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Author: Ravindar Reddy Ravula
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 0
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Book Description
Detecting new and unknown malware is a major challenge in today's software security profession. A lot of approaches for the detection of malware using data mining techniques have already been proposed. Majority of the works used static features of malware. However, static detection methods fall short of detecting present day complex malware. Although some researchers proposed dynamic detection methods, the methods did not use all the malware features. In this work, an approach for the detection of new and unknown malware was proposed and implemented. 582 malware and 521 benign software samples were collected from the Internet. Each sample was reverse engineered for analyzing its effect on the operating environment and to extract the static and behavioral features. The raw data extracted from the reverse engineering was preprocessed and two datasets are obtained: dataset with reversed features and dataset with API Call features. Feature reduction was performed manually on the dataset with reversed features and the features that do not contribute to the classification were removed. Machine learning classification algorithm, J48 was applied to dataset with reversed features to obtain classification rules and a decision tree with the rules was obtained. To reduce the tree size and to obtain optimum number of decision rules, attribute values in the dataset with reversed features were discretized and another dataset was prepared with discretized attribute values. The new dataset was applied to J48 algorithm and a decision tree was generated with another set of classification rules. To further reduce the tree and number of decision rules, the dataset with discretized features was subjected to a machine learning tool, BLEM2 which is based on the rough sets and produces decision rules. To test the accuracy of the rules, the dataset with decision rules from BLEM2 was given as input to J48 algorithm. The same procedure was followed for the dataset with API Call features. Another set of experiments was conducted on the three datasets using Naïve Bayes classifier to generate training model for classification. All the training models were tested with an independent training set. J48 decision tree algorithm produced better results with DDF and DAF datasets with accuracies of 81.448% and 89.140% respectively. Naïve Bayes classifier produced better results with DDF dataset with an accuracy of 85.067%.
Author: Ravindar Reddy Ravula
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 0
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Book Description
Detecting new and unknown malware is a major challenge in today's software security profession. A lot of approaches for the detection of malware using data mining techniques have already been proposed. Majority of the works used static features of malware. However, static detection methods fall short of detecting present day complex malware. Although some researchers proposed dynamic detection methods, the methods did not use all the malware features. In this work, an approach for the detection of new and unknown malware was proposed and implemented. 582 malware and 521 benign software samples were collected from the Internet. Each sample was reverse engineered for analyzing its effect on the operating environment and to extract the static and behavioral features. The raw data extracted from the reverse engineering was preprocessed and two datasets are obtained: dataset with reversed features and dataset with API Call features. Feature reduction was performed manually on the dataset with reversed features and the features that do not contribute to the classification were removed. Machine learning classification algorithm, J48 was applied to dataset with reversed features to obtain classification rules and a decision tree with the rules was obtained. To reduce the tree size and to obtain optimum number of decision rules, attribute values in the dataset with reversed features were discretized and another dataset was prepared with discretized attribute values. The new dataset was applied to J48 algorithm and a decision tree was generated with another set of classification rules. To further reduce the tree and number of decision rules, the dataset with discretized features was subjected to a machine learning tool, BLEM2 which is based on the rough sets and produces decision rules. To test the accuracy of the rules, the dataset with decision rules from BLEM2 was given as input to J48 algorithm. The same procedure was followed for the dataset with API Call features. Another set of experiments was conducted on the three datasets using Naïve Bayes classifier to generate training model for classification. All the training models were tested with an independent training set. J48 decision tree algorithm produced better results with DDF and DAF datasets with accuracies of 81.448% and 89.140% respectively. Naïve Bayes classifier produced better results with DDF dataset with an accuracy of 85.067%.
Author: Mihai Christodorescu
Publisher: Springer Science & Business Media
ISBN: 0387445994
Category : Computers
Languages : en
Pages : 307
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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: Shane Molinari
Publisher: Packt Publishing Ltd
ISBN: 1804615706
Category : Computers
Languages : en
Pages : 230
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Book Description
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. Malware Science 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.
Author: Mehedy Masud
Publisher: CRC Press
ISBN: 1439854556
Category : Computers
Languages : en
Pages : 450
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Book Description
Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware d
Author: Michael Sikorski
Publisher: No Starch Press
ISBN: 1593272901
Category : Computers
Languages : en
Pages : 802
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Book Description
Malware analysis is big business, and attacks can cost a company dearly. When malware breaches your defenses, you need to act quickly to cure current infections and prevent future ones from occurring. For those who want to stay ahead of the latest malware, Practical Malware Analysis will teach you the tools and techniques used by professional analysts. With this book as your guide, you'll be able to safely analyze, debug, and disassemble any malicious software that comes your way. You'll learn how to: –Set up a safe virtual environment to analyze malware –Quickly extract network signatures and host-based indicators –Use key analysis tools like IDA Pro, OllyDbg, and WinDbg –Overcome malware tricks like obfuscation, anti-disassembly, anti-debugging, and anti-virtual machine techniques –Use your newfound knowledge of Windows internals for malware analysis –Develop a methodology for unpacking malware and get practical experience with five of the most popular packers –Analyze special cases of malware with shellcode, C++, and 64-bit code Hands-on labs throughout the book challenge you to practice and synthesize your skills as you dissect real malware samples, and pages of detailed dissections offer an over-the-shoulder look at how the pros do it. You'll learn how to crack open malware to see how it really works, determine what damage it has done, thoroughly clean your network, and ensure that the malware never comes back. Malware analysis is a cat-and-mouse game with rules that are constantly changing, so make sure you have the fundamentals. Whether you're tasked with securing one network or a thousand networks, or you're making a living as a malware analyst, you'll find what you need to succeed in Practical Malware Analysis.
Author: Abhijit Mohanta
Publisher: Apress
ISBN: 9781484261927
Category : Computers
Languages : en
Pages : 780
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Book Description
Discover how the internals of malware work and how you can analyze and detect it. You will learn not only how to analyze and reverse malware, but also how to classify and categorize it, giving you insight into the intent of the malware. Malware Analysis and Detection Engineering is a one-stop guide to malware analysis that simplifies the topic by teaching you undocumented tricks used by analysts in the industry. You will be able to extend your expertise to analyze and reverse the challenges that malicious software throws at you. The book starts with an introduction to malware analysis and reverse engineering to provide insight on the different types of malware and also the terminology used in the anti-malware industry. You will know how to set up an isolated lab environment to safely execute and analyze malware. You will learn about malware packing, code injection, and process hollowing plus how to analyze, reverse, classify, and categorize malware using static and dynamic tools. You will be able to automate your malware analysis process by exploring detection tools to modify and trace malware programs, including sandboxes, IDS/IPS, anti-virus, and Windows binary instrumentation. The book provides comprehensive content in combination with hands-on exercises to help you dig into the details of malware dissection, giving you the confidence to tackle malware that enters your environment. What You Will Learn Analyze, dissect, reverse engineer, and classify malware Effectively handle malware with custom packers and compilers Unpack complex malware to locate vital malware components and decipher their intent Use various static and dynamic malware analysis tools Leverage the internals of various detection engineering tools to improve your workflow Write Snort rules and learn to use them with Suricata IDS Who This Book Is For Security professionals, malware analysts, SOC analysts, incident responders, detection engineers, reverse engineers, and network security engineers "This book is a beast! If you're looking to master the ever-widening field of malware analysis, look no further. This is the definitive guide for you." Pedram Amini, CTO Inquest; Founder OpenRCE.org and ZeroDayInitiative
Author: Priyanka Nandal
Publisher: diplom.de
ISBN: 3960677081
Category : Computers
Languages : en
Pages : 69
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Book Description
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.
Author: Wenke Lee
Publisher: Springer Science & Business Media
ISBN: 0387687688
Category : Computers
Languages : en
Pages : 178
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Book Description
Botnets have become the platform of choice for launching attacks and committing fraud on the Internet. A better understanding of Botnets will help to coordinate and develop new technologies to counter this serious security threat. Botnet Detection: Countering the Largest Security Threat consists of chapters contributed by world-class leaders in this field, from the June 2006 ARO workshop on Botnets. This edited volume represents the state-of-the-art in research on Botnets.
Author: Ana Fred
Publisher: Springer
ISBN: 3642371868
Category : Computers
Languages : en
Pages : 481
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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, IC3K 2011, held in Paris, France, in October 2011. This book includes revised and extended versions of a strict selection of the best papers presented at the conference; 39 revised full papers together with one invited lecture were carefully reviewed and selected from 429 submissions. According to the three covered conferences KDIR 2011, KEOD 2011, and KMIS 2011, the papers are organized in topical sections on knowledge discovery and information retrieval, knowledge engineering and ontology development, and on knowledge management and information sharing.
Author: B.M.K. Prasad
Publisher: CRC Press
ISBN: 1315318911
Category : Computers
Languages : en
Pages : 1551
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Book Description
This book is a collection of accepted papers that were presented at the International Conference on Communication and Computing Systems (ICCCS-2016), Dronacharya College of Engineering, Gurgaon, September 9–11, 2016. The purpose of the conference was to provide a platform for interaction between scientists from industry, academia and other areas of society to discuss the current advancements in the field of communication and computing systems. The papers submitted to the proceedings were peer-reviewed by 2-3 expert referees. This volume contains 5 main subject areas: 1. Signal and Image Processing, 2. Communication & Computer Networks, 3. Soft Computing, Intelligent System, Machine Vision and Artificial Neural Network, 4. VLSI & Embedded System, 5. Software Engineering and Emerging Technologies.
