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The proper interpretation of the malware API call sequence plays a crucial role in identifying its malicious intent. Moreover, there is a necessity to characterize smart malware mimicry activities that resemble goodware programs. Those types of malware imply further challenges in recognizing their malicious activities. In this paper, we propose a standard and straightforward contextual behavioral models that characterize Windows malware and goodware. We relied on the word embedding to realize the contextual association that may occur between API functions in malware sequences. Our empirical results proved that there is a considerable distinction between malware and goodware call sequences. Based on that distinction, we propose a new method to detect malware that relies on the Markov chain. We also propose a heuristic method that identifies malware’s mimicry activities by tracking the likelihood behavior of a given API call sequence. Experimental results showed that our proposed model outperforms other peer models that rely on API call sequences. Our model returns an average malware detection accuracy of 0.990, with a false positive rate of 0.010. Regarding malware mimicry, our model shows an average noteworthy accuracy of 0.993 in detecting false positives.
With the rapid development in computers and Internet technology, malicious programs (malware) also have significantly developed in both categories and quantities. Researchers have centered their attention on inventing diversity malware detection methods to relieve the expeditiously growing malware rate. Generally, malware detection methods are categorized into either static or dynamic [1]. In static malware detection, researchers usually check and analyze portable executable (PE) files’ contents without executing the malware samples.
Throughout the static analysis, analyzers investigated PE files by collecting and extracting specific features such as string patterns, operation code (op-code) sequences, and byte sequences. The features collected during static analysis are generally viewed as discriminating features that are used to decide whether a given sample is malicious or not [2]. Nevertheless, static malware detection methods have shown to be inappropriate to overcome the skillful techniques used by malware authors to bypass detection [3, 4, 5].
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In contrast to static analysis, dynamic analysis tools are used to monitor the malware during execution. Through observing malware in real-time, we can extract valuable behavior features such as network behavior, system calls, registry change, and memory usage [6].
The Application Programming Interface (API) call sequences are viewed to be a distinguishable representative features in behavioral-based malware analysis [7]. The reason behind its prominence is because API call analysis can uncover and capture the malware behavior. Those types of real behaviors are not attainable in static analysis. Therefore, dynamic analysis research works relied on real-time features such as API call sequence as well as control flow that reveal malicious malware behavior [8]. However, dynamic analysis approaches are also insufficient. It was reported in [9] that brilliant malware can discover whether it runs on a virtual or real environment.
One of the most smart malware approaches to avoid exposure is through behaving as normal or benign executable files. This kind of mimicry behavior became a real challenge to malware detection tools. It is natural to think that the most common malware attacks (especially for Windows operating systems) are formed using executable files, however, security reports [10] showed that the wildest serious attacks are the ones that are carried out using mimicry infections. Those types of infections allow attackers to exploit the vulnerabilities of third-party applications to trigger executable payloads. Another quandary is regarded due to the vulnerabilities of third-party applications that are not promptly patched. Therefore, the late or absence of proper security updates increases much longer the lifespan of attacks committed by mimicry infections.
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Machine learning-based techniques have been used to detect malicious parts that are embedded in infected user applications such as PDF files. Research work demonstrated the effectiveness of learning-based systems at detecting obfuscated attacks that are capable of circumventing plain heuristics [11, 12, 13]; however, the problem still requires significant work to resolve.
Malware analysis tools should also pay attention to non-executable files that seem to behave benignly. Nevertheless, they conceal malicious code which makes their detection significantly harder. Although their imperfection, dynamic analysis is prospectively able to conquest some benchmark metrics. Those metrics are determined during malware interactions with the subsidiary operating system. Those metrics can be used to detect a possible attack [14].
In this work, we exploited the contextual embedding features in the API call sequence. Through modeling the transitions existing in the calling sequence, we generated behavioral models for malware and goodware. Although malicious and non-malicious applications are using the same API functions, we proved that there are variations in how both types utilized the API functions. We also propose a solution to detect Windows malware and malware mimicry or fake goodware programs.
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We organized the rest of the paper as follows: Section 2 discusses the related work and other research backgrounds. In Section 3, we present our proposed malware detection model. The datasets, along with the empirical evaluations of our model, are presented in Section 4. Section 5 concludes this paper.
Many studies aimed to analyze malware characteristics. The most leading way to analyze malware is through monitoring its behavior. One of the leading approaches to perceive the program behavior is through tracking its API calls [15, 16]. API functions are standard by themselves; there are no groups called malicious or non-malicious functions. Malicious applications also utilize the regular API functions to perform its harmful activities. The calling mechanism to API functions does not characterize the difference between malicious and normal programs. Although, the flow order of API calls may lead to the contextual behavioral characteristic of the calling process [17]. However, due to the vast amount of API functions, it becomes laborious to describe running processes’ behavioral attributes by monitoring and tracing all APIs simultaneously.
The API calling sequence that takes place among the processes and the operating system is considered influential. Hence, it is viewed as a fundamental distinction between the behavior of malicious and normal processes [3]. Therefore, most research work in malware analysis tried to understand the process behavior through analyzing API calls [18]. The order of functions in the calling sequences could lead to meaningful expressions that provide reliable malware recognition. The API calls encode sufficient information regarding the possible malware functionalities that happen throughout malware execution.
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Popular machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Naive Bayes (NB) are widely used in malware detection [19, 20, 21, 22]. Conventional machine learning algorithms are potentially able to learn behavioral features from malware samples. However, the performance of any machine learning algorithm is determined by the accuracy of the extracted features. In addition, it is also troublesome to extract significant behavioral features to improve detection performance. Therefore, common machine learning algorithms seem discouraging for malware detection [23, 24].
Lu et al. [25] and Wu et al. [26] converted API calls into regular expression (RE) rules to identify and extract malicious sequence patterns. They recognized any malicious sequence as malware when any match exists between the observed API call sequence and predefined RE rules. Taejin et al. [27] transformed API calls into some code arrangements and grouped the APIs using n-gram. Tran et al. [28] used natural language processing to analyze the API call sequence. They divided the long sequence calls into small chunks using approaches like n-gram. The resultant n-grams were assigned values by using the term frequency-inverse document frequency (TF-IDF).
The main objective of using TF-IDF is to transform the textual n-grams into numerical features to enable the application of machine learning algorithms. However, statistical approaches like TF-IDF do not conserve any contextual association that exists among words [29, 30]. Consequently, in our work, we employed the word embedding on the API calling sequences to infer the contextual association among the API calls.
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Despite the accuracy of machine learning-based models for malware detection, researchers getting more suspicions about the reliability of learning algorithms against malware mimicry attacks [31, 32, 33, 34]. These types of attacks became quite popular,
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