Ligo et al., 2021 - Google Patents
Autonomous cyberdefense introduces risk: Can we manage the risk?Ligo et al., 2021
View PDF- Document ID
- 9001931655445748233
- Author
- Ligo A
- Kott A
- Linkov I
- Publication year
- Publication venue
- Computer
External Links
Snippet
Autonomous Cyberdefense Introduces Risk: Can We Manage the Risk? Page 1 SECTION
TITLE 106 COMPUTER PUBLISHED BY THE IEEE COMPUTER SOCIETY US Government
work not protected by US Copyright. CYBERTRUST Managing risks is a recurring research …
- 241000282414 Homo sapiens 0 abstract description 32
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
- G06F21/57—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
- 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
-
- 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
-
- 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
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
-
- 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
- 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
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mohammed | Building Trust in Driverless Technology: Overcoming Cybersecurity Challenges | |
| Zhang et al. | Intrusion detection system using deep learning for in-vehicle security | |
| Ghosh et al. | An integrated approach of threat analysis for autonomous vehicles perception system | |
| Han et al. | Secure operations of connected and autonomous vehicles | |
| Sedjelmaci et al. | Cyber security game for intelligent transportation systems | |
| Carlo et al. | The importance of cybersecurity frameworks to regulate emergent AI technologies for space applications | |
| Ligo et al. | Autonomous cyberdefense introduces risk: Can we manage the risk? | |
| Pekaric et al. | A systematic review on security and safety of self-adaptive systems | |
| Bou-Harb et al. | On the impact of empirical attack models targeting marine transportation | |
| Alsobeh et al. | Integrating data-driven security, model checking, and self-adaptation for IoT systems using BIP components: A conceptual proposal model | |
| Jing et al. | An artificial intelligence security framework | |
| Bairwa et al. | Implications of Cyber-Physical Adversarial Attacks on Autonomous Systems | |
| Rastogi et al. | Explaining radar features for detecting spoofing attacks in connected autonomous vehicles | |
| Boddupalli et al. | Redem: Real-time detection and mitigation of communication attacks in connected autonomous vehicle applications | |
| Quraishi et al. | Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs | |
| Sharma et al. | Machine Learning Techniques for Intelligent Vulnerability Detection in Cyber-Physical Systems | |
| Püllen et al. | ISO/SAE 21434-based risk assessment of security incidents in automated road vehicles | |
| Maxa et al. | Security challenges of vehicle recovery for urban air mobility contexts | |
| Kott | Autonomous Intelligent Cyber-defense Agent: Introduction and Overview | |
| Pavithra et al. | Security Algorithm for Intelligent Transport System in Cyber-Physical Systems Perceptive: Attacks, Vulnerabilities, and Countermeasures | |
| Muriithi et al. | Vulnerability Assessment and Detection of Stealthy Sequential Cyberattacks in Hybrid Tracked Vehicles | |
| Zhou et al. | ODFa ${}^{2} $: Overall Defense Framework Against Cyber-Attacks on Intelligent Connected Vehicles | |
| He | A machine learning-based anomaly detection framework for connected and autonomous vehicles cyber security | |
| Liu et al. | Optimization of mitigation deployment using deep reinforcement learning over an enhanced ATT &CK | |
| Bodeau et al. | Cyber Resiliency Metrics and Scoring in Practice |