An Approach Ahead Product Counterfeiting Identification for BIRTHMARKS in Light of DYKIS

Programming skin pigmentation will be an exceptional trademark of a project. Thus, thinking about the birthmarks between those plaintiff What's more respondent projects gives a compelling methodology for programming counterfeiting identification. However, programming skin pigmentation era appearances two principle challenges: the non attendance of source book What's more different code confusion systems that endeavour should shroud the aspects of a system. We recommend another sort for product skin pigmentation known as progressive magic direction book grouping (DYKIS) that might a chance to be concentrated from an executable without the have for source book. Those counterfeiting identification calculation In view of our new birthmarks will be versatile to both powerless confusion strategies for example, compiler optimizations and solid confusion systems executed clinched alongside instruments for example, such that sand mark, allatori What's more upx. We recommended an instrument known as DYKIS-PD (DYKIS counterfeiting identification tool) Furthermore require on direct examinations ahead vast number about double projects

A malware instruction set for behavior-based analysis

We introduce a new representation for monitored behavior of malicious software called Malware Instruction Set (MIST). The representation is optimized for effective and efficient analysis of behavior using data mining and machine learning techniques. It can be obtained automatically during analysis of malware with a behavior monitoring tool or by converting existing behavior reports. The representation is not restricted to a particular monitoring tool and thus can also be used as a meta language to unify behavior reports of different sources

Learning More About the Underground Economy : A Case-Study of Keyloggers and Dropzones

We study an active underground economy that trades stolen digital credentials.We present a method with which it is possible to directly analyze the amount of data harvested through these types of attacks in a highly automated fashion. We exemplify this method by applying it to keylogger-based stealing of credentials via dropzones, anonymous collection points of illicitly collected data. Based on the collected data from more than 70 dropzones, we present the first empirical study of this phenomenon, giving many first-hand details about the attacks that were observed during a seven-month period between April and October 2008. This helps us better understand the nature and size of these quickly emerging underground marketplaces

Resilient self-debugging software protection

Debuggers are a popular reverse engineering and tampering tool. Self-debugging is an effective technique for applications to defend themselves against hostile debuggers. In penetration tests on state-of-the-art self-debugging, we observed several opportunities through which it could be attacked, however. We therefore improved upon the existing technique, making it more resilient by introducing reciprocal debugging and making the transfers of control between protected application and self-debugger more stealthy. This paper presents the improved self-debugging design, and details our research efforts into realizing reciprocal debugging. In our evaluation we show that the improved design is significantly harder for attackers to defeat

Detecting Obfuscated Scripts With Machine Learning Techniques

Complex operating system administration tasks can be automated and simplified by using scripting languages. For the Windows operating system, one of the most commonly used scripting languages is PowerShell. The PowerShell scripting language provides vast functionality for the system administrators. At the same time, it leaves a large attack surface for adversaries to bypass the OS protections. Signature and supervised machine learning based intrusion detection systems (IDS) can be used for monitoring and detecting such malicious scripts. However, the detection can be evaded by obfuscating the scripts. As the next step in the defense, we can use obfuscation itself as a reliable sign of malicious code. This thesis investigates the methods of detecting obfuscated PowerShell scripts with machine learning (ML) techniques. We trained the logistic regression, random forest and gradient boosting models on a balanced dataset. To generate the dataset, unobfuscated scripts were taken from open-source projects and they were obfuscated by open-source obfuscators. We then selected the most important independent features for obfuscation detection. The ML methods were compared using their ROC curves and AUC values. The best method turns out to be the gradient boosting model, which has the AUC close to one for the used dataset. Moreover, the model can classify a script faster than in one millisecond. Thus, the model can replace existing approaches to obfuscation detection, and it can be used by antivirus vendors in the process of detecting malicious PowerShell scripts

Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning

International audienceThe ability to efficiently detect the software protections used is at a prime to facilitate the selection and application of adequate deob-fuscation techniques. We present a novel approach that combines semantic reasoning techniques with ensemble learning classification for the purpose of providing a static detection framework for obfuscation transformations. By contrast to existing work, we provide a methodology that can detect multiple layers of obfuscation, without depending on knowledge of the underlying functionality of the training-set used. We also extend our work to detect constructions of obfuscation transformations, thus providing a fine-grained methodology. To that end, we provide several studies for the best practices of the use of machine learning techniques for a scalable and efficient model. According to our experimental results and evaluations on obfuscators such as Tigress and OLLVM, our models have up to 91% accuracy on state-of-the-art obfuscation transformations. Our overall accuracies for their constructions are up to 100%