Query: malware detection survey

title: Cloud Malware and Malware Detection Survey
abstract: Malware affects computing systems like cloud systems through its malicious actions towards confidentiality, availability, and integrity of system resources and services. Because of the deployment of Cloud Computing environment is increasingly common, and we are implicitly reliant on them for many services as it offers cost saving service for Cloud user. So, it is important to take care of Cloud Security. In this paper, we investigate a survey on malware types that may exploit Cloud Computing systems; in the next sections, different aspects of malware detection techniques and types are discussed and how these malware detection systems fit into the cloud computing system architecture. Finally, a comparison between malware detection sensors is provided through many points like modeling techniques, main components, scalability, open source availability, and a risk analysis for those detection systems.

title: A Survey on Mobile Malware Detection Techniques

title: A Survey on Malware Detection and Analysis Tools
abstract: The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermines the effectiveness of traditional defenses that typically use signature based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior which indicate their origin and purpose. The behavioural trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.

title: A Survey on Malware Detection and Classification
abstract: Today one of the foremost threats on the Internet is mischievous software, often denoted as malware. In this era, one of the hot research areas is detection, visualization, and classification of malware. The current era needs an automated malware classification system without disassembly or code obfuscation. This paper gives a brief survey of visualization and classification of malware image. This survey includes a study of a considerable number of previous research papers covering the research characteristics of a system design of malware classification technique, a survey of the category of malware, and malware images.

title: Adversarial EXEmples
abstract: Recent work has shown that adversarial Windows malware samples referred to as adversarial EXEmples in this article can bypass machine learning based detection relying on static code analysis by perturbing relatively few input bytes. To preserve malicious functionality, previous attacks either add bytes to existing non functional areas of the file, potentially limiting their effectiveness, or require running computationally demanding validation steps to discard malware variants that do not correctly execute in sandbox environments. In this work, we overcome these limitations by developing a unifying framework that does not only encompass and generalize previous attacks against machine learning models, but also includes three novel attacks based on practical, functionality preserving manipulations to the Windows Portable Executable file format. These attacks, named Full DOS, Extend, and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section. Our experimental results show that these attacks outperform existing ones in both white box and black box scenarios, achieving a better tradeoff in terms of evasion rate and size of the injected payload, while also enabling evasion of models that have been shown to be robust to previous attacks. To facilitate reproducibility of our findings, we open source our framework and all the corresponding attack implementations as part of the secml malware Python library. We conclude this work by discussing the limitations of current machine learning based malware detectors, along with potential mitigation strategies based on embedding domain knowledge coming from subject matter experts directly into the learning process.

title: Malware Detection in Android Systems with Traditional Machine Learning Models: A Survey
abstract: Due to the increased number of mobile devices, they are integrated in every dimension of our daily life. To execute some sophisticated programs, a capable operating must be set up on them. Undoubtedly, Android is the most popular mobile operating system in the world. IT is extensively used both in smartphones and tablets with an open source manner which is distributed with Apache License. Therefore, many mobile application developers focused on these devices and implement their products. In recent years, the popularity of Android devices makes it a desirable target for malicious attackers. Especially sophisticated attackers focused on the implementation of Android malware which can acquire and/or utilize some personal and sensitive data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. In this work, we aimed to analyze the algorithms which are used in malware detection and making a comparative analysis of the literature. With this study, it is intended to produce a comprehensive survey resource for the researchers, which aim to work on malware detection.

title: A Survey of different machine learning models for static and dynamic malware detection
abstract: Malicious software (malware) plays a vital role in cybercrime security. As the number of malicious attacks and its target sources is increasing, it is difficult to find and prevent the attack due to its change in behaviour. Most of the traditional malware detection models are based on the statistical, analytical, and machine learning models. Detection of malware usually utilizes virus signature methods to defend against malicious software. Most antivirus tools to categorize malware depend on regular expression and pattern. Antivirus is less likely to update their databases to detect and prevent malware as file features have to update a newly created malware. The practically maximum human effort was required in order to generate attack signatures. In this paper, different types of malware detection models and their problems are discussed. This paper provides an extensive survey on the malware attack detection using traditional supervised, unsupervised models. Different types of malware attacks and their variations in behaviour are discussed in offline and online systems.

title: Classification Of Malware Detection Using Machine Learning Algorithms: A Survey
abstract: Malware is the one which frequently growing day by day and becomes major threats to the Internet Security. The are several methods for classifying of new malware from the existing signatures or code. The traditional approaches are not much effective to compete the new arriving malware samples. More antivirus softwares provides defense mechanism against malwares but still zero day attack is not achieved. To enhance in mechanisms machine learning algorithms are used and provide good experimental results accordingly. While the traditional signature approaches are also failed to compete the new malwares. In this paper, we define malware and types of malware as an overview, as well we define the new mechanism of using machine learning algorithms how effective and efficient in classification of malware detection and we presented the existing works related to malware detection classification using machine learning algorithms and it is discussed about main important challenges that are facing in malware detection classification.

title: Malware Detection Techniques: A Survey
abstract: Malware is derived from malicious software which mitigate to attacks on the computer systems and collecting private data. The survey is available in huge evidences to suggest its impact in global losses. Malware detectors are basic tools to protect from the same malware attacks. Therefore, it is important to require study on malware detection techniques, to avoid and identify the type of malware attacked on systems. In this manuscript, a survey report is available to defend against malware attacks and analysis techniques. There are many malware detection techniques, such as signature and anomaly detection techniques with an idea of comparison and decision making about its strengths. This provides as a user reference to the end user for likely detailed information.

title: A Survey on Malware Detection Technology and Future Trends
abstract: Malware has become a serious threat to the internet. Their numbers are constantly increasing, and the level of complexity is rising. This paper aims to conduct a systematic survey on the development of malware detection technology. The main contributions of this paper are: 1) Describing in detail the state of the art of malware detection methods, 2) Exploring the challenges and limitations of machine learning implementation on malware detection, 3) Providing new trends and developments in malware detection technology. This survey is expected to help researchers to understand the malware detection technology and the direction of its research development.