Visible to the public Biblio

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2020-08-03
Qin, Xinghong, Li, Bin, Huang, Jiwu.  2019.  A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2617–2621.
Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method.
2020-07-03
Jia, Guanbo, Miller, Paul, Hong, Xin, Kalutarage, Harsha, Ban, Tao.  2019.  Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :458—465.

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.

2020-04-20
Xiao, Tianrui, Khisti, Ashish.  2019.  Maximal Information Leakage based Privacy Preserving Data Disclosure Mechanisms. 2019 16th Canadian Workshop on Information Theory (CWIT). :1–6.
It is often necessary to disclose training data to the public domain, while protecting privacy of certain sensitive labels. We use information theoretic measures to develop such privacy preserving data disclosure mechanisms. Our mechanism involves perturbing the data vectors to strike a balance in the privacy-utility trade-off. We use maximal information leakage between the output data vector and the confidential label as our privacy metric. We first study the theoretical Bernoulli-Gaussian model and study the privacy-utility trade-off when only the mean of the Gaussian distributions can be perturbed. We show that the optimal solution is the same as the case when the utility is measured using probability of error at the adversary. We then consider an application of this framework to a data driven setting and provide an empirical approximation to the Sibson mutual information. By performing experiments on the MNIST and FERG data sets, we show that our proposed framework achieves equivalent or better privacy than previous methods based on mutual information.
2020-02-17
Wen, Jinming, Yu, Wei.  2019.  Exact Sparse Signal Recovery via Orthogonal Matching Pursuit with Prior Information. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :5003–5007.
The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝn from linear model y = Ax, where A ∈ ℝm×n is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.
2020-02-10
Sharifzadeh, Mehdi, Aloraini, Mohammed, Schonfeld, Dan.  2019.  Quantized Gaussian Embedding Steganography. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2637–2641.

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.

2020-01-13
Farzaneh, Behnam, Montazeri, Mohammad Ali, Jamali, Shahram.  2019.  An Anomaly-Based IDS for Detecting Attacks in RPL-Based Internet of Things. 2019 5th International Conference on Web Research (ICWR). :61–66.
The Internet of Things (IoT) is a concept that allows the networking of various objects of everyday life and communications on the Internet without human interaction. The IoT consists of Low-Power and Lossy Networks (LLN) which for routing use a special protocol called Routing over Low-Power and Lossy Networks (RPL). Due to the resource-constrained nature of RPL networks, they may be exposed to a variety of internal attacks. Neighbor attack and DIS attack are the specific internal attacks at this protocol. This paper presents an anomaly-based lightweight Intrusion Detection System (IDS) based on threshold values for detecting attacks on the RPL protocol. The results of the simulation using Cooja show that the proposed model has a very high True Positive Rate (TPR) and in some cases, it can be 100%, while the False Positive Rate (FPR) is very low. The results show that the proposed model is fully effective in detecting attacks and applicable to large-scale networks.
2019-10-15
Panagiotakis, C., Papadakis, H., Fragopoulou, P..  2018.  Detection of Hurriedly Created Abnormal Profiles in Recommender Systems. 2018 International Conference on Intelligent Systems (IS). :499–506.

Recommender systems try to predict the preferences of users for specific items. These systems suffer from profile injection attacks, where the attackers have some prior knowledge of the system ratings and their goal is to promote or demote a particular item introducing abnormal (anomalous) ratings. The detection of both cases is a challenging problem. In this paper, we propose a framework to spot anomalous rating profiles (outliers), where the outliers hurriedly create a profile that injects into the system either random ratings or specific ratings, without any prior knowledge of the existing ratings. The proposed detection method is based on the unpredictable behavior of the outliers in a validation set, on the user-item rating matrix and on the similarity between users. The proposed system is totally unsupervised, and in the last step it uses the k-means clustering method automatically spotting the spurious profiles. For the cases where labeling sample data is available, a random forest classifier is trained to show how supervised methods outperforms unsupervised ones. Experimental results on the MovieLens 100k and the MovieLens 1M datasets demonstrate the high performance of the proposed schemata.

Zhang, F., Deng, Z., He, Z., Lin, X., Sun, L..  2018.  Detection Of Shilling Attack In Collaborative Filtering Recommender System By Pca And Data Complexity. 2018 International Conference on Machine Learning and Cybernetics (ICMLC). 2:673–678.

Collaborative filtering (CF) recommender system has been widely used for its well performing in personalized recommendation, but CF recommender system is vulnerable to shilling attacks in which shilling attack profiles are injected into the system by attackers to affect recommendations. Design robust recommender system and propose attack detection methods are the main research direction to handle shilling attacks, among which unsupervised PCA is particularly effective in experiment, but if we have no information about the number of shilling attack profiles, the unsupervised PCA will be suffered. In this paper, a new unsupervised detection method which combine PCA and data complexity has been proposed to detect shilling attacks. In the proposed method, PCA is used to select suspected attack profiles, and data complexity is used to pick out the authentic profiles from suspected attack profiles. Compared with the traditional PCA, the proposed method could perform well and there is no need to determine the number of shilling attack profiles in advance.

2019-03-25
Ali-Tolppa, J., Kocsis, S., Schultz, B., Bodrog, L., Kajo, M..  2018.  SELF-HEALING AND RESILIENCE IN FUTURE 5G COGNITIVE AUTONOMOUS NETWORKS. 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K). :1–8.
In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect, diagnose and correct degraded states in the managed network functions or other resources. Such methods are increasingly important in future network deployments, since ultra-high reliability is one of the key requirements for the future 5G mobile networks, e.g. in critical machine-type communication. In this paper, we discuss the considerations for improving the resiliency of future cognitive autonomous mobile networks. In particular, we present an automated anomaly detection and diagnosis function for SON self-healing based on multi-dimensional statistical methods, case-based reasoning and active learning techniques. Insights from both the human expert and sophisticated machine learning methods are combined in an iterative way. Additionally, we present how a more holistic view on mobile network self-healing can improve its performance.
2019-01-31
Nakamura, T., Nishi, H..  2018.  TMk-Anonymity: Perturbation-Based Data Anonymization Method for Improving Effectiveness of Secondary Use. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. :3138–3143.

The recent emergence of smartphones, cloud computing, and the Internet of Things has brought about the explosion of data creation. By collating and merging these enormous data with other information, services that use information become more sophisticated and advanced. However, at the same time, the consideration of privacy violations caused by such merging is indispensable. Various anonymization methods have been proposed to preserve privacy. The conventional perturbation-based anonymization method of location data adds comparatively larger noise, and the larger noise makes it difficult to utilize the data effectively for secondary use. In this research, to solve these problems, we first clarified the definition of privacy preservation and then propose TMk-anonymity according to the definition.

2018-11-19
Sun, K., Esnaola, I., Perlaza, S. M., Poor, H. V..  2017.  Information-Theoretic Attacks in the Smart Grid. 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm). :455–460.

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler (KL) divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.

2018-08-23
Xi, X., Zhang, F., Lian, Z..  2017.  Implicit Trust Relation Extraction Based on Hellinger Distance. 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). :223–227.

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.

2018-05-01
Zhao, H., Ren, J., Pei, Z., Cai, Z., Dai, Q., Wei, W..  2017.  Compressive Sensing Based Feature Residual for Image Steganalysis Detection. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :1096–1100.

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.

2018-04-04
Lin, Y., Abur, A..  2017.  Identifying security vulnerabilities of weakly detectable network parameter errors. 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :295–301.
This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.
2017-03-08
Çeker, H., Upadhyaya, S..  2015.  Enhanced recognition of keystroke dynamics using Gaussian mixture models. MILCOM 2015 - 2015 IEEE Military Communications Conference. :1305–1310.

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.