[go: up one dir, main page]

Chen et al., 2018 - Google Patents

Droideye: Fortifying security of learning-based classifier against adversarial android malware attacks

Chen et al., 2018

View PDF
Document ID
18427380960610860104
Author
Chen L
Hou S
Ye Y
Xu S
Publication year
Publication venue
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

External Links

Snippet

To combat the evolving Android malware attacks, systems using machine learning techniques have been successfully deployed for Android malware detection. In these systems, based on different feature representations, various kinds of classifiers are …
Continue reading at www.academia.edu (PDF) (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
    • 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
    • G06K9/4671—Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • 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
    • 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
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • 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
    • 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
    • H04L63/1441—Countermeasures against malicious traffic
    • 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
    • G06F21/71—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information
    • G06F21/77—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in smart cards

Similar Documents

Publication Publication Date Title
Chen et al. Droideye: Fortifying security of learning-based classifier against adversarial android malware attacks
Islam et al. Android malware classification using optimum feature selection and ensemble machine learning
Ganju et al. Property inference attacks on fully connected neural networks using permutation invariant representations
Chen et al. Adversarial machine learning in malware detection: Arms race between evasion attack and defense
Sinha et al. A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning
Razak et al. Bio-inspired for features optimization and malware detection
Ye et al. A survey on malware detection using data mining techniques
Chakraborty et al. Ec2: Ensemble clustering and classification for predicting android malware families
Liu et al. ATMPA: attacking machine learning-based malware visualization detection methods via adversarial examples
Zhang et al. Adversarial feature selection against evasion attacks
Dhalaria et al. Android malware detection techniques: A literature review
Pelekis et al. Adversarial machine learning: a review of methods, tools, and critical industry sectors: S. Pelekis et al.
Kirubavathi et al. Behavioral based detection of android ransomware using machine learning techniques
Visalakshi Detecting android malware using an improved filter based technique in embedded software
Panja et al. An efficient malware detection approach based on machine learning feature influence techniques for resource-constrained devices
Habeeb et al. Enhanced android malware detection through artificial neural networks technique
Albishry et al. An attribute extraction for automated malware attack classification and detection using soft computing techniques
Jyothish et al. Effectiveness of machine learning based android malware detectors against adversarial attacks
Li et al. Sok: Arms race in adversarial malware detection
Chen et al. Predicting Android malware combining permissions and API call sequences
Devine et al. An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions.
Zyout et al. Malware classification approaches utilizing binary and text encoding of permissions
Alsumaidaee et al. Optimizing Malware Detection and Classification in Real-Time Using Hybrid Deep Learning Approaches.
Surendran et al. Implementation of dynamic scanner to protect the documents from ransomware using machine learning algorithms
Andrade et al. Malware classification using word embeddings algorithms and long‐short term memory networks