Classifying Windows Ransomware Based on Runtime Behavior Using Machine Learning Algorithms PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Classifying Windows Ransomware Based on Runtime Behavior Using Machine Learning Algorithms PDF full book. Access full book title Classifying Windows Ransomware Based on Runtime Behavior Using Machine Learning Algorithms by Lovina Moses Dmello. Download full books in PDF and EPUB format.
Author: Lovina Moses Dmello
Publisher:
ISBN:
Category :
Languages : en
Pages : 96
Get Book Here
Book Description
Ransomware is deƠ̐1ned as a type of malware program that infects, locks or takes control of the users system and demands ransom from the user to undo the damage. Ransomware detection is an important factor in security of computer systems. However, Zero-day attacks and polymorphic viruses are not easily detected by signature-based methods. As a result, need for machine learning based detection arises. The purpose of this work is to determine result of feature selection on classiƠ̐1cation methods when used on top of cuckoo sandbox. ClassiƠ̐1cation algorithms like k-Nearest-Neighbors, Naive Bayes, Support Vector Machines and Random Forest were evaluated. The dataset for this study consisted over 1584 ransomware samples of 11 diƠ̐0erent ransomware families. Cuckoo sandbox is used to run these samples and see their real time behavior. This work demonstrated the improvement in accuracy obtained using mutual information criteria for feature selection.
Author: Lovina Moses Dmello
Publisher:
ISBN:
Category :
Languages : en
Pages : 96
Get Book Here
Book Description
Ransomware is deƠ̐1ned as a type of malware program that infects, locks or takes control of the users system and demands ransom from the user to undo the damage. Ransomware detection is an important factor in security of computer systems. However, Zero-day attacks and polymorphic viruses are not easily detected by signature-based methods. As a result, need for machine learning based detection arises. The purpose of this work is to determine result of feature selection on classiƠ̐1cation methods when used on top of cuckoo sandbox. ClassiƠ̐1cation algorithms like k-Nearest-Neighbors, Naive Bayes, Support Vector Machines and Random Forest were evaluated. The dataset for this study consisted over 1584 ransomware samples of 11 diƠ̐0erent ransomware families. Cuckoo sandbox is used to run these samples and see their real time behavior. This work demonstrated the improvement in accuracy obtained using mutual information criteria for feature selection.
Author: Mihai Christodorescu
Publisher: Springer Science & Business Media
ISBN: 0387445994
Category : Computers
Languages : en
Pages : 307
Get Book Here
Book Description
This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.
Author:
Publisher:
ISBN:
Category : Computer crimes
Languages : en
Pages : 34
Get Book Here
Book Description
The availability of computer systems is constantly being challenged by cybercriminals who seek to disrupt access to this indispensable technology and the data they contain as a means for making profit. This trend has given rise to a new form of malware that is known as ransomware, an invasive type of malware that is designed to appropriate compute resources in return for a ransom. According to the U.S. Department of Homeland Security, ransomware represents the fastest growing malware threat to individuals and organizations. Therefore, it is imperative to explore solutions that can defend against such malware. In this study, we evaluate the effectiveness of machine learning algorithms and their suitability for detecting ransomware on x86 platforms. We show that dynamically extracting instruction op- codes from execution traces can be harnessed for training machine learning models that can used to perform runtime detection of ransomware. We evaluate different machine learning models and demonstrate that tracking a limited number of instruction opcodes commonly used crypto-graphic are sufficient for reliably detecting ransomware with high accuracy. We show that our method can achieve high detection rates above 99% while evaluating our solution against real ransomware available in a state-of-the-art dataset from VirusTotal.
Author: Mark Stamp
Publisher: Springer Nature
ISBN: 3030625826
Category : Computers
Languages : en
Pages : 651
Get Book Here
Book Description
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.
Author: Xiao-Zhi Gao
Publisher: Springer Nature
ISBN: 9811512752
Category : Technology & Engineering
Languages : en
Pages : 558
Get Book Here
Book Description
This book features high-quality papers presented at the International Conference on Computational Intelligence and Communication Technology (CICT 2019) organized by ABES Engineering College, Ghaziabad, India, and held from February 22 to 23, 2019. It includes the latest advances and research findings in fields of computational science and communication such as communication & networking, web & informatics, hardware and software designs, distributed & parallel processing, advanced software engineering, advanced database management systems and bioinformatics. As such, it is of interest to research scholars, students, and engineers around the globe.
Author: Lars Kaiser
Publisher: GRIN Verlag
ISBN: 3346809358
Category : Computers
Languages : en
Pages : 168
Get Book Here
Book Description
Bachelor Thesis from the year 2022 in the subject Computer Science - Commercial Information Technology, grade: 1.0, University of Applied Sciences Essen, language: English, abstract: One goal of the thesis is to evaluate static, dynamic and hybrid approaches in order to draw conclusions about the domains mentioned in the title of the thesis. Consequently, result-oriented conclusions about the characteristics that distinguish the three approaches from each other are to be drawn from the respective publications on basis of qualitative and quantitative evaluation criteria and the knowledge gap in the comparative literature is intended to be filled by the evaluation of hybrid approaches. The aim is to build a high-level understanding of the different methods and to identify differences and commonalities between these approaches based on research literature that presents new approaches within these domains. In particular, strengths, weaknesses and special properties of the three domains are to be determined. The second goal of this thesis is to develop a more comprehensive practical understanding of ML-based malware detection techniques, as exemplified by the practical section. Here, the ML workflow model is used to propose and implement a static malware detector step-by-step using the Python programming language and various ML algorithms. Accordingly the three primary research-questions this thesis aims to address are as follows: 1. Which static, dynamic and hybrid ML based approaches exist both in current and past research and how do they work? 2. How do the underlying methodological domains (static, dynamic and hybrid) com-pare under consideration of multiple quantitative and qualitative evaluation criteria? 3. How can a static malware detection model be implemented hands on in practice using the ML workflow process model as a guideline?
Author: Abdurrahman Pektaş
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
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.
Author: Ali Dehghantanha
Publisher: Springer
ISBN: 3319739514
Category : Computers
Languages : en
Pages : 334
Get Book Here
Book Description
This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes. The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions – this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works. The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgrounds in artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.
Author: Bander Alsulami
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 94
Get Book Here
Book Description
The advent of modern polymorphic and metamorphic malware, which encrypt or change their code when they replicate, rendered static signature detectors and classifiers less effective and gave rise to techniques that analyze the behavior of programs to detect and classify malware. Behavioral malware detectors and classifiers use run-time features to capture execution characteristics of running applications and are designed to overcome the shortcomings of static signature techniques. However, behavioral techniques introduce new challenges, for example, they need to be a) effective at attaining low false positive rates in a realistic setting, b) adaptive at maintaining their effectiveness as hosts change over time, and c) resilient against evasive malware that imitate the behavior of benign programs to avoid detection. This dissertation describes an adaptive and resilient malware detector and classifier based on behavioral data extracted from Microsoft Windows Prefetch files. The system detects and classifies malware with high accuracy, few false positives, and low overhead, but also adapts to changes in the monitored hosts, and is resilient against evasive malware. The malware detector uses an online algorithm to adapt to changes in the host Windows platforms over time. Moreover, the detector includes a defense mechanism against evasive mimicry malware. The malware classifier aims to improve on the state-of-art by classifying common and rare families with high classification accuracy and adapting to newly discovered malware samples and families. Extensive experiments are conducted to evaluate the effectiveness and performance of the detector and classifier, the efficiency of the online adaptation algorithm to learn new behavioral signatures, and the resilience of the techniques against mimicry malware. The novelty of the work lies in a) building a behavioral malware detector and classifier using dynamic features extracted from Microsoft Windows Prefetch files, b) creating a malware detector that is robust to mimicry attacks, and c) building an extensive experimental framework to evaluate the malware detector and classifier on a large collection of data.
Author: Martin Andreoni
Publisher: Springer Nature
ISBN: 3031614860
Category :
Languages : en
Pages : 413
Get Book Here
Book Description
