HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps
Authors
Keywords
-
Journal
SENSORS
Volume 22, Issue 3, Pages 1079
Publisher
MDPI AG
Online
2022-01-30
DOI
10.3390/s22031079
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
- (2022) Shu Chen et al. Computational Intelligence and Neuroscience
- A survey and taxonomy of program analysis for IoT platforms
- (2021) Alyaa A. Hamza et al. Ain Shams Engineering Journal
- A conflicts’ classification for IoT-based services: a comparative survey
- (2021) Hamada Ibrhim et al. PeerJ Computer Science
- Supervised contrastive learning over prototype-label embeddings for network intrusion detection
- (2021) Manuel Lopez-Martin et al. Information Fusion
- Combining context-relevant features with multi-stage attention network for short text classification
- (2021) Yingying Liu et al. COMPUTER SPEECH AND LANGUAGE
- Deep learning and big data technologies for IoT security
- (2020) Mohamed Ahzam Amanullah et al. COMPUTER COMMUNICATIONS
- The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
- (2020) Daniel Gibert et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Security and privacy protection in cloud computing: Discussions and challenges
- (2020) PanJun Sun JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Multi-representational convolutional neural networks for text classification
- (2019) Rize Jin et al. COMPUTATIONAL INTELLIGENCE
- Program Analysis of Commodity IoT Applications for Security and Privacy
- (2019) Z. Berkay Celik et al. ACM COMPUTING SURVEYS
- A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT
- (2019) Jayasree Sengupta et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- PGFit: Static permission analysis of health and fitness apps in IoT programming frameworks
- (2019) Mehdi Nobakht et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Preserving Balance Between Privacy and Data Integrity in Edge-Assisted Internet of Things
- (2019) Tian Wang et al. IEEE Internet of Things Journal
- Malware classification using self organising feature maps and machine activity data
- (2018) Pete Burnap et al. COMPUTERS & SECURITY
- Distributed attack detection scheme using deep learning approach for Internet of Things
- (2018) Abebe Abeshu Diro et al. Future Generation Computer Systems-The International Journal of eScience
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
- (2018) Tom Young et al. IEEE Computational Intelligence Magazine
- Static analysis of android apps: A systematic literature review
- (2017) Li Li et al. INFORMATION AND SOFTWARE TECHNOLOGY
- Rewriting modulo SMT and open system analysis
- (2017) Camilo Rocha et al. Journal of Logical and Algebraic Methods in Programming
- CARED-SOA: A Context-Aware Event-Driven Service-Oriented Architecture
- (2017) Alfonso Garcia De Prado et al. IEEE Access
- A stochastic evolutionary coalition game model of secure and dependable virtual service in Sensor-Cloud
- (2015) Jianhua Liu et al. APPLIED SOFT COMPUTING
- Classification of malware based on integrated static and dynamic features
- (2012) Rafiqul Islam et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started