Detection of malicious pdf files based on hierarchical document structure

The method aims to classify a malicious pdf using the hierarchical document structure. Detection of malicious pdf files based on hierarchical document structure oct 20 feb 2014. Peepdf is a python based tool which helps you to explore pdf files. Stavrou 20 malicious pdf detection using metadata and structural features, in proceedings of annual computer security applications conference acsac. The file detection test is one of the most deterministic factors to evaluate the effectiveness of an antivirus engine. We identify various features in pdf documents which are used by malware authors to construct a malicious file. The portable document format pdf is one of the most popular. Intelligent attacks using documentbased malware that exploit vulnerabilities in. Combining static and dynamic analysis for the detection of.

Laskov 20 detection of malicious pdf files based on hierarchical document structure, in ndss. Apr 24, 20 in this paper, we propose an efficient static method for detection of malicious pdf documents which relies on essential differences in the structural properties of malicious and benign pdf files. Detecting malicious documents with platform diversity meng xu and taesoo kim. Every month, office 365 atp blocks more than 500,000 email messages that use malicious html and document files that open a website with malicious content. The main claim of the researchers was that even if the attacker knows which features are responsible for the detection, due to the complexity of the pdf format. First, we extract the features of the original malicious or benign pdf files using the feature extraction component of the mimicus. Detecting malicious documents with platform diversity. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. To this end, we propose two different approaches termed topdown and. Applied sciences free fulltext malicious pdf detection model. Machine learning algorithm used for detecting malicious.

An evasion resilient approach to the detection of malicious. Pdf malicious pdf files detection using structural and javascript. Pdf detection of malware in pdf files using nicad4 tool. Its original purpose was for research and dissection of pdf based malware, but i find it useful also to investigate the structure of completely benign pdf files. In this paper we presented sfem, a structural feature extraction methodology for the detection of unknown malicious xml based documents using machine learning algorithms. In proceedings of the network and distributed system security symposium ndss.

Several malicious pdf detection tools have been proposed by the academic community to address the pdf threat. Malicious pdf files remain a real threat, in practice, to masses of computer users. A fulltextannotation is a structured hierarchical response for the utf8 text extracted from the image, organized as pagesblocksparagraphswordssymbols. An effective machine learning based approach for pdf malware detection jason zhang, ph. In spite of a series of a security patches issued by adobe and other vendors, many users still have. Static detection of malicious javascriptbearing pdf documents acm, 2011, pp. For further details please refer to the methodology documents as well as the information provided on our website. Pdf file is a hierarchical structure of objects that are logically. The proposed detection method is based on the analysis of hierarchical document structure and is henceforth abbreviated as hidost. The recent targeted attacks extensively use nonexecutable malware as a stealthy attack vector.

Mar 30, 2018 although static methods perform in orders of magnitude faster, their applicability has been limited to only specific file formats. An evasion of structural methods for malicious pdf files detection davide maiorca department of electrical and electronic engineering. We demonstrate its effectiveness on a data corpus containing about 600,000 realworld malicious and benign pdf files and evaluate its resistance against adversarial evasion attempts. Heuristic malware detection mechanism based on executable. This taxonomy is partially based on a systematic survey paper 40 with the addition of works after 20 as well as summaries parser, machine learning, and pattern dependencies and evasion techniques. In spite of a series of a security patches issued by adobe and other vendors, many users still have vulnerable client software installed on their computers. Senior threat researcher sophos, abingdon ox14 3yp, u. Malware detection in pdf files using machine learning. Hidost introduces the static machinelearning based malware detection system to operate multiple file formats like pdf or swf having hierarchical document structure. In this paper, we propose an efficient static method for detection of malicious pdf documents which relies on essential differences in the structural properties of malicious and benign pdf files.

Lstmbased hierarchical denoising network for android. This is due to its popularity as a document exchange format, the lack of user awareness of its dangers, as well as its ability to carry and execute malware. The entropy based detection method by pareek et al, 206 put forward that the level of ambiguity in malicious files is less than that of benign files. At the root of the hierarchy is the document s catalog dic tionary. Detection of malicious pdf files based on hierarchical document structure nedim srndi.

Malicious pdf files remain a real threat, in practice, to masses of computer users, even after several highprofile security incidents. Advanced methods for the detection of malicious pdf files. In this contribution we present a technique for detection of javascriptbearing malicious pdf documents based on static analysis of extracted javascript code. Attackers employ several techniques to evade file based detection of attachments and blocking of. In this paper we present a machine learning based approach for detection of malicious pdf documents. Structural feature extraction methodology for the detection of malicious office documents using machine learning methods, expert systems with applications on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at. Based on these feature set we arrive on models which is used to detect malicious pdf documents. Detection of malicious documents through deviation from file format specifications. In this section, we introduce the overview of our malware detection method, explain how to get the opcode sequence from the android application source file, and then describe how an lstm based hdn hierarchical model is designed and learned from raw opcode sequence to complete malware detection. Automatic detection of malicious pdf files using dynamic analysis ahmad bazzi1 and yoshikuni onozato2 1graduate school of engineering, gunma university, japan 2division of electronics and informatics, faculty of science and technology, gunma university, japan abstract malicious nonexecutable les are being increasingly used to break into users computers. Paper presented at the 20th network and distributed systems symposium. It is an extension of previous work published by srndic and laskov in 26, herein referred to as sl20. Advanced methods for the detection of malicious pdf files detection methods based on metadata analysis content metadata analysis smutz and stavrou presented pdfrate, a framework which is based on metafeatures extracted from a documents content for the detection of malicious pdf files the process is based on the use of a. Malicious software in form of internet worms, computer viruses, and trojan horses poses a major threat to the security of networked systems.

Detection and analysis of shellcode in malicious documents. Detection of malicious pdf files and directions for. Chair of company, foundation and trust law chair of banking and securities law. Identifying drawbacks in malicious pdf detectors request pdf. From 2007 onward, the pdf document has proven to be a successful vector for malware infections, making up 80% of all exploits found by cisco scansafe in 2009 1. How to structure your qms documentation the international standard iso 100. Bulk analysis of malicious pdf documents semantic scholar. Using the proposed structural features, a classifier of pdf documents is presented whose detection ac curacy, estimated on about 220,000 pdf documents under. Malicious pdf detection, svm, evasion attacks, gradientdescent, feature selections, adversarial learning. It extracts information from both the structure and the content of the pdf file, and it. An effective machine learning based approach for pdf malware. Ac focused on the related work done in the malicious pdf detection domain, its background and provided comprehensive explanations regarding to the pdf file s structure and the attacks that can be carried out through it, ac also participated in the collection of the pdf files malicious and benign and revising the manuscript.

Automatic detection of malicious pdf files using dynamic analysis. Generally, all objects are located within the hi erarchy. The second approach uses a series of dynamic tests on diverse platforms to open a document and execute its embedded malcode in diverse environments forcing the malcode to behave abnormally leading to its detection. About the physical and logical structure of pdf files. Detection of malicious pdf files based on hierarchical document structure. Smutz and stavrou presented pdfrate, a framework for the detection of malicious pdf files which is based on metafeatures extracted from a documents content. We demonstrate its effectiveness on a data corpus containing about 600,000 realworld malicious and benign pdf files and. A structural and content based approach for a precise and robust detection of malicious pdf files davide maiorca, davide ariu, igino corona and giorgio giacinto department of electrical and electronic engineering, university of cagliari, italy fdavide. A pattern recognition system for malicious pdf files detection springer, 2012, pp. Apr 09, 2008 in the apper i read its given that the logical structure of pdf documets can be alike n so is a good way to find malware with help of link count. We evaluated sfem using a large and representative collection of microsoft word xml based documents. The extracted features include the number of font objects, average length of stream objects, and the number of lower case characters in the title. Most of the academic work on the detection of malicious pdf is based on static analysis, because static analysis requires less computing resources and it is much faster.

We demonstrate its effectiveness on a data corpus containing about 600,000 realworld malicious and benign pdf files and evaluate its resistance. Read detection of malicious pdf files and directions for enhancements. It can be used interactively to browse the objects and streams contained in a pdf. Malicious pdf detection using metadata and structural features acm, 2012, pp. Encryption pdf documents support encryption to protect their con. Keywords malware detection, malicious pdf document, heuristics 1. A featurevector generative adversarial network for evading.

Keeping pace with the creation of new malicious pdf files using an. Heuristic malware detection mechanism based on decision trees composition as an alternative approach, in order to substantiate the possibility to build heuristic malware detection tool based on static analysis of executable les this study provides the results of classier e ectiveness evaluation based on the decision trees composition. We have developed a static approach that leverages on information extracted by both the structure and the content of pdf files, which allows to improve. Malicious pdf detection using metadata and structural features charles smutz center for secure information systems george mason university, fairfax, va 22030. It extracts information from both the structure and the content of the pdf file. Hierarchical novelty detection for visual object recognition.

Files based on hierarchical document structure, in. A structural and contentbased approach for a precise and. To process native documents, such as word or indesign files, for auto field detection, open the file using the acrobat 9 form wizard forms start form wizard and select an existing electronic document in the create or edit form dialog box. Enhancing office 365 advanced threat protection with. In order to detect and acquire unknown malicious pdf files, we implemented a static analysis approach based on the hierarchical structural. The solution proposed by this thesis is to automatically. The desire to enhance security in the face of attacks based on malicious pdf files has led to a great deal of published research in the recent years. Combining static and dynamic analysis for the detection of malicious documents. This creates problems when trying to detect nonjavascript or targeted attacks. Detection of malicious pdf files based on hierarchical. This detection method was effective, however attackers came up with evasion techniques to bypass the detection of malicious javascript. There exists a substantial body of previous work on the detection of nonexecutable malware, including static, dynamic, and combined methods. In this work, we present a novel machine learning system to the detection of malicious pdf files.

All of which suffer some drawbacks that limit its utility. The form wizard will convert the document to pdf and auto detect form fields in one step. The list includes pdf examiner, jsunpack, wepawet and gallus. These online tools automate the scanning of pdf files to identify malicious components. Most of the academic work on the detection of malicious pdf is based on static analysis, because static analysis requires less computing resources and it. A taxonomy of malicious pdf document detection techniques. Malware detection on byte streams of pdf files using. Machine learning algorithm used for detecting malicious pdf. Sembiring, penerapan teknik support vector machine untuk pendeteksian intrusi pada jaringan. Based on these feature sets, detection rate is high as compared to approaches which depends on analysis of javascript embedded in the pdf document. Malicious pdf detection using metadata and structural. Feb 01, 2015 read detection of malicious pdf files and directions for enhancements. Despite the continuous countermeasuring efforts, embedding malware in pdf documents and using it as a malware distribution mechanism is still a threat. An effective machine learning based approach for pdf.

Keeping pace with the creation of new malicious pdf files. In this paper overview of pdf file structure is provided and basic attacks that occur via pdf files are discussed. In this paper, we present a novel machine learning system for the automatic detection of malicious pdf documents. Detect malware in portable document format files pdf using. Portable document format pdf is an electronic document format and it was.

The diversity and amount of its variants severely undermine the e ectiveness of classical signature based detection. The proposed approach to construct adversarial malicious pdf files based on a fvgan is illustrated in fig. Malicious pdf detection using metadata and structural features. Creating new pdf documents is very easy and the volume of pdf documents identified as malicious has grown beyond the capabilities of security researchers to analyze by hand. These test reports are released twice a year including a false alarm test. Laskov, detection of malicious pdf files based on hierarchical document structure, in in proceedings of the network and distributed system security symposium, ndss 20, 2012. Attacks via malicious pdf files usually occur through email communications. C smutz, a stavrou, in proceedings of the 28th annual computer security applications conference. Datadriven detection of malicious document phd thesis proposal weijen li, department of computer science. In this paper, we design a convolutional neural network to tackle the malware detection on the pdf files.

Pdf detection of malicious pdf files based on hierarchical. Malicious pdf detection, svm, evasion attacks, gradientdescent, feature selections, adversarial learning abstract. Objects, file structure, document structure, and content streams. Various social engineering techniques are being used by the attackers to make users open malicious files. There are also several handy web based tools you can use for analyzing suspicious pdfs without having to install any tools. Structural feature extraction methodology for the detection of malicious office documents using machine learning methods, expert systems with applications on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Malicious pdf files detection using structural and javascript based features. We present how we used machine learning techniques to detect malicious behaviours in pdf. As an alternative to javascriptbased detection, we pro pose analyzing the structural properties of pdf documents to discriminate between malicious and benign. In this paper, we propose a highly performant static method for detection of malicious pdf documents which, instead of analyzing javascript or any other content, makes use of essential differences in the structural properties of malicious and benign pdf files. In an earlier post i outlined 6 free local tools for examining pdf files. Automatic detection of malicious pdf files using dynamic. Based on these extracted features, each pdf file is represented by a 5dimensional real vector.