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software Backdoor Analysis Based on Sensitive Flow Tracking and Concolic Execution

Wuhan University Journal of Natural Sciences 2016年第5期21卷 421-427页

作者： XU Xin WANG Jiajie CHENG Shaoyin ZHANG Tao JIANG Fan School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China China Information Technology Security Evaluation Center Beijing 100085 China Anhui Province Key Lab of Software in Computing and Communication Hefei 230026 Anhui China

In order to effectively detect and analyze the backdoors this paper introduces a method named Backdoor Analysis based on Sensitive flow tracking and Concolic Execution（BASEC）.BASEC uses sensitive flow tracking to effectively discover backdoor behaviors, such as stealing secret information and injecting evil data into system, with less false negatives. With concolic execution on predetermined path, the backdoor trigger condition can be extracted and analyzed to achieve high accuracy. BASEC has been implemented and experimented on several software backdoor samples widespread on the Internet, and over 90% of them can be detected. Compared with behavior-based and system-call-based detection methods, BASEC relies less on the historical sample collections, and is more effective in detecting software backdoors, especially those injected into software by modifying and recompiling source codes.

关键词： software backdoor detection data flow tracking concolic execution malware detection

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Three dimensional stratum modeling and a fast algorithm for arbitrary cutting based on half-edge structure

Three dimensional stratum modeling and a fast algorithm for ...

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2009 International Conference on Computational Intelligence and software Engineering, CiSE 2009

作者： Xi, Liu Dong, Lanfang Lu, Detang Hefei China Anhui Province Key Lab of Software in Computing and Communication School of Computer Science and Technology USTC Hefei China Institute of Engineering and Science Software USTC Hefei China

ISBN: (纸本)9781424445073

The three dimensional stratum modeling provides important basis of analysis and policy-making in areas such as Geophysics, Petroleum, Minerals, Urban construction and so on. In this paper, we firstly propose a general flow of three dimensional stratum modeling, then research stratum partition algorithm and reconstruct the surface of three dimensional stratum model (3DSM), finally program the module of the simulation and visualization of 3DSM, and propose a fast algorithm for arbitrary cutting based on half-edge structure. The result shows that the algorithm performs real-time, and the reconstructed model of it is perfect. The algorithm has general applicability in entity cutting, it can be used in 3DSM, medical treatment, city planning and other similar areas. ©2009 IEEE.

关键词： Three dimensional computer graphics

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Unpaired Multimodal Facial Expression Recognition 15th

Unpaired Multimodal Facial Expression Recognition

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15th Asian Conference on computer Vision, ACCV 2020

作者： Xia, Bin Wang, Shangfei Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China Anhui Robot Technology Standard Innovation Base Hefei China

ISBN: (纸本)9783030695408

Current works on multimodal facial expression recognition typically require paired visible and thermal facial images. Although visible cameras are readily availab.e in our daily life, thermal cameras are expensive and less prevalent. It is costly to collect a large quantity of synchronous visible and thermal facial images. To tackle this paired training data bottleneck, we propose an unpaired multimodal facial expression recognition method, which makes full use of the massive number of unpaired visible and thermal images by utilizing thermal images to construct better image representations and classifiers for visible images during training. Specifically, two deep neural networks are trained from visible and thermal images to learn image representations and expression classifiers for two modalities. Then, an adversarial strategy is adopted to force statistical similarity between the learned visible and thermal representations, and to minimize the distribution mismatch between the predictions of the visible and thermal images. Through adversarial learning, the proposed method leverages thermal images to construct better image representations and classifiers for visible images during training, without the requirement of paired data. A decoder network is built upon the visible hidden features in order to preserve some inherent features of the visible view. We also take the variability of the different images’ transferability into account via adaptive classification loss. During testing, only visible images are required and the visible network is used. Thus, the proposed method is appropriate for real-world scenarios, since thermal imaging is rare in these instances. Experiments on two benchmark multimodal expression databases and three visible facial expression databases demonstrate the superiority of the proposed method compared to state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Infrared imaging

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Test Case Generation Based on Program Invariant and Adaptive Random Algorithm

Test Case Generation Based on Program Invariant and Adaptive...

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Advances in Information Technology and Education

作者： Yuan Yuan Zeng Fanping Zhu Guanmiao Deng Chaoqiang Xiong Neng Department of Computer University of Science and Technology of China Anhui Province Key Lab of Software in Computing and Communication

Testing is a critical activity to find software errors. And choosing an effective test suite is the key problem in software testing area. Program invariant, as an attribute of program, can record the implementation state of test case very well and reveal the coverage of program data. In this paper, we integrate adaptive random testing and invariant technology, and present a new method which makes full use of the feedback information of program invariant and invalid case suite to generate the next case. Experiment results show that, compared with other similar methods, running for the same time, our method can achieve higher coverage and faster convergence speed.

关键词： Random testing Program invariant Adaptive random algorithm Test case generation

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PASS: An adaptive multi-copy routing scheme for intermittently connected mobile Ad Hoc networks

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Journal of Convergence Information Technology 2012年第14期7卷 315-323页

作者： Xu, Jia Wang, Ru Chuan Sun, Li Juan College of Computer Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Jiangsu Province Nanjing China Key Laboratory for Computer Network and Information Integration Southeast University Ministry of Education Nanjing China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

Intermittently Connected Mobile Ad Hoc Networks (ICMANs) are mobile networks in which complete source-to-destination paths do not exist most of the time. This paper proposed a Predictable Adaptive Seed Spray routing (PASS) for intermittently connected mobile Ad Hoc networks. PASS is a cost-efficient delay-bounded routing protocol without network parameters and provides acceptable and resilient service in the face of challenged environments. Simulations have shown that PASS outperforms existing schemes such as Epidemic, binary spray & wait and AMR with respect to routing cost and adaptability.

关键词： Routing protocols

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A new boundary treatment method for SPH and application in fluid simulation

A new boundary treatment method for SPH and application in f...

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Third International Conference on Information and Computing(第三届信息与计算科学国际会议 ICIC 2010)

作者： Bo Song Lanfang Dong School of computer science and technology USTC HefeiChina Anhui Province Key Lab of Software in Computing and Communication

ISBN: (纸本)9781424470815;9780769540474

Appropriate dealing with boundary conditions is very important for SPH. Current boundary treatment methods like boundary force method, ghost particle method and virtual boundary layer method, when dealing with complex boundaries, they will have to compute much on putting external particles. For resolving this problem, we propose a new method assuming part of boundary surface instead of external particles, called effective force surface, directly repels the internal particles which approach sufficiently close. We call this new method EFS method. It is implemented in the SPH model for simulating fluid in rabbit-type cup and dam-breaking. In the former case, liquid flows vividly and no internal particles penetrate boundaries. In the latter case, the result is similar to existing result. The experimental results show that EFS method can effectively and precisely deal with complex boundary conditions.

关键词： SPH boundary treatment EFS method

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Network Security Evaluation Algorithm Based on Access Level Vectors

Network Security Evaluation Algorithm Based on Access Level ...

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The 9th International Conference for Young computer Scientists(第九届国际青年计算机大会)

作者： Kai Li Naijie Gu Kun Bi Hongzhu Ji Department of Computer Science and Technology University of Science and Technology of China Hefei China 230027 Anhui Province Key Lab. of Computing and Communication Software Hefei China 230027

The attack graph, a typical model-based method, is widely used in the field of network security evaluation. The biggest disadvantage of attack graph method is its exponential growth of the state space. This paper presents an efficient algorithm based on the malefactor's access level vector in every host of the network to generate a reduced attack graph in polynomial compute complexity. In this algorithm, the state space is reduced to O(nm) , where n is the number of nodes and m is the whole number of vulnerabilities in the network. We also present a standard method to generate attack templates from the vulnerabilities.

关键词： Network security evaluation attack graph access level vector attack template.

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Personalized video emotion tagging through a topic model

Personalized video emotion tagging through a topic model

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shan Wu Shangfei Wang Zhen Gao Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781509041183

The inherent dependencies among video content, personal characteristics, and perceptual emotion are crucial for personalized video emotion tagging, but have not been thoroughly exploited. To address this, we propose a novel topic model to capture such inherent dependencies. We assume that there are several potential human factors, or "topics," that affect the personal characteristics and the personalized emotion responses to videos. During training, the proposed topic model exploits the latent space to model the relationships among personal characteristics, video content and video tagging behaviors. After learning, the proposed model can generate meaningful latent topics, which help personalized video emotion tagging. Efficient learning and inference algorithms of the model are proposed. Experimental results on the CP-QAE-I database demonstrate the effectiveness of the proposed approach in modeling complex relationships among video content, personal characteristics, and perceptual emotion, as well as its good performance in personalized video emotion.

关键词： Video emotion tagging personal characteristics topic model

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A New Anomaly Detection Method Based on Rough Set Reduction and HMM

A New Anomaly Detection Method Based on Rough Set Reduction ...

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International Conference on computer and Information Science (ACIS)

作者： Fanping Zeng Kaitao Yin Minghui Chen Xufa Wang Department of Computer University of Science and Technology Hefei Anhui China Anhui Province Key Lab of Software in Computer and Communication Hefei Anhui PR China Department of computer University of Science and Technology of China Hefei Anhui PR China

Over the past few years, anomaly detection has been an increasing concern with the rapid growth of the network security. Hidden Markov model (HMM) has been applied in various methods in intrusion detection and proved to be a good tool to model normal behaviors of privileged processes, however, one major problem with this approach is that it demands excessive computing resources and costs a long model training time, which makes it inefficient for practical intrusion detection. This paper presents a new method of bringing rough set reduction into HMM to overcome the shortcoming. The proposed approach classifies and simplifies the long observation sequence by virtue of rough set reduction, and the decision conditions obtained in rough set reduction phase could be used in further detection. The experimental results indicate that this method can promote the model training efficiency. Further-more, it is suitable for anomaly detection with high detect rate and low false alarm rate.

关键词： Hidden Markov models Intrusion detection computer networks Electronic mail software computer security Phase detection Set theory Information science Costs

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Emotion recognition through integrating EEG and peripheral signals

Emotion recognition through integrating EEG and peripheral s...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yangyang Shu Shangfei Wang Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China 230027

ISBN: (纸本)9781509041183

The inherent dependencies among multiple physiological signals are crucial for multimodal emotion recognition, but have not been thoroughly exploited yet. This paper propose to use restricted Boltzmann machine (RBM) to model such ***, the visible nodes of RBM represent EEG and peripheral physiological signals, and thus the connections between visible nodes and hidden nodes capture the intrinsic relations among multiple physiological signals. The RBM generates new representation from multiple physiological signals. Then, a support vector machine is adopted to recognize users' emotion states from the generated features. Furthermore, we extend the proposed fusion method for incomplete datas, since physiological signals are often corrupted due to artifacts. Specifically, we pre-train the RBM using all the complete data, then we update missing values and RBM parameters to minimize free energy of visible vectors using both complete and incomplete data. Experiments on two benchmark databases demonstrate the effectiveness of the proposed methods.

关键词： multimodal emotion recognition RBM EEG peripheral physiological signal missing data

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