Alohali et al., 2023 - Google Patents
Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment.Alohali et al., 2023
View PDF- Document ID
- 7505036377138484167
- Author
- Alohali M
- Elsadig M
- Al-Wesabi F
- Al Duhayyim M
- Hilal A
- Motwakel A
- Publication year
- Publication venue
- Computer Systems Science & Engineering
External Links
Snippet
With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security …
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
- 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
- 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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- 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
-
- 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
- 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
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lu | Malware detection with lstm using opcode language | |
Song et al. | Deepmem: Learning graph neural network models for fast and robust memory forensic analysis | |
CN110135157B (en) | Malware homology analysis method, system, electronic device and storage medium | |
US11025649B1 (en) | Systems and methods for malware classification | |
US11580222B2 (en) | Automated malware analysis that automatically clusters sandbox reports of similar malware samples | |
Aamir et al. | AMDDLmodel: Android smartphones malware detection using deep learning model | |
Alohali et al. | Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment. | |
Li et al. | Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection | |
Li et al. | An adversarial machine learning method based on opcode n-grams feature in malware detection | |
Mishra et al. | Hybrid deep learning algorithm for smart cities security enhancement through blockchain and internet of things | |
Ibor et al. | Novel adaptive cyberattack prediction model using an enhanced genetic algorithm and deep learning (AdacDeep) | |
Santhadevi et al. | Light Weight Gradient Ensemble Model for detecting network attack at the edge of the IoT network | |
Jyothish et al. | Effectiveness of machine learning based android malware detectors against adversarial attacks | |
Liu et al. | SeInspect: Defending model stealing via heterogeneous semantic inspection | |
Rahman et al. | An exploratory analysis of feature selection for malware detection with simple machine learning algorithms | |
Asha et al. | Evaluation of adversarial machine learning tools for securing AI systems | |
Lin et al. | Senseinput: An image-based sensitive input detection scheme for phishing website detection | |
Jere et al. | Principal component properties of adversarial samples | |
Li et al. | Security application of intrusion detection model based on deep learning in english online education | |
Patel et al. | AMD-XAI-ML: Android malware detection based on an explainable AI using machine learning for smart computing environment | |
Gupta et al. | Deep learning approach for malicious url detection using cnn, rnn, lstm and bi-lstm models | |
Rayankula | An evaluation and performance study on BODMAS dataset for malware analysis | |
Stokes et al. | Detection of prevalent malware families with deep learning | |
Maazalahi et al. | A Hybrid Machine Learning Approach and Genetic Algorithm for Malware Detection | |
Kumar et al. | Enhancing Malware Detection Accuracy: A Comparative Analysis of Machine Learning Models with Explainable AI |