[go: up one dir, main page]

Liu et al., 2022 - Google Patents

SeInspect: Defending model stealing via heterogeneous semantic inspection

Liu et al., 2022

Document ID
4886854069822158583
Author
Liu X
Ma Z
Liu Y
Qin Z
Zhang J
Wang Z
Publication year
Publication venue
European symposium on research in computer security

External Links

Snippet

Recent works developed an emerging attack, called Model Stealing (MS), to steal the functionalities of remote models, rendering the privacy of cloud-based machine learning services under threat. In this paper, we propose a new defense against MS attacks, using Se …
Continue reading at link.springer.com (other versions)

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
    • G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/562—Static detection
    • G06F21/563—Static detection by source code analysis
    • 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
    • G06F21/554—Detecting local intrusion or implementing counter-measures involving event detection and direct action
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Wang et al. Practical detection of trojan neural networks: Data-limited and data-free cases
Chawla et al. Host based intrusion detection system with combined CNN/RNN model
Zahoora et al. Zero-day ransomware attack detection using deep contractive autoencoder and voting based ensemble classifier
Wei et al. Deephunter: A graph neural network based approach for robust cyber threat hunting
Dai et al. SMASH: A malware detection method based on multi-feature ensemble learning
Avci et al. Analyzing the performance of long short‐term memory architectures for malware detection models
Ajorpaz et al. Evax: Towards a practical, pro-active & adaptive architecture for high performance & security
Galli et al. Explainability in AI-based behavioral malware detection systems
Albahar et al. A modified ResNeXt for Android malware identification and classification
Liu et al. SeInspect: Defending model stealing via heterogeneous semantic inspection
Yagemann et al. Barnum: Detecting document malware via control flow anomalies in hardware traces
He et al. Image-based zero-day malware detection in iomt devices: A hybrid ai-enabled method
Köylü et al. A survey on machine learning in hardware security
Zheng et al. A new malware detection method based on vmcadr in cloud environments
Alohali et al. Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment.
Alqahtani Machine learning techniques for malware detection with challenges and future directions
Dahiya et al. Android malware analysis and detection: A systematic review
Stokes et al. Detection of prevalent malware families with deep learning
Liu et al. Model stealing detection for iot services based on multi-dimensional features
Cai et al. WBP: Training-Time Backdoor Attacks Through Hardware-Based Weight Bit Poisoning
Li et al. DeepReturn: a deep neural network can learn how to detect previously-unseen rop payloads without using any heuristics
Pan et al. Explainable AI for Cybersecurity
Zheng et al. MaskDroid: Robust Android Malware Detection with Masked Graph Representations
Kumar et al. An efficient security testing for android application based on behavior and activities using RFE-MLP and ensemble classifier
Venkatesh et al. A Non-intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones