Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g., system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released ("zero-day") malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from ablation study.
@cite_29 propose a feature representation associating the API call sequence with their arguments. It assigns each argument to bind with its API call to form a new sequence, However, this approach leads to an extremely long feature vector and might lose the pattern of API call sequence. @cite_27 propose another two feature representations. These representations consist of first 200 API calls as well as its argument". However, this argument" only indicates whether this API call is connected with the later one and it might not maintain sufficient information from arguments.
@cite_27 @cite_29
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Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bag-of-words (i.e., keyword matching or word-to-word alignment) and lack a deep understanding of the semantics of the query. We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bags-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches. Modern programming frameworks come with large libraries, with diverse applications such as for matching regular expressions, parsing XML files and sending email. Programmers often use search engines such as Google and Bing to learn about existing APIs. In this paper, we describe SWIM, a tool which suggests code snippets given API-related natural language queries such as "generate md5 hash code". The query does not need to contain framework-specific trivia such as the type names or methods of interest. We translate user queries into the APIs of interest using clickthrough data from the Bing search engine. Then, based on patterns learned from open-source code repositories, we synthesize idiomatic code describing the use of these APIs. We introduce structured call sequences to capture API-usage patterns. Structured call sequences are a generalized form of method call sequences, with if-branches and while-loops to represent conditional and repeated API usage patterns, and are simple to extract and amenable to synthesis. We evaluated swim with 30 common C# API-related queries received by Bing. For 70 of the queries, the first suggested snippet was a relevant solution, and a relevant solution was present in the top 10 results for all benchmarked queries. The online portion of the workflow is also very responsive, at an average of 1.5 seconds per snippet. Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bags-of-words and lack a deep understanding of the semantics of the query. We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bag-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches. Reusing APIs of existing libraries is a common practice during software development, but searching suitable APIs and their usages can be time-consuming [6]. In this paper, we study a new and more practical approach to help users find usages of APIs given only simple text phrases, when users have limited knowledge about an API library. We model API invocations as an API graph and aim to find an optimum connected subgraph that meets users' search needs. The problem is challenging since the search space in an API graph is very huge. We start with a greedy subgraph search algorithm which returns a connected subgraph containing nodes with high textual similarity to the query phrases. Two refinement techniques are proposed to improve the quality of the returned subgraph. Furthermore, as the greedy subgraph search algorithm relies on online query of shortest path between two graph nodes, we propose a space-efficient compressed shortest path indexing scheme that can efficiently recover the exact shortest path. We conduct extensive experiments to show that the proposed subgraph search approach for API recommendation is very effective in that it boosts the average F1-measure of the state-of-the-art approach, Portfolio [15], on two groups of real-life queries by 64 and 36 respectively.