The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Babu M. Mehtre - One of the best experts on this subject based on the ideXlab platform.
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SNDS - Static Malware Analysis Using Machine Learning Methods
Communications in Computer and Information Science, 2014Co-Authors: Hiran V. Nath, Babu M. MehtreAbstract:Malware analysis forms a critical component of cyber defense mechanism. In the last decade, lot of research has been done, using machine learning methods on both static as well as dynamic analysis. Since the aim and objective of Malware developers have changed from just for fame to political espionage or financial gain, the Malware is also getting evolved in its form, and infection methods. One of the latest form of Malware is known as targeted Malware, on which not much research has happened. Targeted Malware, which is a superset of Advanced Persistent Threat (APT), is growing in its volume and complexity in recent years. Targeted Cyber attack (through targeted Malware) plays an increasingly malicious role in disrupting the online social and financial systems. APTs are designed to steal corporate / national secrets and/or harm national/corporate interests. It is difficult to recognize targeted Malware by antivirus, IDS, IPS and Custom Malware detection tools. Attackers leverage compelling social engineering techniques along with one or more zero day vulnerabilities for deploying APTs. Along with these, the recent introduction of Crypto locker and Ransom ware pose serious threats to organizations/nations as well as individuals. In this paper, we compare various machine-learning techniques used for analyzing Malwares, focusing on static analysis.
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Static Malware Analysis Using Machine Learning Methods
Recent Trends in Computer Networks and Distributed Systems Security: Second International Conference SNDS 2014 Trivandrum India March 13-14 2014 Proce, 2014Co-Authors: Hiran V. Nath, Babu M. MehtreAbstract:Malware analysis forms a critical component of cyber defense mechanism. In the last decade, lot of research has been done, using machine learning methods on both static as well as dynamic analysis. Since the aim and objective of Malware developers have changed from just for fame to political espionage or financial gain, the Malware is also getting evolved in its form, and infection methods. One of the latest form of Malware is known as targeted Malware, on which not much research has happened. Targeted Malware, which is a superset of Advanced Persistent Threat (APT), is growing in its volume and complexity in recent years. Targeted Cyber attack (through targeted Malware) plays an increasingly malicious role in disrupting the online social and financial systems. APTs are designed to steal corporate / national secrets and/or harm national/corporate interests. It is difficult to recognize targeted Malware by antivirus, IDS, IPS and Custom Malware detection tools. Attackers leverage compelling social engineering techniques along with one or more zero day vulnerabilities for deploying APTs. Along with these, the recent introduction of Crypto locker and Ransom ware pose serious threats to organizations/nations as well as individuals. In this paper, we compare various machine-learning techniques used for analyzing Malwares, focusing on static analysis.
Hiran V. Nath - One of the best experts on this subject based on the ideXlab platform.
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SNDS - Static Malware Analysis Using Machine Learning Methods
Communications in Computer and Information Science, 2014Co-Authors: Hiran V. Nath, Babu M. MehtreAbstract:Malware analysis forms a critical component of cyber defense mechanism. In the last decade, lot of research has been done, using machine learning methods on both static as well as dynamic analysis. Since the aim and objective of Malware developers have changed from just for fame to political espionage or financial gain, the Malware is also getting evolved in its form, and infection methods. One of the latest form of Malware is known as targeted Malware, on which not much research has happened. Targeted Malware, which is a superset of Advanced Persistent Threat (APT), is growing in its volume and complexity in recent years. Targeted Cyber attack (through targeted Malware) plays an increasingly malicious role in disrupting the online social and financial systems. APTs are designed to steal corporate / national secrets and/or harm national/corporate interests. It is difficult to recognize targeted Malware by antivirus, IDS, IPS and Custom Malware detection tools. Attackers leverage compelling social engineering techniques along with one or more zero day vulnerabilities for deploying APTs. Along with these, the recent introduction of Crypto locker and Ransom ware pose serious threats to organizations/nations as well as individuals. In this paper, we compare various machine-learning techniques used for analyzing Malwares, focusing on static analysis.
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Static Malware Analysis Using Machine Learning Methods
Recent Trends in Computer Networks and Distributed Systems Security: Second International Conference SNDS 2014 Trivandrum India March 13-14 2014 Proce, 2014Co-Authors: Hiran V. Nath, Babu M. MehtreAbstract:Malware analysis forms a critical component of cyber defense mechanism. In the last decade, lot of research has been done, using machine learning methods on both static as well as dynamic analysis. Since the aim and objective of Malware developers have changed from just for fame to political espionage or financial gain, the Malware is also getting evolved in its form, and infection methods. One of the latest form of Malware is known as targeted Malware, on which not much research has happened. Targeted Malware, which is a superset of Advanced Persistent Threat (APT), is growing in its volume and complexity in recent years. Targeted Cyber attack (through targeted Malware) plays an increasingly malicious role in disrupting the online social and financial systems. APTs are designed to steal corporate / national secrets and/or harm national/corporate interests. It is difficult to recognize targeted Malware by antivirus, IDS, IPS and Custom Malware detection tools. Attackers leverage compelling social engineering techniques along with one or more zero day vulnerabilities for deploying APTs. Along with these, the recent introduction of Crypto locker and Ransom ware pose serious threats to organizations/nations as well as individuals. In this paper, we compare various machine-learning techniques used for analyzing Malwares, focusing on static analysis.
Gunter Ollmann - One of the best experts on this subject based on the ideXlab platform.
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the evolution of commercial Malware development kits and colour by numbers Custom Malware
Computer Fraud & Security, 2008Co-Authors: Gunter OllmannAbstract:In the last half-decade, the motivations behind Malware creation have changed considerably. What was once attributed to solitary attention-driven individuals working from their bedroom-office has now evolved into an international collective of professionally trained authors motivated by high profit. These financially driven conglomerates offer all manner of business models — buying, leasing, subscription and even pay-as-you-go Malware financial schemes — all of which are competitively priced to attract prospective patrons.
Athanasios Kalachanis - One of the best experts on this subject based on the ideXlab platform.
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Machine learning in the field of information security
2018Co-Authors: Athanasios KalachanisAbstract:The purpose of this thesis is to analyze existing machine learning application in the information security field and demonstrate a machine learning Malware classifier. At first, we’ll make a brief introduction into data science, Malware, machine learning and adversarialmachine learning. Moreover, we will concentrate on applications of machine learning systems in the cyber security field and how an attacker can evade such systems and impact the integrity, availability and confidentialityby exploiting the classifiers vulnerabilities. Finally, we present a Custom Malware classifier as a proof of concept executablefiles.
Emmanuel Tsukerman - One of the best experts on this subject based on the ideXlab platform.
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machine learning for cybersecurity cookbook
2019Co-Authors: Emmanuel TsukermanAbstract:Learn how to apply modern AI to create powerful cybersecurity solutions for Malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, Malware, data privacy, intrusion detection system(IDS) and social engineering Automate your daily workflow by addressing various security challenges using the recipes covered in the book Book Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for Malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learn Learn how to build Malware classifiers to detect suspicious activities Apply ML to generate Custom Malware to pentest your security Use ML algorithms with complex datasets to implement cybersecurity concepts Create neural networks to identify fake videos and images Secure your organization from one of the most popular threats – insider threats Defend against zero-day threats by constructing an anomaly detection system Detect web vulnerabilities effectively by combining Metasploit and ML Understand how to train a model without exposing the training data Who this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.
