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2015-05-05
Sayed, B., Traore, I..  2014.  Protection against Web 2.0 Client-Side Web Attacks Using Information Flow Control. Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on. :261-268.

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.

Raut, R.D., Kulkarni, S., Gharat, N.N..  2014.  Biometric Authentication Using Kekre's Wavelet Transform. Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on. :99-104.

This paper proposes an enhanced method for personal authentication based on finger Knuckle Print using Kekre's wavelet transform (KWT). Finger-knuckle-print (FKP) is the inherent skin patterns of the outer surface around the phalangeal joint of one's finger. It is highly discriminable and unique which makes it an emerging promising biometric identifier. Kekre's wavelet transform is constructed from Kekre's transform. The proposed system is evaluated on prepared FKP database that involves all categories of FKP. The total database of 500 samples of FKP. This paper focuses the different image enhancement techniques for the pre-processing of the captured images. The proposed algorithm is examined on 350 training and 150 testing samples of database and shows that the quality of database and pre-processing techniques plays important role to recognize the individual. The experimental result calculate the performance parameters like false acceptance rate (FAR), false rejection rate (FRR), True Acceptance rate (TAR), True rejection rate (TRR). The tested result demonstrated the improvement in EER (Error Equal Rate) which is very much important for authentication. The experimental result using Kekre's algorithm along with image enhancement shows that the finger knuckle recognition rate is better than the conventional method.
 

2015-05-04
Honghui Dong, Xiaoqing Ding, Mingchao Wu, Yan Shi, Limin Jia, Yong Qin, Lianyu Chu.  2014.  Urban traffic commuting analysis based on mobile phone data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on. :611-616.

With the urban traffic planning and management development, it is a highly considerable issue to analyze and estimate the original-destination data in the city. Traditional method to acquire the OD information usually uses household survey, which is inefficient and expensive. In this paper, the new methodology proposed that using mobile phone data to analyze the mechanism of trip generation, trip attraction and the OD information. The mobile phone data acquisition is introduced. A pilot study is implemented on Beijing by using the new method. And, much important traffic information can be extracted from the mobile phone data. We use the K-means clustering algorithm to divide the traffic zone. The attribution of traffic zone is identified using the mobile phone data. Then the OD distribution and the commuting travel are analyzed. At last, an experiment is done to verify availability of the mobile phone data, that analyzing the "Traffic tide phenomenon" in Beijing. The results of the experiments in this paper show a great correspondence to the actual situation. The validated results reveal the mobile phone data has tremendous potential on OD analysis.
 

Khosmood, F., Nico, P.L., Woolery, J..  2014.  User identification through command history analysis. Computational Intelligence in Cyber Security (CICS), 2014 IEEE Symposium on. :1-7.

As any veteran of the editor wars can attest, Unix users can be fiercely and irrationally attached to the commands they use and the manner in which they use them. In this work, we investigate the problem of identifying users out of a large set of candidates (25-97) through their command-line histories. Using standard algorithms and feature sets inspired by natural language authorship attribution literature, we demonstrate conclusively that individual users can be identified with a high degree of accuracy through their command-line behavior. Further, we report on the best performing feature combinations, from the many thousands that are possible, both in terms of accuracy and generality. We validate our work by experimenting on three user corpora comprising data gathered over three decades at three distinct locations. These are the Greenberg user profile corpus (168 users), Schonlau masquerading corpus (50 users) and Cal Poly command history corpus (97 users). The first two are well known corpora published in 1991 and 2001 respectively. The last is developed by the authors in a year-long study in 2014 and represents the most recent corpus of its kind. For a 50 user configuration, we find feature sets that can successfully identify users with over 90% accuracy on the Cal Poly, Greenberg and one variant of the Schonlau corpus, and over 87% on the other Schonlau variant.

Jing Li, Ming Chen.  2014.  On-Road Multiple Obstacles Detection in Dynamical Background. Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on. 1:102-105.

Road In this paper, we focus on both the road vehicle and pedestrians detection, namely obstacle detection. At the same time, a new obstacle detection and classification technique in dynamical background is proposed. Obstacle detection is based on inverse perspective mapping and homography. Obstacle classification is based on fuzzy neural network. The estimation of the vanishing point relies on feature extraction strategy, which segments the lane markings of the images by combining a histogram-based segmentation with temporal filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. The IPM image is computed based on the stabilized vanishing point. The method exploits the geometrical relations between the elements in the scene so that obstacle can be detected. The estimated homography of the road plane between successive images is used for image alignment. A new fuzzy decision fusion method with fuzzy attribution for obstacle detection and classification application is described. The fuzzy decision function modifies parameters with auto-adapted algorithm to get better classification probability. It is shown that the method can achieve better classification result.
 

Xiong Xu, Yanfei Zhong, Liangpei Zhang.  2014.  Adaptive Subpixel Mapping Based on a Multiagent System for Remote-Sensing Imagery. Geoscience and Remote Sensing, IEEE Transactions on. 52:787-804.

The existence of mixed pixels is a major problem in remote-sensing image classification. Although the soft classification and spectral unmixing techniques can obtain an abundance of different classes in a pixel to solve the mixed pixel problem, the subpixel spatial attribution of the pixel will still be unknown. The subpixel mapping technique can effectively solve this problem by providing a fine-resolution map of class labels from coarser spectrally unmixed fraction images. However, most traditional subpixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear subpixel, leading to incomplete and inaccurate results. To improve the subpixel mapping accuracy, this paper proposes an adaptive subpixel mapping framework based on a multiagent system for remote-sensing imagery. In the proposed multiagent subpixel mapping framework, three kinds of agents, namely, feature detection agents, subpixel mapping agents and decision agents, are designed to solve the subpixel mapping problem. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed subpixel mapping algorithm in comparison with the hard classification method and other subpixel mapping algorithms: subpixel mapping based on a back-propagation neural network and the spatial attraction model. The experimental results indicate that the proposed algorithm outperforms the other two subpixel mapping algorithms in reconstructing the different structures in mixed pixels.
 

Balakrishnan, R., Parekh, R..  2014.  Learning to predict subject-line opens for large-scale email marketing. Big Data (Big Data), 2014 IEEE International Conference on. :579-584.

Billions of dollars of services and goods are sold through email marketing. Subject lines have a strong influence on open rates of the e-mails, as the consumers often open e-mails based on the subject. Traditionally, the e-mail-subject lines are compiled based on the best assessment of the human editors. We propose a method to help the editors by predicting subject line open rates by learning from past subject lines. The method derives different types of features from subject lines based on keywords, performance of past subject lines and syntax. Furthermore, we evaluate the contribution of individual subject-line keywords to overall open rates based on an iterative method-namely Attribution Scoring - and use this for improved predictions. A random forest based model is trained to combine these features to predict the performance. We use a dataset of more than a hundred thousand different subject lines with many billions of impressions to train and test the method. The proposed method shows significant improvement in prediction accuracy over the baselines for both new as well as already used subject lines.
 

Shaobu Wang, Shuai Lu, Ning Zhou, Guang Lin, Elizondo, M., Pai, M.A..  2014.  Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction. Power Systems, IEEE Transactions on. 29:2049-2059.

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal “basis” of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.
 

Haciosman, M., Bin Ye, Howells, G..  2014.  Protecting and Identifiying Smartphone Apps Using Icmetrics. Emerging Security Technologies (EST), 2014 Fifth International Conference on. :94-98.

As web-server spoofing is increasing, we investigate a novel technology termed ICmetrics, used to identify fraud for given software/hardware programs based on measurable quantities/features. ICmetrics technology is based on extracting features from digital systems' operation that may be integrated together to generate unique identifiers for each of the systems or create unique profiles that describe the systems' actual behavior. This paper looks at the properties of the several behaviors as a potential ICmetrics features to identify android apps, it presents several quality features which meet the ICmetrics requirements and can be used for encryption key generation. Finally, the paper identifies four android apps and verifies the use of ICmetrics by identifying a spoofed app as a different app altogether.

Luque, J., Anguera, X..  2014.  On the modeling of natural vocal emotion expressions through binary key. Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European. :1562-1566.

This work presents a novel method to estimate natural expressed emotions in speech through binary acoustic modeling. Standard acoustic features are mapped to a binary value representation and a support vector regression model is used to correlate them with the three-continuous emotional dimensions. Three different sets of speech features, two based on spectral parameters and one on prosody are compared on the VAM corpus, a set of spontaneous dialogues from a German TV talk-show. The regression analysis, in terms of correlation coefficient and mean absolute error, show that the binary key modeling is able to successfully capture speaker emotion characteristics. The proposed algorithm obtains comparable results to those reported on the literature while it relies on a much smaller set of acoustic descriptors. Furthermore, we also report on preliminary results based on the combination of the binary models, which brings further performance improvements.

Kaghaz-Garan, S., Umbarkar, A., Doboli, A..  2014.  Joint localization and fingerprinting of sound sources for auditory scene analysis. Robotic and Sensors Environments (ROSE), 2014 IEEE International Symposium on. :49-54.

In the field of scene understanding, researchers have mainly focused on using video/images to extract different elements in a scene. The computational as well as monetary cost associated with such implementations is high. This paper proposes a low-cost system which uses sound-based techniques in order to jointly perform localization as well as fingerprinting of the sound sources. A network of embedded nodes is used to sense the sound inputs. Phase-based sound localization and Support-Vector Machine classification are used to locate and classify elements of the scene, respectively. The fusion of all this data presents a complete “picture” of the scene. The proposed concepts are applied to a vehicular-traffic case study. Experiments show that the system has a fingerprinting accuracy of up to 97.5%, localization error less than 4 degrees and scene prediction accuracy of 100%.

Jun-Yong Lee, Hyoung-Gook Kim.  2014.  Audio fingerprinting to identify TV commercial advertisement in real-noisy environment. Communications and Information Technologies (ISCIT), 2014 14th International Symposium on. :527-530.

This paper proposes a high-performance audio fingerprint extraction method for identifying TV commercial advertisement. In the proposed method, a salient audio peak pair fingerprints based on constant Q transform (CQT) are hashed and stored, to be efficiently compared to one another. Experimental results confirm that the proposed method is quite robust in different noise conditions and improves the accuracy of the audio fingerprinting system in real noisy environments.

Yuxi Liu, Hatzinakos, D..  2014.  Human acoustic fingerprints: A novel biometric modality for mobile security. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. :3784-3788.

Recently, the demand for more robust protection against unauthorized use of mobile devices has been rapidly growing. This paper presents a novel biometric modality Transient Evoked Otoacoustic Emission (TEOAE) for mobile security. Prior works have investigated TEOAE for biometrics in a setting where an individual is to be identified among a pre-enrolled identity gallery. However, this limits the applicability to mobile environment, where attacks in most cases are from imposters unknown to the system before. Therefore, we employ an unsupervised learning approach based on Autoencoder Neural Network to tackle such blind recognition problem. The learning model is trained upon a generic dataset and used to verify an individual in a random population. We also introduce the framework of mobile biometric system considering practical application. Experiments show the merits of the proposed method and system performance is further evaluated by cross-validation with an average EER 2.41% achieved.

Zurek, E.E., Gamarra, A.M.R., Escorcia, G.J.R., Gutierrez, C., Bayona, H., Perez, R., Garcia, X..  2014.  Spectral analysis techniques for acoustic fingerprints recognition. Image, Signal Processing and Artificial Vision (STSIVA), 2014 XIX Symposium on. :1-5.

This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.

2015-05-01
Shuai Yi, Xiaogang Wang.  2014.  Profiling stationary crowd groups. Multimedia and Expo (ICME), 2014 IEEE International Conference on. :1-6.

Detecting stationary crowd groups and analyzing their behaviors have important applications in crowd video surveillance, but have rarely been studied. The contributions of this paper are in two aspects. First, a stationary crowd detection algorithm is proposed to estimate the stationary time of foreground pixels. It employs spatial-temporal filtering and motion filtering in order to be robust to noise caused by occlusions and crowd clutters. Second, in order to characterize the emergence and dispersal processes of stationary crowds and their behaviors during the stationary periods, three attributes are proposed for quantitative analysis. These attributes are recognized with a set of proposed crowd descriptors which extract visual features from the results of stationary crowd detection. The effectiveness of the proposed algorithms is shown through experiments on a benchmark dataset.

Lu Wang, Yung, N.H.C., Lisheng Xu.  2014.  Multiple-Human Tracking by Iterative Data Association and Detection Update. Intelligent Transportation Systems, IEEE Transactions on. 15:1886-1899.

Multiple-object tracking is an important task in automated video surveillance. In this paper, we present a multiple-human-tracking approach that takes the single-frame human detection results as input and associates them to form trajectories while improving the original detection results by making use of reliable temporal information in a closed-loop manner. It works by first forming tracklets, from which reliable temporal information is extracted, and then refining the detection responses inside the tracklets, which also improves the accuracy of tracklets' quantities. After this, local conservative tracklet association is performed and reliable temporal information is propagated across tracklets so that more detection responses can be refined. The global tracklet association is done last to resolve association ambiguities. Experimental results show that the proposed approach improves both the association and detection results. Comparison with several state-of-the-art approaches demonstrates the effectiveness of the proposed approach.

Yueguo Zhang, Lili Dong, Shenghong Li, Jianhua Li.  2014.  Abnormal crowd behavior detection using interest points. Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on. :1-4.

Abnormal crowd behavior detection is an important research issue in video processing and computer vision. In this paper we introduce a novel method to detect abnormal crowd behaviors in video surveillance based on interest points. A complex network-based algorithm is used to detect interest points and extract the global texture features in scenarios. The performance of the proposed method is evaluated on publicly available datasets. We present a detailed analysis of the characteristics of the crowd behavior in different density crowd scenes. The analysis of crowd behavior features and simulation results are also demonstrated to illustrate the effectiveness of our proposed method.

Harish, P., Subhashini, R., Priya, K..  2014.  Intruder detection by extracting semantic content from surveillance videos. Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on. :1-5.

Many surveillance cameras are using everywhere, the videos or images captured by these cameras are still dumped but they are not processed. Many methods are proposed for tracking and detecting the objects in the videos but we need the meaningful content called semantic content from these videos. Detecting Human activity recognition is quite complex. The proposed method called Semantic Content Extraction (SCE) from videos is used to identify the objects and the events present in the video. This model provides useful methodology for intruder detecting systems which provides the behavior and the activities performed by the intruder. Construction of ontology enhances the spatial and temporal relations between the objects or features extracted. Thus proposed system provides a best way for detecting the intruders, thieves and malpractices happening around us.

Woon Cho, Abidi, M.A., Kyungwon Jeong, Nahyun Kim, Seungwon Lee, Joonki Paik, Gwang-Gook Lee.  2014.  Object retrieval using scene normalized human model for video surveillance system. Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on. :1-2.

This paper presents a human model-based feature extraction method for a video surveillance retrieval system. The proposed method extracts, from a normalized scene, object features such as height, speed, and representative color using a simple human model based on multiple-ellipse. Experimental results show that the proposed system can effectively track moving routes of people such as a missing child, an absconder, and a suspect after events.

Yuxi Liu, Hatzinakos, D..  2014.  Earprint: Transient Evoked Otoacoustic Emission for Biometrics. Information Forensics and Security, IEEE Transactions on. 9:2291-2301.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.

Ketenci, S., Ulutas, G., Ulutas, M..  2014.  Detection of duplicated regions in images using 1D-Fourier transform. Systems, Signals and Image Processing (IWSSIP), 2014 International Conference on. :171-174.

Large number of digital images and videos are acquired, stored, processed and shared nowadays. High quality imaging hardware and low cost, user friendly image editing software make digital mediums vulnerable to modifications. One of the most popular image modification techniques is copy move forgery. This tampering technique copies part of an image and pastes it into another part on the same image to conceal or to replicate some part of the image. Researchers proposed many techniques to detect copy move forged regions of images recently. These methods divide image into overlapping blocks and extract features to determine similarity among group of blocks. Selection of the feature extraction algorithm plays an important role on the accuracy of detection methods. Column averages of 1D-FT of rows is used to extract features from overlapping blocks on the image. Blocks are transformed into frequency domain using 1D-FT of the rows and average values of the transformed columns form feature vectors. Similarity of feature vectors indicates possible forged regions. Results show that the proposed method can detect copy pasted regions with higher accuracy compared to similar works reported in the literature. The method is also more resistant against the Gaussian blurring or JPEG compression attacks as shown in the results.