Behavior Based Malware Classification Using Online Machine Learning
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Behavior Based Malware Classification Using Online Machine Learning
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Author : Abdurrahman Pektaş
language : en
Publisher:
Release Date : 2015
Behavior Based Malware Classification Using Online Machine Learning written by Abdurrahman Pektaş and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Recently, malware, short for malicious software has greatly evolved and became a major threat to the home users, enterprises, and even to the governments. Despite the extensive use and availability of various anti-malware tools such as anti-viruses, intrusion detection systems, firewalls etc., malware authors can readily evade these precautions by using obfuscation techniques. To mitigate this problem, malware researchers have proposed various data mining and machine learning approaches for detecting and classifying malware samples according to the their static or dynamic feature set. Although the proposed methods are effective over small sample set, the scalability of these methods for large data-set are in question.Moreover, it is well-known fact that the majority of the malware is the variant of the previously known samples. Consequently, the volume of new variant created far outpaces the current capacity of malware analysis. Thus developing malware classification to cope with increasing number of malware is essential for security community. The key challenge in identifying the family of malware is to achieve a balance between increasing number of samples and classification accuracy. To overcome this limitation, unlike existing classification schemes which apply machine learning algorithm to stored data, i.e., they are off-line, we proposed a new malware classification system employing online machine learning algorithms that can provide instantaneous update about the new malware sample by following its introduction to the classification scheme.To achieve our goal, firstly we developed a portable, scalable and transparent malware analysis system called VirMon for dynamic analysis of malware targeting Windows OS. VirMon collects the behavioral activities of analyzed samples in low kernel level through its developed mini-filter driver. Secondly we set up a cluster of five machines for our online learning framework module (i.e. Jubatus), which allows to handle large scale of data. This configuration allows each analysis machine to perform its tasks and delivers the obtained results to the cluster manager.Essentially, the proposed framework consists of three major stages. The first stage consists in extracting the behavior of the sample file under scrutiny and observing its interactions with the OS resources. At this stage, the sample file is run in a sandboxed environment. Our framework supports two sandbox environments: VirMon and Cuckoo. During the second stage, we apply feature extraction to the analysis report. The label of each sample is determined by using Virustotal, an online multiple anti-virus scanner framework consisting of 46 engines. Then at the final stage, the malware dataset is partitioned into training and testing sets. The training set is used to obtain a classification model and the testing set is used for evaluation purposes .To validate the effectiveness and scalability of our method, we have evaluated our method on 18,000 recent malicious files including viruses, trojans, backdoors, worms, etc., obtained from VirusShare, and our experimental results show that our method performs malware classification with 92% of accuracy.
Journal Of The National Institute Of Information And Communications Technology
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Author :
language : en
Publisher:
Release Date : 2016
Journal Of The National Institute Of Information And Communications Technology written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Electronic journals categories.
Malware Analysis Using Artificial Intelligence And Deep Learning
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Author : Mark Stamp
language : en
Publisher: Springer Nature
Release Date : 2020-12-20
Malware Analysis Using Artificial Intelligence And Deep Learning written by Mark Stamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-20 with Computers categories.
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
Data Science For Malware Analysis
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Author : Shane Molinari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-15
Data Science For Malware Analysis written by Shane Molinari and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Computers categories.
Unlock the secrets of malware data science with cutting-edge techniques, AI-driven analysis, and international compliance standards to stay ahead of the ever-evolving cyber threat landscape Key Features Get introduced to three primary AI tactics used in malware and detection Leverage data science tools to combat critical cyber threats Understand regulatory requirements for using AI in cyber threat management Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's world full of online threats, the complexity of harmful software presents a significant challenge for detection and analysis. This insightful guide will teach you how to apply the principles of data science to online security, acting as both an educational resource and a practical manual for everyday use. Data Science for Malware Analysis starts by explaining the nuances of malware, from its lifecycle to its technological aspects before introducing you to the capabilities of data science in malware detection by leveraging machine learning, statistical analytics, and social network analysis. As you progress through the chapters, you’ll explore the analytical methods of reverse engineering, machine language, dynamic scrutiny, and behavioral assessments of malicious software. You’ll also develop an understanding of the evolving cybersecurity compliance landscape with regulations such as GDPR and CCPA, and gain insights into the global efforts in curbing cyber threats. By the end of this book, you’ll have a firm grasp on the modern malware lifecycle and how you can employ data science within cybersecurity to ward off new and evolving threats.What you will learn Understand the science behind malware data and its management lifecycle Explore anomaly detection with signature and heuristics-based methods Analyze data to uncover relationships between data points and create a network graph Discover methods for reverse engineering and analyzing malware Use ML, advanced analytics, and data mining in malware data analysis and detection Explore practical insights and the future state of AI's use for malware data science Understand how NLP AI employs algorithms to analyze text for malware detection Who this book is for This book is for cybersecurity experts keen on adopting data-driven defense methods. Data scientists will learn how to apply their skill set to address critical security issues, and compliance officers navigating global regulations like GDPR and CCPA will gain indispensable insights. Academic researchers exploring the intersection of data science and cybersecurity, IT decision-makers overseeing organizational strategy, and tech enthusiasts eager to understand modern cybersecurity will also find plenty of useful information in this guide. A basic understanding of cybersecurity and information technology is a prerequisite.
Malware Analysis And Intrusion Detection In Cyber Physical Systems
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Author : Shiva Darshan, S.L.
language : en
Publisher: IGI Global
Release Date : 2023-09-26
Malware Analysis And Intrusion Detection In Cyber Physical Systems written by Shiva Darshan, S.L. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-26 with Computers categories.
Many static and behavior-based malware detection methods have been developed to address malware and other cyber threats. Even though these cybersecurity systems offer good outcomes in a large dataset, they lack reliability and robustness in terms of detection. There is a critical need for relevant research on enhancing AI-based cybersecurity solutions such as malware detection and malicious behavior identification. Malware Analysis and Intrusion Detection in Cyber-Physical Systems focuses on dynamic malware analysis and its time sequence output of observed activity, including advanced machine learning and AI-based malware detection and categorization tasks in real time. Covering topics such as intrusion detection systems, low-cost manufacturing, and surveillance robots, this premier reference source is essential for cyber security professionals, computer scientists, students and educators of higher education, researchers, and academicians.
Android Malware Detection Using Machine Learning
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Author : ElMouatez Billah Karbab
language : en
Publisher:
Release Date : 2021
Android Malware Detection Using Machine Learning written by ElMouatez Billah Karbab and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Behavior Based Malware Detection With Quantitative Data Flow Analysis
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Author : Tobias Wüchner
language : en
Publisher:
Release Date : 2016
Behavior Based Malware Detection With Quantitative Data Flow Analysis written by Tobias Wüchner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Analysis And Improvements Of Behaviour Based Malware Detection Mechanisms
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Author : Nada Massoud Alruhaily
language : en
Publisher:
Release Date : 2017
Analysis And Improvements Of Behaviour Based Malware Detection Mechanisms written by Nada Massoud Alruhaily and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Malware (Computer software) categories.
The massive growth of computer usage has led to an increase in the related security concerns. Malware, such as Viruses, Worms, and Trojans, have become a major issue due to the serious damages they cause. Since the first malware emerged, there has been a continuous battle between security researchers and malware writers, where the latter are constantly trying to evade detection by adopting new functionalities and malicious techniques. This thesis focuses on addressing some of the concerns and challenges encountered when detecting malware, based on their behavioural features observed; for each identified challenge, an approach that addresses the problem is proposed and evaluated. Firstly, the thesis provides an in-depth analysis of the underlying causes of malware misclassification when using machine learning-based malware detectors. Such causes need to be determined, so that the right mitigation can be adopted. The analysis shows that the misclassification is mostly due to changes in several malware variants without the family membership or the year of discovery being a factor. In addition, the thesis proposes a probabilistic approach for optimising the scanning performance of Forensic Virtual Machines (FVMs); which are cloud-based lightweight scanners that perform distributed monitoring of the cloud's Virtual Machines (VMs). Finally, a market-inspired prioritisation approach is proposed to balance the trade-off between the consumption of VMs' resources and accuracy when detecting malware on the cloud's VMs using Virtual Machine Introspection-based lightweight monitoring approaches (e.g. FVMs). The thesis concludes by highlighting future work and new directions that have emerged from the work presented.
Malware Detection
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Author : Priyanka Nandal
language : en
Publisher: diplom.de
Release Date : 2017-11-21
Malware Detection written by Priyanka Nandal and has been published by diplom.de this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-21 with Computers categories.
In the present work the behavior of malicious software is studied, the security challenges are understood, and an attempt is made to detect the malware behavior automatically using dynamic approach. Various classification techniques are studied. Malwares are then grouped according to these techniques and malware with unknown characteristics are clustered into an unknown group. The classifiers used in this research are k-Nearest Neighbors (kNN), J48 Decision Tree, and n-grams.
Malware Data Science
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Author : Joshua Saxe
language : en
Publisher: No Starch Press
Release Date : 2018-09-25
Malware Data Science written by Joshua Saxe and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-25 with Computers categories.
Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.