Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Data preparation for data mining
Data preparation for data mining
A vector space model for automatic indexing
Communications of the ACM
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Machine Learning
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Static Analysis of Binary Code to Isolate Malicious Behaviors
WETICE '99 Proceedings of the 8th Workshop on Enabling Technologies on Infrastructure for Collaborative Enterprises
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Learning to detect malicious executables in the wild
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Static Analyzer of Vicious Executables (SAVE)
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
The Art of Computer Virus Research and Defense
The Art of Computer Virus Research and Defense
Polymorphic Malicious Executable Scanner by API Sequence Analysis
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Semantics-Aware Malware Detection
SP '05 Proceedings of the 2005 IEEE Symposium on Security and Privacy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Using engine signature to detect metamorphic malware
Proceedings of the 4th ACM workshop on Recurring malcode
PolyUnpack: Automating the Hidden-Code Extraction of Unpack-Executing Malware
ACSAC '06 Proceedings of the 22nd Annual Computer Security Applications Conference
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Static analysis of executables to detect malicious patterns
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Renovo: a hidden code extractor for packed executables
Proceedings of the 2007 ACM workshop on Recurring malcode
Behavior-based malware detection
Behavior-based malware detection
Opcodes as predictor for malware
International Journal of Electronic Security and Digital Forensics
Embedded Malware Detection Using Markov n-Grams
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Malware detection using adaptive data compression
Proceedings of the 1st ACM workshop on Workshop on AISec
Eureka: A Framework for Enabling Static Malware Analysis
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
Information Sciences: an International Journal
ACSAC '08 Proceedings of the 2008 Annual Computer Security Applications Conference
Unknown Malcode Detection Using OPCODE Representation
EuroISI '08 Proceedings of the 1st European Conference on Intelligence and Security Informatics
Information Security Tech. Report
International Journal of Computer Applications in Technology
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Review: Intrusion detection by machine learning: A review
Expert Systems with Applications: An International Journal
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Information Sciences: an International Journal
Semi-Supervised Learning
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Detecting self-mutating malware using control-flow graph matching
DIMVA'06 Proceedings of the Third international conference on Detection of Intrusions and Malware & Vulnerability Assessment
Idea: opcode-sequence-based malware detection
ESSoS'10 Proceedings of the Second international conference on Engineering Secure Software and Systems
OFFSS: optimal fuzzy-valued feature subset selection
IEEE Transactions on Fuzzy Systems
On the concept of software obfuscation in computer security
ISC'07 Proceedings of the 10th international conference on Information Security
Information Sciences: an International Journal
Editorial: Guest editorial: Special issue on data mining for information security
Information Sciences: an International Journal
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
Information Sciences: an International Journal
Hi-index | 0.07 |
Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing faster every year and poses a serious global security threat. Consequently, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect malware only after the malicious executable has already caused damage and provided the malware is adequately documented. Therefore, the signature-based method consistently fails to detect new malware. In this paper, we propose a new method to detect unknown malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting unknown malware.