Biblio

Recent studies shows that by the end of 2016 more than 60% of Internet traffic would be running on HTTPS. In presence of secure tunnels such as HTTPS, transparent caching solutions become in vain, as the application payload is encrypted by lower level security protocols. This paper addresses this issue and provides an alternate approach, for contents caching without compromising their security. There are three parts to our proposal. First, we propose two new IP layer primitives that allow routers to differentiate between IP and ICN flows. Second, we introduce DCAR (Dual-mode Content Aware Router), which is a traditional IP router enabled to understand the proposed IP primitives. Third, design of DISCS (DCAR based Information centric Secure Content Sharing) framework is proposed that leverages DCAR to allow content object caching along with security services that are comparable to HTTPS. Finally we share details on realizing such system.

Recently, wireless home routers increasingly become smart. While these smart routers provide rich functionalities to users, they also raise security concerns. Since a smart home router may process and store personal data for users, once compromised, these sensitive information will be exposed. Unfortunately, current operating systems on home routers are far from secure. As a consequence, users are facing a difficult tradeoff between functionality and privacy risks. This paper attacks this dilemma with a novel SEAL architecture for home routers. SEAL leverages the ARM TrustZone technology to divide a conventional router OS (i.e., Linux) in a non-secure/normal world. All sensitive user data are shielded from the normal world using encryption. Modules (called applets) that process the sensitive data are located in a secure world and confined in secure sandboxes provided by a tiny secure OS. We report the system design of SEAL and our preliminary implementation and evaluation results.

The explosion in Internet-connected household devices, such as light-bulbs, smoke-alarms, power-switches, and webcams, is creating new vectors for attacking "smart-homes" at an unprecedented scale. Common perception is that smart-home IoT devices are protected from Internet attacks by the perimeter security offered by home routers. In this paper we demonstrate how an attacker can infiltrate the home network via a doctored smart-phone app. Unbeknownst to the user, this app scouts for vulnerable IoT devices within the home, reports them to an external entity, and modifies the firewall to allow the external entity to directly attack the IoT device. The ability to infiltrate smart-homes via doctored smart-phone apps demonstrates that home routers are poor protection against Internet attacks and highlights the need for increased security for IoT devices.

We present algorithmic techniques for parallel PDE solvers that leverage numerical smoothness properties of physics simulation to detect and correct silent data corruption within local computations. We initially model such silent hardware errors (which are of concern for extreme scale) via injected DRAM bit flips. Our mitigation approach generalizes previously developed "robust stencils" and uses modified linear algebra operations that spatially interpolate to replace large outlier values. Prototype implementations for 1D hyperbolic and 3D elliptic solvers, tested on up to 2048 cores, show that this error mitigation enables tolerating orders of magnitude higher bit-flip rates. The runtime overhead of the approach generally decreases with greater solver scale and complexity, becoming no more than a few percent in some cases. A key advantage is that silent data corruption can be handled transparently with data in cache, reducing the cost of false-positive detections compared to rollback approaches.

In this paper we propose Mastino, a novel defense system to detect malware download events. A download event is a 3-tuple that identifies the action of downloading a file from a URL that was triggered by a client (machine). Mastino utilizes global situation awareness and continuously monitors various network- and system-level events of the clients' machines across the Internet and provides real time classification of both files and URLs to the clients upon submission of a new, unknown file or URL to the system. To enable detection of the download events, Mastino builds a large download graph that captures the subtle relationships among the entities of download events, i.e. files, URLs, and machines. We implemented a prototype version of Mastino and evaluated it in a large-scale real-world deployment. Our experimental evaluation shows that Mastino can accurately classify malware download events with an average of 95.5% true positive (TP), while incurring less than 0.5% false positives (FP). In addition, we show the Mastino can classify a new download event as either benign or malware in just a fraction of a second, and is therefore suitable as a real time defense system.

Traditional sensitive data disclosure analysis faces two challenges: to identify sensitive data that is not generated by specific API calls, and to report the potential disclosures when the disclosed data is recognized as sensitive only after the sink operations. We address these issues by developing BidText, a novel static technique to detect sensitive data disclosures. BidText formulates the problem as a type system, in which variables are typed with the text labels that they encounter (e.g., during key-value pair operations). The type system features a novel bi-directional propagation technique that propagates the variable label sets through forward and backward data-flow. A data disclosure is reported if a parameter at a sink point is typed with a sensitive text label. BidText is evaluated on 10,000 Android apps. It reports 4,406 apps that have sensitive data disclosures, with 4,263 apps having log based disclosures and 1,688 having disclosures due to other sinks such as HTTP requests. Existing techniques can only report 64.0% of what BidText reports. And manual inspection shows that the false positive rate for BidText is 10%.

Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency–Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work.

With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework's security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.

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.

The face is the most dominant and distinct communication tool of human beings. Automatic analysis of facial behavior allows machines to understand and interpret a human's states and needs for natural interactions. This research focuses on developing advanced computer vision techniques to process and analyze facial images for the recognition of various facial behaviors. Specifically, this research consists of two parts: automatic facial landmark detection and tracking, and facial behavior analysis and recognition using the tracked facial landmark points. In the first part, we develop several facial landmark detection and tracking algorithms on facial images with varying conditions, such as varying facial expressions, head poses and facial occlusions. First, to handle facial expression and head pose variations, we introduce a hierarchical probabilistic face shape model and a discriminative deep face shape model to capture the spatial relationships among facial landmark points under different facial expressions and face poses to improve facial landmark detection. Second, to handle facial occlusion, we improve upon the effective cascade regression framework and propose the robust cascade regression framework for facial landmark detection, which iteratively predicts the landmark visibility probabilities and landmark locations. The second part of this research applies our facial landmark detection and tracking algorithms to facial behavior analysis, including facial action recognition and face pose estimation. For facial action recognition, we introduce a novel regression framework for joint facial landmark detection and facial action recognition. For head pose estimation, we are working on a robust algorithm that can perform head pose estimation under facial occlusion.

Recognition of facial expressions authenticity is quite troublesome for humans. Therefore, it is an interesting topic for the computer vision community, as the developed algorithms for facial expressions authenticity estimation may be used as indicators of deception. This paper discusses the state-of-the art methods developed for smile veracity estimation and proposes a plan of development and validation of a novel approach to automated discrimination between genuine and posed facial expressions. The proposed fully automated technique is based on the extension of the high-dimensional Local Binary Patterns (LBP) to the spatio-temporal domain and combines them with the dynamics of facial landmarks movements. The proposed technique will be validated on several existing smile databases and a novel database created with the use of a high speed camera. Finally, the developed framework will be applied for the detection of deception in real life scenarios.

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.

Face is crucial for human identity, while face identification has become crucial to information security. It is important to understand and work with the problems and challenges for all different aspects of facial feature extraction and face identification. In this tutorial, we identify and discuss four research challenges in current Face Detection/Recognition research and related research areas: (1) Unavoidable Facial Feature Alterations, (2) Voluntary Facial Feature Alterations, (3) Uncontrolled Environments, and (4) Accuracy Control on Large-scale Dataset. We also direct several different applications (spin-offs) of facial feature studies in the tutorial.

Augmented reality is poised to become a dominant computing paradigm over the next decade. With promises of three-dimensional graphics and interactive interfaces, augmented reality experiences will rival the very best science fiction novels. This breakthrough also brings in unique challenges on how users can authenticate one another to share rich content between augmented reality headsets. Traditional authentication protocols fall short when there is no common central entity or when access to the central authentication server is not available or desirable. Looks Good To Me (LGTM) is an authentication protocol that leverages the unique hardware and context provided with augmented reality headsets to bring innate human trust mechanisms into the digital world to solve authentication in a usable and secure way. LGTM works over point to point wireless communication so users can authenticate one another in a variety of circumstances and is designed with usability at its core, requiring users to perform only two actions: one to initiate and one to confirm. Users intuitively authenticate one another, using seemingly only each other's faces, but under the hood LGTM uses a combination of facial recognition and wireless localization to bootstrap trust from a wireless signal, to a location, to a face, for secure and usable authentication.

Heterogeneous face recognition aims to identify or verify person identity by matching facial images of different modalities. In practice, it is known that its performance is highly influenced by modality inconsistency, appearance occlusions, illumination variations and expressions. In this paper, a new method named as ensemble of sparse cross-modal metrics is proposed for tackling these challenging issues. In particular, a weak sparse cross-modal metric learning method is firstly developed to measure distances between samples of two modalities. It learns to adjust rank-one cross-modal metrics to satisfy two sets of triplet based cross-modal distance constraints in a compact form. Meanwhile, a group based feature selection is performed to enforce that features in the same position of two modalities are selected simultaneously. By neglecting features that attribute to "noise" in the face regions (eye glasses, expressions and so on), the performance of learned weak metrics can be markedly improved. Finally, an ensemble framework is incorporated to combine the results of differently learned sparse metrics into a strong one. Extensive experiments on various face datasets demonstrate the benefit of such feature selection especially when heavy occlusions exist. The proposed ensemble metric learning has been shown superiority over several state-of-the-art methods in heterogeneous face recognition.

Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.

In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.

In Data mining is the method of extracting the knowledge from huge amount of data and interesting patterns. With the rapid increase of data storage, cloud and service-based computing, the risk of misuse of data has become a major concern. Protecting sensitive information present in the data is crucial and critical. Data perturbation plays an important role in privacy preserving data mining. The major challenge of privacy preserving is to concentrate on factors to achieve privacy guarantee and data utility. We propose a data perturbation method that perturbs the data using fuzzy logic and random rotation. It also describes aspects of comparable level of quality over perturbed data and original data. The comparisons are illustrated on different multivariate datasets. Experimental study has proved the model is better in achieving privacy guarantee of data, as well as data utility.

Within few years, Cloud computing has emerged as the most promising IT business model. Thanks to its various technical and financial advantages, Cloud computing continues to convince every day new users coming from scientific and industrial sectors. To satisfy the various users' requirements, Cloud providers must maximize the performance of their IT resources to ensure the best service at the lowest cost. The performance optimization efforts in the Cloud can be achieved at different levels and aspects. In the present paper, we propose to introduce a fuzzy logic process in scheduling strategy for public Cloud in order to improve the response time, processing time and total cost. In fact, fuzzy logic has proven his ability to solve the problem of optimization in several fields such as data mining, image processing, networking and much more.

Over the last few decades, accessibility scenarios have undergone a drastic change. Today the way people access information and resources is quite different from the age when internet was not evolved. The evolution of the Internet has made remarkable, epoch-making changes and has become the backbone of smart city. The vision of smart city revolves around seamless connectivity. Constant connectivity can provide uninterrupted services to users such as e-governance, e-banking, e-marketing, e-shopping, e-payment and communication through social media. And to provide uninterrupted services to such applications to citizens is our prime concern. So this paper focuses on smart handoff framework for next generation heterogeneous networks in smart cities to provide all time connectivity to anyone, anyhow and anywhere. To achieve this, three strategies have been proposed for handoff initialization phase-Mobile controlled, user controlled and network controlled handoff initialization. Each strategy considers a different set of parameters. Results show that additional parameters with RSSI and adaptive threshold and hysteresis solve ping-pong and corner effect problems in smart city.

The performance of clustering is a crucial challenge, especially for pattern recognition. The models aggregation has a positive impact on the efficiency of Data clustering. This technique is used to obtain more cluttered decision boundaries by aggregating the resulting clustering models. In this paper, we study an aggregation scheme to improve the stability and accuracy of clustering, which allows to find a reliable and robust clustering model. We demonstrate the advantages of our aggregation method by running Fuzzy C-Means (FCM) clustering on Reuters-21578 corpus. Experimental studies showed that our scheme optimized the bias-variance on the selected model and achieved enhanced clustering for unstructured textual resources.

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. In order to assure safety of Chinese Train Control System (CTCS), it is necessary to ensure the operational risk is acceptable throughout its life-cycle, which requires a pragmatic risk assessment required for effective risk control. Many risk assessment techniques currently used in railway domain are qualitative, and rely on the experience of experts, which unavoidably brings in subjective judgements. This paper presents a method that combines fuzzy reasoning and analytic hierarchy process approach to quantify the experiences of experts to get the scores of risk parameters. Fuzzy reasoning is used to obtain the risk of system hazard, analytic hierarchy process approach is used to determine the risk level (RL) and its membership of the system. This method helps safety analyst to calculate overall collective risk level of system. A case study of risk assessment of CTCS system is used to demonstrate this method can give quantitative result of collective risks without much information from experts, but can support the risk assessment with risk level and its membership, which are more valuable to guide the further risk management.

Cloud and its transactions have emerged as a major challenge. This paper aims to come up with an efficient and best possible way to transfer data in cloud computing environment. This goal is achieved with the help of Soft Computing Techniques. Of the various techniques such as fuzzy logic, genetic algorithm or neural network, the paper proposes an effective method of intrusion detection using genetic algorithm. The selection of the optimized path for the data transmission proved to be effective method in cloud computing environment. Network path optimization increases data transmission speed making intrusion in network nearly impossible. Intruders are forced to act quickly for intruding the network which is quite a tough task for them in such high speed data transmission network.

In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to Łukasiewicz logic. The MaxSAT problem for a set of formulae Φ is the problem of finding an assignment to the variables in Φ that satisfies the maximum number of formulae. Three possible solutions (encodings) are proposed to the new problem: (1) Disjunctive Linear Relations (DLRs), (2)Mixed Integer Linear Programming (MILP) and (3)Weighted Constraint Satisfaction Problem (WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have numerous applications in optimization problems that involve vagueness.

Availability is one of the most important requirements in the production system. Keeping the level of high availability in Infrastructure-as-a-Service (IaaS) cloud computing is a challenge task because of the complexity of service providing. By definition, the availability can be maintain by using fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them focus on the fault detection aspect. In this paper, after a rigorous analysis on the nature of failures, we would like to introduce a technique to identified the failures occurring in IaaS system. By using fuzzy logic algorithm, this proposed technique can provide better performance in terms of accuracy and detection speed, which is critical for the cloud system.