrusion detection systems using decision tree model | |
Li et al. | Data fusion for network intrusion detection: a review | |
Janeja | Data analytics for cybersecurity | |
Vangipuram et al. | A machine learning approach for imputation and anomaly detection in IoT environment | |
Abu Al‐Haija et al. | Boost‐Defence for resilient IoT networks: A head‐to‐toe approach | |
Yue et al. | Deep Learning‐Based Security Behaviour Analysis in IoT Environments: A Survey | |
Rani et al. | An Ensemble‐Based Multiclass Classifier for Intrusion Detection Using Internet of Things | |
Alghayadh | A hybrid intrusion detection system for smart home security based on machine learning and user behavior | |
Kumar et al. | An efficient simulated annealing based constrained optimization approach for outlier detection mechanism in RFID-sensor integrated MANET | |
Luo et al. | A systematic literature review of intrusion detection systems in the cloud‐based IoT environments | |
Demertzis et al. | An explainable semi-personalized federated learning model | |
Selim et al. | An efficient machine learning model for malicious activities recognition in water‐based industrial internet of things | |
Kaur et al. | A neutrosophic AHP-based computational technique for security management in a fog computing network | |
Shukla et al. | UInDeSI4. 0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem | |
Wang et al. | Real-time cyber-physical security solution leveraging an integrated learning-based approach | |
Shafiq et al. | Analyzing IoT attack feature association with threat actors | |
Papaioannou et al. | Risk-based user authentication for mobile passenger ID devices for land and sea border control | |
Kulkarni et al. | An intrusion detection system using extended Kalman filter and neural networks for IoT networks | |
Ryu et al. | Study on Trends and Predictions of Convergence in Cybersecurity Technology Using Machine Learning | |
Abuali et al. | Intrusion Detection Techniques in Social Media Cloud: Review and Future Directions | |
Padmavathi et al. | An efficient botnet detection approach based on feature learning and classification | |
Nandanwar et al. | TL-BILSTM IoT: transfer learning model for prediction of intrusion detection system in IoT environment | |
Guan et al. | The design and implementation of a multidimensional and hierarchical web anomaly detection system |