Asmitha et al., 2014 - Google Patents
A machine learning approach for linux malware detectionAsmitha et al., 2014
- Document ID
- 243005143677167249
- Author
- Asmitha K
- Vinod P
- Publication year
- Publication venue
- 2014 international conference on issues and challenges in intelligent computing techniques (ICICT)
External Links
Snippet
The increasing number of malware is becoming a serious threat to the private data as well as to the expensive computer resources. Linux is a Unix based machine and gained popularity in recent years. The malware attack targeting Linux has been increased recently …
- 238000001514 detection method 0 title abstract description 21
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
- G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
- G06F21/566—Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
-
- 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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/569—Immunoassay; Biospecific binding assay for micro-organisms, e.g. protozoa, bacteria, viruses
- G01N33/56911—Bacteria
- G01N33/5695—Mycobacteria
-
- 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
- G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Asmitha et al. | A machine learning approach for linux malware detection | |
| Alsaheel et al. | {ATLAS}: A sequence-based learning approach for attack investigation | |
| Mimura et al. | Applying NLP techniques to malware detection in a practical environment | |
| Lu | Malware detection with lstm using opcode language | |
| US10305923B2 (en) | Server-supported malware detection and protection | |
| Fan et al. | Malicious sequential pattern mining for automatic malware detection | |
| Herron et al. | Machine learning-based android malware detection using manifest permissions | |
| Park et al. | Deriving common malware behavior through graph clustering | |
| Shahzad et al. | Elf-miner: Using structural knowledge and data mining methods to detect new (linux) malicious executables | |
| Banin et al. | Multinomial malware classification via low-level features | |
| Sun et al. | An opcode sequences analysis method for unknown malware detection | |
| El Boujnouni et al. | New malware detection framework based on N-grams and support vector domain description | |
| Xiao et al. | A novel malware classification method based on crucial behavior | |
| Ban et al. | Integration of multi-modal features for android malware detection using linear SVM | |
| Mira et al. | Novel malware detection methods by using LCS and LCSS | |
| Alazab et al. | Detecting malicious behaviour using supervised learning algorithms of the function calls | |
| Raymond et al. | Investigation of Android Malware Using Deep Learning Approach. | |
| Zhang et al. | Smartdetect: a smart detection scheme for malicious web shell codes via ensemble learning | |
| Okane et al. | Malware detection: program run length against detection rate | |
| Sahu et al. | A review of malware detection based on pattern matching technique | |
| Stokes et al. | Neural classification of malicious scripts: A study with javascript and vbscript | |
| Zuhair | A panoramic evaluation of machine learning and deep learning-aided ransomware detection tools using a hybrid cluster of rich smartphone traits | |
| Uma et al. | Survey on Android malware detection and protection using data mining algorithms | |
| Landage et al. | Malware detection with different voting schemes | |
| Habtor et al. | Machine-Learning Classifiers for Malware Detection Using Data Features. |