Biblio
This paper presents a framework for privacy-preserving video delivery system to fulfill users' privacy demands. The proposed framework leverages the inference channels in sensitive behavior prediction and object tracking in a video surveillance system for the sequence privacy protection. For such a goal, we need to capture different pieces of evidence which are used to infer the identity. The temporal, spatial and context features are extracted from the surveillance video as the observations to perceive the privacy demands and their correlations. Taking advantage of quantifying various evidence and utility, we let users subscribe videos with a viewer-dependent pattern. We implement a prototype system for off-line and on-line requirements in two typical monitoring scenarios to construct extensive experiments. The evaluation results show that our system can efficiently satisfy users' privacy demands while saving over 25% more video information compared to traditional video privacy protection schemes.
Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.
Present work deals with prediction of damage location in a composite cantilever beam using signal from optical fiber sensor coupled with a neural network with back propagation based learning mechanism. The experimental study uses glass/epoxy composite cantilever beam. Notch perpendicular to the axis of the beam and spanning throughout the width of the beam is introduced at three different locations viz. at the middle of the span, towards the free end of the beam and towards the fixed end of the beam. A plastic optical fiber of 6 cm gage length is mounted on the top surface of the beam along the axis of the beam exactly at the mid span. He-Ne laser is used as light source for the optical fiber and light emitting from other end of the fiber is converted to electrical signal through a converter. A three layer feed forward neural network architecture is adopted having one each input layer, hidden layer and output layer. Three features are extracted from the signal viz. resonance frequency, normalized amplitude and normalized area under resonance frequency. These three features act as inputs to the neural network input layer. The outputs qualitatively identify the location of the notch.
Face recognition has attained a greater importance in bio-metric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. The second contribution is to show empirically the performance of face recognition for two age groups. Thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (≤ 15years and ≥ 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. The obtained evaluation results shows higher recognition rate for age groups ≥ 20years than ≤ 15years, which indicates the variation in face recognition across the different age groups.
Traditional authentication techniques such as static passwords are vulnerable to replay and guessing attacks. Recently, many studies have been conducted on keystroke dynamics as a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions suffer from a numerous number of features with an insufficient number of samples which lead to a high verification error rate and high verification time. In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that supports fixed and free text samples. The proposed system utilizes the ReliefF dimensionality reduction method, as a preprocessing step, to minimize the feature space dimensionality. The proposed system applies clustering to users' profile templates to reduce the verification time. The proposed system is applied to two different benchmark datasets. Experimental results prove the effectiveness and efficiency of the proposed system.
The security and typical attack behavior of Modbus/TCP industrial network communication protocol are analyzed. The data feature of traffic flow is extracted through the operation mode of the depth analysis abnormal behavior, and the intrusion detection method based on the support vector machine (SVM) is designed. The method analyzes the data characteristics of abnormal communication behavior, and constructs the feature input structure and detection system based on SVM algorithm by using the direct behavior feature selection and abnormal behavior pattern feature construction. The experimental results show that the method can effectively improve the detection rate of abnormal behavior, and enhance the safety protection function of industrial network.
Growing numbers of ubiquitous electronic devices and services motivate the need for effortless user authentication and identification. While biometrics are a natural means of achieving these goals, their use poses privacy risks, due mainly to the difficulty of preventing theft and abuse of biometric data. One way to minimize information leakage is to derive biometric keys from users' raw biometric measurements. Such keys can be used in subsequent security protocols and ensure that no sensitive biometric data needs to be transmitted or permanently stored. This paper is the first attempt to explore the use of human body impedance as a biometric trait for deriving secret keys. Building upon Randomized Biometric Templates as a key generation scheme, we devise a mechanism that supports consistent regeneration of unique keys from users' impedance measurements. The underlying set of biometric features are found using a feature learning technique based on Siamese networks. Compared to prior feature extraction methods, the proposed technique offers significantly improved recognition rates in the context of key generation. Besides computing experimental error rates, we tailor a known key guessing approach specifically to the used key generation scheme and assess security provided by the resulting keys. We give a very conservative estimate of the number of guesses an adversary must make to find a correct key. Results show that the proposed key generation approach produces keys comparable to those obtained by similar methods based on other biometrics.
It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.
In this paper we study keystroke dynamics as an authentication mechanism for touch screen based devices. The authentication process decides whether the identity of a given person is accepted or rejected. This can be easily implemented by using a two-class classifier which operates with the help of positive samples (belonging to the authentic person) and negative ones. However, collecting negative samples is not always a viable option. In such cases a one-class classification algorithm can be used to characterize the target class and distinguish it from the outliers. We implemented an authentication test-framework that is capable of working with both one-class and two-class classification algorithms. The framework was evaluated on our dataset containing keystroke samples from 42 users, collected from touch screen-based Android devices. Experimental results yield an Equal Error Rate (EER) of 3% (two-class) and 7% (one-class) respectively.
Novice programmers exhibit a repertoire of affective states over time when they are learning computer programming. The modeling of frustration is important as it informs on the need for pedagogical intervention of the student who may otherwise lose confidence and interest in the learning. In this paper, contextual and keystroke features of the students within a Java tutoring system are used to detect frustration of student within a programming exercise session. As compared to psychological sensors used in other studies, the use of contextual and keystroke logs are less obtrusive and the equipment used (keyboard) is ubiquitous in most learning environment. The technique of logistic regression with lasso regularization is utilized for the modeling to prevent over-fitting. The results showed that a model that uses only contextual and keystroke features achieved a prediction accuracy level of 0.67 and a recall measure of 0.833. Thus, we conclude that it is possible to detect frustration of a student from distilling both the contextual and keystroke logs within the tutoring system with an adequate level of accuracy.
Continuous Authentication by analysing the user's behaviour profile on the computer input devices is challenging due to limited information, variability of data and the sparse nature of the information. As a result, most of the previous research was done as a periodic authentication, where the analysis was made based on a fixed number of actions or fixed time period. Also, the experimental data was obtained for most of the previous research in a very controlled condition, where the task and environment were fixed. In this paper, we will focus on actual continuous authentication that reacts on every single action performed by the user. The experimental data was collected in a complete uncontrolled condition from 52 users by using our data collection software. In our analysis, we have considered both keystroke and mouse usages behaviour pattern to avoid a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The result we have obtained from this research is satisfactory enough for further investigation on this domain.
In this research, we focus on context independent continuous authentication that reacts on every separate action performed by a user. The experimental data was collected in a complete uncontrolled condition from 53 users by using our data collection software. In our analysis, we considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The best result obtained from this research is that for 47 bio-metric subjects we have on average 275 actions required to detect an imposter where these biometric subjects are never locked out from the system.
Keystroke dynamics is a form of behavioral biometrics that can be used for continuous authentication of computer users. Many classifiers have been proposed for the analysis of acquired user patterns and verification of users at computer terminals. The underlying machine learning methods that use Gaussian density estimator for outlier detection typically assume that the digraph patterns in keystroke data are generated from a single Gaussian distribution. In this paper, we relax this assumption by allowing digraphs to fit more than one distribution via the Gaussian Mixture Model (GMM). We have conducted an experiment with a public data set collected in a controlled environment. Out of 30 users with dynamic text, we obtain 0.08% Equal Error Rate (EER) with 2 components by using GMM, while pure Gaussian yields 1.3% EER for the same data set (an improvement of EER by 93.8%). Our results show that GMM can recognize keystroke dynamics more precisely and authenticate users with higher confidence level.
This paper analyzes score normalization for keystroke dynamics authentication systems. Previous studies have shown that the performance of behavioral biometric recognition systems (e.g. voice and signature) can be largely improved with score normalization and target-dependent techniques. The main objective of this work is twofold: i) to analyze the effects of different thresholding techniques in 4 different keystroke dynamics recognition systems for real operational scenarios; and ii) to improve the performance of keystroke dynamics on the basis of target-dependent score normalization techniques. The experiments included in this work are worked out over the keystroke pattern of 114 users from two different publicly available databases. The experiments show that there is large room for improvements in keystroke dynamic systems. The results suggest that score normalization techniques can be used to improve the performance of keystroke dynamics systems in more than 20%. These results encourage researchers to explore this research line to further improve the performance of these systems in real operational environments.
The aim of this research is to advance the user active authentication using keystroke dynamics. Through this research, we assess the performance and influence of various keystroke features on keystroke dynamics authentication systems. In particular, we investigate the performance of keystroke features on a subset of most frequently used English words. The performance of four features such as i) key duration, ii) flight time latency, iii) diagraph time latency, and iv) word total time duration are analyzed. Two machine learning techniques are employed for assessing keystroke authentications. The selected classification methods are support vector machine (SVM), and k-nearest neighbor classifier (K-NN). The logged experimental data are captured for 28 users. The experimental results show that key duration time offers the best performance result among all four keystroke features, followed by word total time.
Botnets are considered one of the most dangerous species of network-based attack today because they involve the use of very large coordinated groups of hosts simultaneously. The behavioral analysis of computer networks is at the basis of the modern botnet detection methods, in order to intercept traffic generated by malwares for which signatures do not exist yet. Defining a pattern of features to be placed at the basis of behavioral analysis, puts the emphasis on the quantity and quality of information to be caught and used to mark data streams as normal or abnormal. The problem is even more evident if we consider extensive computer networks or clouds. With the present paper we intend to show how heuristics applied to large-scale proxy logs, considering a typical phase of the life cycle of botnets such as the search for C&C Servers through AGDs (Algorithmically Generated Domains), may provide effective and extremely rapid results. The present work will introduce some novel paradigms. The first is that some of the elements of the supply chain of botnets could be completed without any interaction with the Internet, mostly in presence of wide computer networks and/or clouds. The second is that behind a large number of workstations there are usually "human beings" and it is unlikely that their behaviors will cause marked changes in the interaction with the Internet in a fairly narrow time frame. Finally, AGDs can highlight, at the moment, common lexical features, detectable quickly and without using any black/white list.
With the increasing popularity of wearable devices, information becomes much easily available. However, personal information sharing still poses great challenges because of privacy issues. We propose an idea of Visual Human Signature (VHS) which can represent each person uniquely even captured in different views/poses by wearable cameras. We evaluate the performance of multiple effective modalities for recognizing an identity, including facial appearance, visual patches, facial attributes and clothing attributes. We propose to emphasize significant dimensions and do weighted voting fusion for incorporating the modalities to improve the VHS recognition. By jointly considering multiple modalities, the VHS recognition rate can reach by 51% in frontal images and 48% in the more challenging environment and our approach can surpass the baseline with average fusion by 25% and 16%. We also introduce Multiview Celebrity Identity Dataset (MCID), a new dataset containing hundreds of identities with different view and clothing for comprehensive evaluation.
Protecting the privacy of user-identification data is fundamental to protect the information systems from attacks and vulnerabilities. Providing access to such data only to the limited and legitimate users is the key motivation for `Biometrics'. In `Biometric Systems' confirming a user's claim of his/her identity reliably, is more important than focusing on `what he/she really possesses' or `what he/she remembers'. In this paper the use of face image for biometric access is proposed using two multistage face recognition algorithms that employ biometric facial features to validate the user's claim. The proposed algorithms use standard algorithms and classifiers such as EigenFaces, PCA and LDA in stages. Performance evaluation of both proposed algorithms is carried out using two standard datasets, the Extended Yale database and AT&T database. Results using the proposed multi-stage algorithms are better than those using other standard algorithms. Current limitations and possible applications of the proposed algorithms are also discussed along, with further scope of making these robust to pose, illumination and noise variations.
The TRECVID report of 2010 [14] evaluated video shot boundary detectors as achieving "excellent performance on [hard] cuts and gradual transitions." Unfortunately, while re-evaluating the state of the art of the shot boundary detection, we found that they need to be improved because the characteristics of consumer-produced videos have changed significantly since the introduction of mobile gadgets, such as smartphones, tablets and outdoor activity purposed cameras, and video editing software has been evolving rapidly. In this paper, we evaluate the best-known approach on a contemporary, publicly accessible corpus, and present a method that achieves better performance, particularly on soft transitions. Our method combines color histograms with key point feature matching to extract comprehensive frame information. Two similarity metrics, one for individual frames and one for sets of frames, are defined based on graph cuts. These metrics are formed into temporal feature vectors on which a SVM is trained to perform the final segmentation. The evaluation on said "modern" corpus of relatively short videos yields a performance of 92% recall (at 89% precision) overall, compared to 69% (91%) of the best-known method.
Software structure analysis is crucial in software testing. Using complex network theory, we present a series of methods and build a two-layer network model for software analysis, including network metrics calculation and features extraction. Through identifying the critical functions and reused modules, we can reduce nearly 80% workload in software testing on average. Besides, the structure network shows some interesting features that can assist to understand the software more clearly.
The main emphasis of this paper is to develop an approach able to detect and assess blindly the perceptual blur degradation in images. The idea deals with a statistical modelling of perceptual blur degradation in the frequency domain using the discrete cosine transform (DCT) and the Just Noticeable Blur (JNB) concept. A machine learning system is then trained using the considered statistical features to detect perceptual blur effect in the acquired image and eventually produces a quality score denoted BBQM for Blind Blur Quality Metric. The proposed BBQM efficiency is tested objectively by evaluating it's performance against some existing metrics in terms of correlation with subjective scores.
With the recent developments in the field of visual sensor technology, multiple imaging sensors are used in several applications such as surveillance, medical imaging and machine vision, in order to improve their capabilities. The goal of any efficient image fusion algorithm is to combine the visual information, obtained from a number of disparate imaging sensors, into a single fused image without the introduction of distortion or loss of information. The existing fusion algorithms employ either the mean or choose-max fusion rule for selecting the best features for fusion. The choose-max rule distorts constants background information whereas the mean rule blurs the edges. In this paper, Non-Subsampled Contourlet Transform (NSCT) based two feature-level fusion schemes are proposed and compared. In the first method Fuzzy logic is applied to determine the weights to be assigned to each segmented region using the salient region feature values computed. The second method employs Golden Section Algorithm (GSA) to achieve the optimal fusion weights of each region based on its Petrovic metric. The regions are merged adaptively using the weights determined. Experiments show that the proposed feature-level fusion methods provide better visual quality with clear edge information and objective quality metrics than individual multi-resolution-based methods such as Dual Tree Complex Wavelet Transform and NSCT.
Image sharpness measurements are important parts of many image processing applications. To measure image sharpness multiple algorithms have been proposed and measured in the past but they have been developed with having out-of-focus photographs in mind and they do not work so well with images taken using a digital microscope. In this article we show the difference between images taken with digital cameras, images taken with a digital microscope and artificially blurred images. The conventional sharpness measures are executed on all these categories to measure the difference and a standard image set taken with a digital microscope is proposed and described to serve as a common baseline for further sharpness measures in the field.
Online Social Networks have emerged as an interesting area for analysis where each user having a personalized user profile interact and share information with each other. Apart from analyzing the structural characteristics, detection of abnormal and anomalous activities in social networks has become need of the hour. These anomalous activities represent the rare and mischievous activities that take place in the network. Graphical structure of social networks has encouraged the researchers to use various graph metrics to detect the anomalous activities. One such measure that seemed to be highly beneficial to detect the anomalies was brokerage value which helped to detect the anomalies with high accuracy. Also, further application of the measure to different datasets verified the fact that the anomalous behavior detected by the proposed measure was efficient as compared to the already proposed measures in Oddball Algorithm.