Malware Program
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A Malware Program is a software program that is intended to disrupt computer systems.
- Context:
- It can (typically) contain a Malware Vector.
- It can be detected by a Malware Detection System (that solves a malware detection task).
- …
- Example(s):
- a Computer Virus.
- a Trojan Horse.
- a Ransomware, such as: WannaCry.
- …
- Counter-Example(s):
- See: Attack Website, Phishing.
References
2022
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/malware Retrieved:2022-7-13.
- Malware (a portmanteau for malicious software) is any software intentionally designed to cause disruption to a computer, server, client, or computer network, leak private information, gain unauthorized access to information or systems, deprive users access to information or which unknowingly interferes with the user's computer security and privacy. By contrast, software that causes harm due to some deficiency is typically described as a software bug. Malware poses serious problems to individuals and businesses on the Internet. According to Symantec’s 2018 Internet Security Threat Report (ISTR), malware variants number has increased to 669,947,865 in 2017, which is twice as many malware variants as in 2016. Cybercrime, which includes malware attacks as well as other crimes committed by computer, was predicted to cost the world economy 6 trillion dollars in 2021, and is increasing at a rate of 15% per year.
Many types of malware exist, including computer viruses, worms, Trojan horses, ransomware, spyware, adware, rogue software, wiper, and scareware. The defense strategies against malware differs according to the type of malware but most can be thwarted by installing antivirus software, firewalls, applying regular patches to reduce zero-day attacks, securing networks from intrusion, having regular backups and isolating infected systems. Malware is now being designed to evade antivirus software detection algorithms.
- Malware (a portmanteau for malicious software) is any software intentionally designed to cause disruption to a computer, server, client, or computer network, leak private information, gain unauthorized access to information or systems, deprive users access to information or which unknowingly interferes with the user's computer security and privacy. By contrast, software that causes harm due to some deficiency is typically described as a software bug. Malware poses serious problems to individuals and businesses on the Internet. According to Symantec’s 2018 Internet Security Threat Report (ISTR), malware variants number has increased to 669,947,865 in 2017, which is twice as many malware variants as in 2016. Cybercrime, which includes malware attacks as well as other crimes committed by computer, was predicted to cost the world economy 6 trillion dollars in 2021, and is increasing at a rate of 15% per year.
2019
- (Ucci et al., 2019) ⇒ Daniele Ucci, Leonardo Aniello, and Roberto Baldoni. (2019). “Survey of Machine Learning Techniques for Malware Analysis.” Computers & Security 81
- ABTRACT: Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies to keep pace with malware evolution. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis in Windows environments, i.e. for the analysis of Portable Executables. We systematize surveyed papers according to their objectives (i.e., the expected output), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of issues and challenges, including those concerning the used datasets, and identify the main current topical trends and how to possibly advance them. In particular, we introduce the novel concept of malware analysis economics, regarding the study of existing trade-offs among key metrics, such as analysis accuracy and economical costs.
2010
- (Ye et al., 2010) ⇒ Yanfang Ye, Tao Li, Yong Chen, and Qingshan Jiang. (2010). “Automatic Malware Categorization Using Cluster Ensemble.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835820
- ABSTRACT: In this paper, resting on the analysis of instruction frequency and function-based instruction sequences, we develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. We propose a principled cluster ensemble framework for combining individual clustering solutions based on the consensus partition. The domain knowledge in the form of sample-level constraints can be naturally incorporated in the ensemble framework. In addition, to account for the characteristics of feature representations, we propose a hybrid hierarchical clustering algorithm which combines the merits of hierarchical clustering and k-medoids algorithms and a weighted subspace K-medoids algorithm to generate base clusterings. The categorization results of our AMCS system can be used to generate signatures for malware families that are useful for malware detection. The case studies on large and real daily malware collection from Kingsoft Anti-Virus Lab demonstrate the effectiveness and efficiency of our AMCS system.