Lavrova et al., 2019 - Google Patents
Approach to presenting network infrastructure of cyberphysical systems to minimize the cyberattack neutralization timeLavrova et al., 2019
View PDF- Document ID
- 11472859636287147884
- Author
- Lavrova D
- Zaitseva E
- Zegzhda D
- Publication year
- Publication venue
- Automatic Control and Computer Sciences
External Links
Snippet
This article proposes an approach to presenting the network infrastructure of cyberphysical systems to provide a more rapid identification of a suitable variant to rearranging the route on a graph that characterizes the target function. The proposed approach minimizes the …
- 230000005591 charge neutralization 0 title description 6
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
- G06F21/50—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
- 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
-
- 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
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
- G06F15/173—Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- 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
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- 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
-
- 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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gharehchopogh et al. | A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT | |
Lo et al. | XG-BoT: An explainable deep graph neural network for botnet detection and forensics | |
Yeh et al. | Predicting Spread Probability of Learning‐Effect Computer Virus | |
Sen | A survey of intrusion detection systems using evolutionary computation | |
Dinakarrao et al. | Cognitive and scalable technique for securing IoT networks against malware epidemics | |
Shakya | Process mining error detection for securing the IoT system | |
Lavrova et al. | Approach to presenting network infrastructure of cyberphysical systems to minimize the cyberattack neutralization time | |
Aldowah et al. | Trust in iot systems: a vision on the current issues, challenges, and recommended solutions | |
Nakhodchi et al. | A comparison between different machine learning models for iot malware detection | |
Rubio et al. | Tracking advanced persistent threats in critical infrastructures through opinion dynamics | |
Gore et al. | A Machine Learning-Based Detection of IoT Cyberattacks in Smart City Application | |
Ghosh et al. | Network anomaly detection using a fuzzy rule-based classifier | |
Zegzhda et al. | Management of a dynamic infrastructure of complex systems under conditions of directed cyber attacks | |
Truong et al. | X-ware: a proof of concept malware utilizing artificial intelligence | |
Al-Hawawreh et al. | Digital twin-driven secured edge-private cloud Industrial Internet of Things (IIoT) framework | |
Xing et al. | PeerRemove: An adaptive node removal strategy for P2P botnet based on deep reinforcement learning | |
La Salle et al. | Joint modeling of hyperledger fabric and sybil attack: petri net approach | |
Kalinin et al. | AI methods for neutralizing cyber threats at unmanned vehicular ecosystem of smart city | |
Kumar et al. | An explainable nature-inspired cyber attack detection system in Software-Defined IoT applications | |
Pavlenko | Functional model of adaptive network topology of large-scale systems based on dynamical graph theory | |
Azizpour et al. | Nada: new architecture for detecting dos and ddos attacks in fog computing | |
Hathal et al. | Attack and anomaly prediction in networks-on-chip of multiprocessor system-on-chip-based IoT utilizing machine learning approaches | |
Marimuthu et al. | Mathematically modified adaptive neuro-fuzzy inference system for an intelligent cyber security system | |
Sasirekha et al. | Localizing Worst-Parent Rank Attack Using Intelligent Edges of Smart Buildings | |
Gotarane et al. | A Hybrid Framework Leveraging Whale Optimization and Deep Learning with Trust-Index for Attack Identification in IoT Networks |