Biblio
With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.
Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.
Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.
Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.
Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.
Digital images are extensively used and exchanged through internet, which gave rise to the need of establishing authorship of images. Image watermarking has provided a solution to prevent false claims of ownership of the media. Information about the owner, generally in the form of a logo, text or image is imperceptibly hid into the subject. Many transforms have been explored by the researcher community for image watermarking. Many watermarking techniques have been developed based on Singular Value Decomposition (SVD) of images. This paper analyses Singular Value Decomposition to understand its use, ability and limitations to hide additional information into the cover image for Digital Image Watermarking application.
The economic progress of the Internet of Things (IoT) is phenomenal. Applications range from checking the alignment of some components during a manufacturing process, monitoring of transportation and pedestrian levels to enhance driving and walking path, remotely observing terminally ill patients by means of medical devices such as implanted devices and infusion pumps, and so on. To provide security, encrypting the data becomes an indispensable requirement, and symmetric encryptions algorithms are becoming a crucial implementation in the resource constrained environments. Typical symmetric encryption algorithms like Advanced Encryption Standard (AES) showcases an assumption that end points of communications are secured and that the encryption key being securely stored. However, devices might be physically unprotected, and attackers may have access to the memory while the data is still encrypted. It is essential to reserve the key in such a way that an attacker finds it hard to extract it. At present, techniques like White-Box cryptography has been utilized in these circumstances. But it has been reported that applying White-Box cryptography in IoT devices have resulted in other security issues like the adversary having access to the intermediate values, and the practical implementations leading to Code lifting attacks and differential attacks. In this paper, a solution is presented to overcome these problems by demonstrating the need of White-Box Cryptography to enhance the security by utilizing the cipher block chaining (CBC) mode.
There is an inevitable trade-off between spatial and spectral resolutions in optical remote sensing images. A number of data fusion techniques of multimodal images with different spatial and spectral characteristics have been developed to generate optical images with both spatial and spectral high resolution. Although some of the techniques take the spectral and spatial blurring process into account, there is no method that attempts to retrieve an optical image with both spatial and spectral high resolution, a spectral blurring filter and a spectral response simultaneously. In this paper, we propose a new framework of spatial resolution enhancement by a fusion of multiple optical images with different characteristics based on tensor decomposition. An optical image with both spatial and spectral high resolution, together with a spatial blurring filter and a spectral response, is generated via canonical polyadic (CP) decomposition of a set of tensors. Experimental results featured that relatively reasonable results were obtained by regularization based on nonnegativity and coupling.
A multipurpose color image watermarking method is presented to provide \textcopyright protection and ownership verification of the multimedia information. For robust color image watermarking, color watermark is utilized to bring universality and immense applicability to the proposed scheme. The cover information is first converted to Red, Green and Blue components image. Each component is transformed in wavelet domain using DWT (Discrete Wavelet Transform) and then decomposition techniques like Singular Value Decomposition (SVD), QR and Schur decomposition are applied. Multiple watermark embedding provides the watermarking scheme free from error (false positive). The watermark is modified by scrambling it using Arnold transform. In the proposed watermarking scheme, robustness and quality is tested with metrics like Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). Further, the proposed scheme is compared with related watermarking schemes.
CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.
Recent studies have shown that adding explicit social trust information to social recommendation significantly improves the prediction accuracy of ratings, but it is difficult to obtain a clear trust data among users in real life. Scholars have studied and proposed some trust measure methods to calculate and predict the interaction and trust between users. In this article, a method of social trust relationship extraction based on hellinger distance is proposed, and user similarity is calculated by describing the f-divergence of one side node in user-item bipartite networks. Then, a new matrix factorization model based on implicit social relationship is proposed by adding the extracted implicit social relations into the improved matrix factorization. The experimental results support that the effect of using implicit social trust to recommend is almost the same as that of using actual explicit user trust ratings, and when the explicit trust data cannot be extracted, our method has a better effect than the other traditional algorithms.
To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.