For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack. Unfortunately, this vision hasn't come to fruition: in fact, developing and maintaining today's security machine learning systems can require engineering resources that are comparable to that of signature-based detection systems, due in part to the need to develop and continuously tune the "features" these machine learning systems look at as attacks evolve. Deep learning, a subfield of machine learning, promises to change this by operating on raw input signals and automating the process of feature design and extraction. In this paper we propose the eXpose neural network, which uses a deep learning approach we have developed to take generic, raw short character strings as input (a common case for security inputs, which include artifacts like potentially malicious URLs, file paths, named pipes, named mutexes, and registry keys), and learns to simultaneously extract features and classify using character-level embeddings and convolutional neural network. In addition to completely automating the feature design and extraction process, eXpose outperforms manual feature extraction based baselines on all of the intrusion detection problems we tested it on, yielding a 5 -10 detection rate gain at 0.1 false positive rate compared to these baselines.
A number of previous works in machine learned based behavioral detection of malware is related to automatic classification of individual file paths or registry keys. In general, previous behavioral malware detection methods have focused on making detections on the basis of sequences of observed process or operating system-level events. For example, @cite_24 proposes a logistic regression-based method for detecting malware infections based on n-grams of audit log event observations. Relatedly, @cite_3 proposes to use an anomaly detection approach on sequences of registry accesses to infer whether a host has been compromised. @cite_8 surveys a wide variety of behavioral malware detection techniques, all of which perform manual feature engineering on collections of events to infer whether or not dynamically executed binaries are malicious or benign.
@cite_24 @cite_3 @cite_8
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Most behavioral detectors of malware remain specific to a given language and platform, mostly executables for Windows. The objective of this paper is to define a generic approach for behavioral detection based on two layers respectively responsible for abstraction and detection. The abstraction layer is specific to a platform and a language. It interprets the collected instructions, API calls and arguments and classifies these operations, as well as the objects involved, according to their purpose in the malware lifecycle. The detection layer remains generic and interoperable with different abstraction components. It relies on parallel automata parsing attribute-grammars where semantic rules are used for object typing (object classification) and object binding (data-flow). Theoretical results are first given with respect to the grammatical constraints weighting on the signature construction as well as to the resulting complexity of the detection. For experimentation purposes, two abstraction components have then been developed: one processing system call traces and the other processing the VBScript interpreted language. Experimentations have provided promising detection rates, in particular for scripts (89 ), with almost no false positives. In the case of process traces, the detection rate remains significant (51 ) but could be increased by sophisticated collection tools. The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated (sandbox) environment will be automatically analyzed and will generate behavior reports. These reports will be preprocessed into sparse vector models for further machine learning (classification). The classifiers used in this research are k-Nearest Neighbors (kNN), Naive Bayes, J48 Decision Tree, Support Vector Machine (SVM), and Multilayer Perceptron Neural Network (MlP). Based on the analysis of the tests and experimental results of all the 5 classifiers, the overall best performance was achieved by J48 decision tree with a recall of 95.9 , a false positive rate of 2.4 , a precision of 97.3 , and an accuracy of 96.8 . In summary, it can be concluded that a proof-of-concept based on automatic behavior-based malware analysis and the use of machine learning techniques could detect malware quite effectively and efficiently. onitoring the behavior of program execution at run-time is widely used to differentiate benign and malicious processes executing in the host computer. Most of the existing run-time malware detection methods use the information available in Windows Application Programming Interface (API) calls. The proposed malware detection system uses the Windows API call sequence. A 3rd order Markov chain (i.e. 4-grams) is used to model the API calls. This composite feature set is provided as an input to the malware detection system to raise the final alarm. Association mining based classification is used because it yields higher detection accuracy than previous data mining based detection systems which employed Naive Bayes, Support Vector Machine and Decision Tree techniques. A minimal subset of API categories is monitored while maintaining high detection accuracy. The number of generated rules is reduced, by removing the redundant rules, to make the malware analysis efficient. The key novelty of the proposed malware detection system is the iterative learning process combined with the run-time monitoring of program execution behavior which makes this as a dynamic malware detection system. The performance of the proposed malware detection system is evaluated for accuracy of malware detection system and compared with the existing data mining based detection systems. It is inferred that the proposed malware detection system outperforms the existing malware detection systems. We address the semantic gapproblem in behavioral monitoring by using hierarchical behavior graphs to infer high-level behaviors from myriad low-level events. Our experimental system traces the execution of a process, performing data-flow analysis to identify meaningful actions such as "proxying", "keystroke logging", "data leaking", and "downloading and executing a program" from complex combinations of rudimentary system calls. To preemptively address evasive malware behavior, our specifications are carefully crafted to detect alternative sequences of events that achieve the same high-level goal. We tested eleven benign programs, variants from seven malicious bot families, four trojans, and three mass-mailing worms and found that we were able to thoroughly identify high-level behaviors across this diverse code base. Moreover, we effectively distinguished malicious execution of high-level behaviors from benign by identifying remotely-initiated actions.