Malicious Code Detection Research Articles

안드로이드 기반 모바일 단말 루팅 공격에 대한 이벤트 추출 기반 대응 기법

Recently, the number of attacks by malicious application has significantly increased, targeting Android-platform mobile terminal such as Samsung Galaxy Note and Galaxy Tab 10.1. The malicious application can be distributed to currently used mobile devices through open market masquerading as an normal application. An attacker inserts malicious code into an application, which might threaten privacy by rooting attack. Once the rooting attack is successful, malicious code can collect and steal private data stored in mobile terminal, for example, SMS messages, contacts list, and public key certificate for banking. To protect the private information from the malicious attack, malicious code detection, rooting attack detection and countermeasure method are required. To meet this end, this paper investigates rooting attack mechanism for Android-platform mobile terminal. Based on that, this paper proposes countermeasure system that enables to extract and collect events related to attacks occurring from mobile terminal, which contributes to active protection from malicious attacks.

유한 오토마타를 이용한 악성코드 실시간 분석 시스템에 관한 연구

최근에 인터넷 환경에서 악성코드를 이용한 사이버 공격이 문제가 되고 있으며, 악성코드로 인한 피해가 점차 심각해지고 규모도 증가하고 있는 추세이다. 그리고 새로운 악성코드의 출현과 더불어 기존의 악성코드를 이용한 변종 역시 커다란 피해를 주고 있다. 본 논문에서는 악성코드 분석방법에서 악성코드라고 의심되어지는 파일을 보다 정확하게 판단하기 위해 악성행위에 대해서 유한 오토마타(Finite Automata) 기법을 이용한 프로파일링 기법을 도입하여 수동이 아닌 자동으로 실시간 악성코드를 분석할 수 있는 효과적인 방법을제 안하고자 한다. 파일내부에서 사용되는 함수들을 유한 오토마타로 표현하여 상호 관계 및 연관성을 파악하여 해당 파일에 대한 악성코드 여부와 정상파일 여부를 실시간적으로 분석할 수 있는 실시간 악성코드 분석 시스템(Realtime Malware Analysis System)을 제안한다. In the recent years, cyber attacks by malicious codes called malware has become a social problem. With the explosive appearance and increase of new malware, innumerable disasters caused by metaphoric malware using the existing malicious codes have been reported. To secure more effective detection of malicious codes, in other words, to make a more accurate judgment as to whether suspicious files are malicious or not, this study introduces the malware analysis system, which is based on a profiling technique using the Finite Automata. This new analysis system enables realtime automatic detection of malware with its optimized partial execution method. In this paper, the functions used within a file are expressed by finite automata to find their correlation, and a realtime malware analysis system enabling us to give an immediate judgment as to whether a file is contaminated by malware is suggested.

A graph mining approach for detecting unknown malwares

Nowadays malware is one of the serious problems in the modern societies. Although the signature based malicious code detection is the standard technique in all commercial antivirus softwares, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new malwares (unknown malwares). Since most of malwares have similar behavior, a behavior based method can detect unknown malwares. The behavior of a program can be represented by a set of called API's (application programming interface). Therefore, a classifier can be employed to construct a learning model with a set of programs' API calls. Finally, an intelligent malware detection system is developed to detect unknown malwares automatically. On the other hand, we have an appealing representation model to visualize the executable files structure which is control flow graph (CFG). This model represents another semantic aspect of programs. This paper presents a robust semantic based method to detect unknown malwares based on combination of a visualize model (CFG) and called API's. The main contribution of this paper is extracting CFG from programs and combining it with extracted API calls to have more information about executable files. This new representation model is called API-CFG. In addition, to have fast learning and classification process, the control flow graphs are converted to a set of feature vectors by a nice trick. Our approach is capable of classifying unseen benign and malicious code with high accuracy. The results show a statistically significant improvement over n-grams based detection method.

Detecting unknown malicious code by applying classification techniques on OpCode patterns

In previous studies classification algorithms were employed successfully for the detection of unknown malicious code. Most of these studies extracted features based on byte n-gram patterns in order to represent the inspected files. In this study we represent the inspected files using OpCode n-gram patterns which are extracted from the files after disassembly. The OpCode n-gram patterns are used as features for the classification process. The classification process main goal is to detect unknown malware within a set of suspected files which will later be included in antivirus software as signatures. A rigorous evaluation was performed using a test collection comprising of more than 30,000 files, in which various settings of OpCode n-gram patterns of various size representations and eight types of classifiers were evaluated. A typical problem of this domain is the imbalance problem in which the distribution of the classes in real life varies. We investigated the imbalance problem, referring to several real-life scenarios in which malicious files are expected to be about 10% of the total inspected files. Lastly, we present a chronological evaluation in which the frequent need for updating the training set was evaluated. Evaluation results indicate that the evaluated methodology achieves a level of accuracy higher than 96% (with TPR above 0.95 and FPR approximately 0.1), which slightly improves the results in previous studies that use byte n-gram representation. The chronological evaluation showed a clear trend in which the performance improves as the training set is more updated.

Detection of Cross Site Scripting Attack in Wireless Networks Using n-Gram and SVM

Large parts of attacks targeting the web are aiming at the weak point of web application. Even though SQL injection, which is the form of XSS (Cross Site Scripting) attacks, is not a threat to the system to operate the web site, it is very critical to the places that deal with the important information because sensitive information can be obtained and falsified. In this paper, the method to detect themalicious SQL injection script code which is the typical XSS attack using n-Gram indexing and SVM (Support Vector Machine) is proposed. In order to test the proposed method, the test was conducted after classifying each data set as normal code and malicious code, and the malicious script code was detected by applying index term generated by n-Gram and data set generated by code dictionary to SVM classifier. As a result, when the malicious script code detection was conducted using n-Gram index term and SVM, the superior performance could be identified in detecting malicious script and the more improved results than existing methods could be seen in the malicious script code detection recall.

Feature representation and selection in malicious code detection methods based on static system calls

Currently almost all static methods for detecting malicious code are signature-based, this leads the result that viruses can easily escape detection by simple mechanisms such as code obfuscation. In this paper, a behavior-based detection approach is proposed to address this problem. The behaviors of interest are defined as static system call sequences. Unlike the traditional approach, which derives system call sequences by running executables (i.e., dynamic system call sequences), this approach statically analyzes binary code to derive system call sequences. In this paper, a method for deriving static system call sequences is presented, and two automatic feature-selection methods based on n-grams are proposed. We use machine-learning methods, including the K-nearest neighbor, Support Vector Machine, and decision tree methods to classify executables. The proposed approach is compared with the dynamic detection approach using dynamic system call sequences. The experimental results show that the proposed approach has higher accuracy and a lower false positive rate than the dynamic detection approach.

Malicious Codes Detection Inspired by MHC

More and more malicious codes spread faster than ever and make the traditional anti-virus technology based on virus signatures to encounter unprecedented difficulties. People try to seek inspirations from Nature. The biology immune system provides a novel idea for malicious codes detection. Inspired by the features of MHC (Major Histocompatibility Complex) in the biological immune system, we propose an MHC-inspired method for malicious codes detection to effectively detect previously unknown malicious codes. The antibody (detector) consists of the constant region and the variable region. The constant region, filled with MHC genes, is to preserve outstanding antibody genes. The variable region, composed of other antibodies genes fragments, is to improve the antibody diversity by mutation. The dynamic evolution of self and nonself, the presentation of antigen, and the generation of antibody are discussed. The experiments are conducted and the results indicate that the proposed method has relatively higher detection rate of unknown malicious codes than AISCSA, a typical immunity-based method for malicious codes detection.

Unknown Malicious Code Detection Based on Bayesian

Analyzed Bayesian classifier with string, n-gram and API as features, we found that it is very difficult to improve Bayesian classifier detection accuracy because selected features are not completely independent. In order to solve this problem, we propose a new improved choose features method which are most representative properties, and show that our method achieve high detection rates, even on completely new, previously unseen malicious executables.

악성코드 포렌식을 위한 패킹 파일 탐지에 관한 연구

악성코드 사고 조사에서 가장 중요한 것은 신속하게 악성코드를 탐지하고 수집하는 것이다. 기존의 조사 방법은 시그니쳐 기반의 안티바이러스 소프트웨어를 이용하는 것이다. 시그니쳐 기반의 탐지는 실행파일 패킹, 암호화 등을 통해 쉽게 탐지를 회피할 수 있다. 그렇기 때문에 악성코드 조사에서 패킹을 탐지하는 것도 중요한 일이다. 패킹탐지는 패킹 시그니쳐 기반과 엔트로피 기반의 탐지 방법이 있다. 패킹 시그니쳐기반의 탐지는 새로운 패킹을 탐지하지 못하는 문제가 있다. 그리고 엔트로피 기반의 탐지 방법은 오탐의 문제가 존재한다. 본 논문에서는 진입점 섹션의 엔트로피 통계와 패킹의 필수적인 특징인 'write' 속성을 이용하여 패킹을 탐지하는 기법을 제시한다. 그리고 패킹 PE 파일을 탐지하는 도구를 구현하고 도구의 성능을 평가한다. In malware accident investigation, the most important thing is detection of malicious code. Signature based anti-virus softwares have been used in most of the accident. Malware can easily avoid signature based detection by using packing or encryption method. Because of this, packed file detection is also important. Detection methods can be divided into signature based detection and entropy based detection. Signature based detection can not detect new packing. And entropy based detection has a problem with false positive. We provides detection method using entropy statistics of entry point section and 'write' properties of essential characteristic of packed file. And then, we show packing detection tool and evaluate its performance.

Unknown malcode detection and the imbalance problem

The recent growth in network usage has motivated the creation of new malicious code for various purposes. Today’s signature-based antiviruses are very accurate for known malicious code, but can not detect new malicious code. Recently, classification algorithms were used successfully for the detection of unknown malicious code. But, these studies involved a test collection with a limited size and the same malicious: benign file ratio in both the training and test sets, a situation which does not reflect real-life conditions. We present a methodology for the detection of unknown malicious code, which examines concepts from text categorization, based on n-grams extraction from the binary code and feature selection. We performed an extensive evaluation, consisting of a test collection of more than 30,000 files, in which we investigated the class imbalance problem. In real-life scenarios, the malicious file content is expected to be low, about 10% of the total files. For practical purposes, it is unclear as to what the corresponding percentage in the training set should be. Our results indicate that greater than 95% accuracy can be achieved through the use of a training set that has a malicious file content of less than 33.3%.

Neural network methods for the detection of malicious code in the code objects

В роботі проаналізовані сучасні проблеми антивірусних компаній і проблеми пов'язані з виявленням модифікованого шкідливого коду. Розроблено метод детектування шкідливого коду за допомогою евристичного аналізатора на основі нейронної мережі. Спроектована модель системи прийняття рішень, та проведено експериментальні дослідження з детектування шкідливих об'єктів

Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

This research synthesizes a taxonomy for classifying detection methods of new malicious code by Machine Learning (ML) methods based on static features extracted from executables. The taxonomy is then operationalized to classify research on this topic and pinpoint critical open research issues in light of emerging threats. The article addresses various facets of the detection challenge, including: file representation and feature selection methods, classification algorithms, weighting ensembles, as well as the imbalance problem, active learning, and chronological evaluation. From the survey we conclude that a framework for detecting new malicious code in executable files can be designed to achieve very high accuracy while maintaining low false positives (i.e. misclassifying benign files as malicious). The framework should include training of multiple classifiers on various types of features (mainly OpCode and byte n-grams and Portable Executable Features), applying weighting algorithm on the classification results of the individual classifiers, as well as an active learning mechanism to maintain high detection accuracy. The training of classifiers should also consider the imbalance problem by generating classifiers that will perform accurately in a real-life situation where the percentage of malicious files among all files is estimated to be approximately 10%.

A scalable multi-level feature extraction technique to detect malicious executables

We present a scalable and multi-level feature extraction technique to detect malicious executables. We propose a novel combination of three different kinds of features at different levels of abstraction. These are binary n-grams, assembly instruction sequences, and Dynamic Link Library (DLL) function calls; extracted from binary executables, disassembled executables, and executable headers, respectively. We also propose an efficient and scalable feature extraction technique, and apply this technique on a large corpus of real benign and malicious executables. The above mentioned features are extracted from the corpus data and a classifier is trained, which achieves high accuracy and low false positive rate in detecting malicious executables. Our approach is knowledge-based because of several reasons. First, we apply the knowledge obtained from the binary n-gram features to extract assembly instruction sequences using our Assembly Feature Retrieval algorithm. Second, we apply the statistical knowledge obtained during feature extraction to select the best features, and to build a classification model. Our model is compared against other feature-based approaches for malicious code detection, and found to be more efficient in terms of detection accuracy and false alarm rate.

개념 그래프 기반의 효율적인 악성 코드 탐지 기법

개념 그래프 기반의 효율적인 악성 코드 탐지 기법