Biblio

This paper argues about a new conceptual modeling language for the White-Box (WB) security analysis. In the WB security domain, an attacker may have access to the inner structure of an application or even the entire binary code. It becomes pretty easy for attackers to inspect, reverse engineer, and tamper the application with the information they steal. The basis of this paper is the 14 patterns developed by a leading provider of software protection technologies and solutions. We provide a part of a new modeling language named i-WBS (White-Box Security) to describe problems of WB security better. The essence of White-Box security problem is code security. We made the new modeling language focus on code more than ever before. In this way, developers who are not security experts can easily understand what they need to really protect.

This paper proposes an approach that combines a deep learning-based method and a traditional machine learning-based method to efficiently detect malicious requests Web servers received. The first few layers of Convolutional Neural Network for Text Classification (TextCNN) are used to automatically extract powerful semantic features and in the meantime transferable statistical features are defined to boost the detection ability, specifically Web request parameter tampering. The semantic features from TextCNN and transferable statistical features from artificially-designing are grouped together to be fed into Support Vector Machine (SVM), replacing the last layer of TextCNN for classification. To facilitate the understanding of abstract features in form of numerical data in vectors extracted by TextCNN, this paper designs trace-back functions that map max-pooling outputs back to words in Web requests. After investigating the current available datasets for Web attack detection, HTTP Dataset CSIC 2010 is selected to test and verify the proposed approach. Compared with other deep learning models, the experimental results demonstrate that the approach proposed in this paper is competitive with the state-of-the-art.

The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation.

With Android application packing technology evolving, there are more and more ways to harden APPs. Manually unpacking APPs becomes more difficult as the time needed for analyzing increase exponentially. At the beginning, the packing technology is designed to prevent APPs from being easily decompiled, tampered and re-packed. But unfortunately, many malicious APPs start to use packing service to protect themselves. At present, most of the antivirus software focus on APPs that are unpacked, which means if malicious APPs apply the packing service, they can easily escape from a lot of antivirus software. Therefore, we should not only emphasize the importance of packing, but also concentrate on the unpacking technology. Only by doing this can we protect the normal APPs, and not miss any harmful APPs at the same time. In this paper, we first systematically study a lot of DEX packing and unpacking technologies, then propose and develop a universal unpacking system, named CrackDex, which is capable of extracting the original DEX file from the packed APP. We propose three core technologies: simulation execution, DEX reassembling, and DEX restoration, to get the unpacked DEX file. CrackDex is a part of the Dalvik virtual machine, and it monitors the execution of functions to locate the unpacking point in the portable interpreter, then launches the simulation execution, collects the data of original DEX file through corresponding structure pointer, finally fulfills the unpacking process by reassembling the data collected. The results of our experiments show that CrackDex can be used to effectively unpack APPs that are packed by packing service in a universal approach without any other knowledge of packing service.