Visible to the public Biblio

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Moussa, Y., Alexan, W..  2020.  Message Security Through AES and LSB Embedding in Edge Detected Pixels of 3D Images. 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES). :224—229.

This paper proposes an advanced scheme of message security in 3D cover images using multiple layers of security. Cryptography using AES-256 is implemented in the first layer. In the second layer, edge detection is applied. Finally, LSB steganography is executed in the third layer. The efficiency of the proposed scheme is measured using a number of performance metrics. For instance, mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean absolute error (MAE) and entropy.

Lyu, L..  2020.  Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
The appearance of distributed learning allows multiple participants to collaboratively train a global model, where instead of directly releasing their private training data with the server, participants iteratively share their local model updates (parameters) with the server. However, recent attacks demonstrate that sharing local model updates is not sufficient to provide reasonable privacy guarantees, as local model updates may result in significant privacy leakage about local training data of participants. To address this issue, in this paper, we present an alternative approach that combines distributed differential privacy (DDP) with a three-layer encryption protocol to achieve a better privacy-utility tradeoff than the existing DP-based approaches. An unbiased encoding algorithm is proposed to cope with floating-point values, while largely reducing mean squared error due to rounding. Our approach dispenses with the need for any trusted server, and enables each party to add less noise to achieve the same privacy and similar utility guarantees as that of the centralized differential privacy. Preliminary analysis and performance evaluation confirm the effectiveness of our approach, which achieves significantly higher accuracy than that of local differential privacy approach, and comparable accuracy to the centralized differential privacy approach.
Farrag, Sara, Alexan, Wassim, Hussein, Hisham H..  2019.  Triple-Layer Image Security Using a Zigzag Embedding Pattern. 2019 International Conference on Advanced Communication Technologies and Networking (CommNet). :1–8.
This paper proposes a triple-layer, high capacity, message security scheme. The first two layers are of a cryptographic nature, whereas the third layer is of a steganographic nature. In the first layer, AES-128 encryption is performed on the secret message. In the second layer, a chaotic logistic map encryption is applied on the output of the first secure layer to increase the security of the scheme. In the third layer of security, a 2D image steganography technique is performed, where the least significant bit (LSB) -embedding is done according to a zigzag pattern in each of the three color planes of the cover image (i.e. RGB). The distinguishing feature of the proposed scheme is that the secret data is hidden in a zigzag manner that cannot be predicted by a third party. Moreover, our scheme achieves higher values of peak signal to noise ratio (PPSNR), mean square error (MSE), the structural similarity index metric (SSIM), normal cross correlation (NCC) and image fidelity (IF) compared to its counterparts form the literature. In addition, a histogram analysis as well as the high achieved capacity are magnificent indicators for a reliable and high capacity steganographic scheme.
LiPing, Yuan, Pin, Han.  2019.  Research of Low-Quality Laser Security Code Enhancement Technique. 2019 Chinese Automation Congress (CAC). :793–796.
The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
Lee, Yong Up, Kang, Kyeong-Yoon, Choi, Ginkyu.  2019.  Secure Visible Light Encryption Communication Technique for Smart Home Service. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0827–0831.
For the security enhancement of the conventional visible light (VL) communication which allows the easy intrusion by adjacent adversary due to visible signal characteristic, the VL communication technique based on the asymmetric Rivest-Shamir-Adleman (RSA) encryption method is proposed for smart indoor service in this paper, and the optimal key length of the RSA encryption process for secure VL communication technique is investigated, and also the error performance dependent on the various asymmetric encryption key is analyzed for the performance evaluation of the proposed technique. Then we could see that the VL communication technique based on the RSA encryption gives the similar RMSE performance independent of the length of the public or private key and provides the better error performance as the signal to noise ratio (SNR) increases.
Qureshi, Ayyaz-Ul-Haq, Larijani, Hadi, Javed, Abbas, Mtetwa, Nhamoinesu, Ahmad, Jawad.  2019.  Intrusion Detection Using Swarm Intelligence. 2019 UK/ China Emerging Technologies (UCET). :1–5.
Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme.
Mutiarachim, A., Pranata, S. Felix, Ansor, B., Shidik, G. Faiar, Fanani, A. Zainul, Soeleman, A., Pramunendar, R. Anggi.  2018.  Bit Localization in Least Significant Bit Using Fuzzy C-Means. 2018 International Seminar on Application for Technology of Information and Communication. :290-294.

Least Significant Bit (LSB) as one of steganography methods that already exist today is really mainstream because easy to use, but has weakness that is too easy to decode the hidden message. It is because in LSB the message embedded evenly to all pixels of an image. This paper introduce a method of steganography that combine LSB with clustering method that is Fuzzy C-Means (FCM). It is abbreviated with LSB\_FCM, then compare the stegano result with LSB method. Each image will divided into two cluster, then the biggest cluster capacity will be choosen, finally save the cluster coordinate key as place for embedded message. The key as a reference when decode the message. Each image has their own cluster capacity key. LSB\_FCM has disadvantage that is limited place to embedded message, but it also has advantages compare with LSB that is LSB\_FCM have more difficulty level when decrypted the message than LSB method, because in LSB\_FCM the messages embedded randomly in the best cluster pixel of an image, so to decrypted people must have the cluster coordinate key of the image. Evaluation result show that the MSE and PSNR value of LSB\_FCM some similiar with the pure LSB, it means that LSB\_FCM can give imperceptible image as good as the pure LSB, but have better security from the embedding place.

Thoen, B., Wielandt, S., Strycker, L. De.  2018.  Fingerprinting Method for Acoustic Localization Using Low-Profile Microphone Arrays. 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN). :1–7.

Indoor localization of unknown acoustic events with MEMS microphone arrays have a huge potential in applications like home assisted living and surveillance. This article presents an Angle of Arrival (AoA) fingerprinting method for use in Wireless Acoustic Sensor Networks (WASNs) with low-profile microphone arrays. In a first research phase, acoustic measurements are performed in an anechoic room to evaluate two computationally efficient time domain delay-based AoA algorithms: one based on dot product calculations and another based on dot products with a PHAse Transform (PHAT). The evaluation of the algorithms is conducted with two sound events: white noise and a female voice. The algorithms are able to calculate the AoA with Root Mean Square Errors (RMSEs) of 3.5° for white noise and 9.8° to 16° for female vocal sounds. In the second research phase, an AoA fingerprinting algorithm is developed for acoustic event localization. The proposed solution is experimentally verified in a room of 4.25 m by 9.20 m with 4 acoustic sensor nodes. Acoustic fingerprints of white noise, recorded along a predefined grid in the room, are used to localize white noise and vocal sounds. The localization errors are evaluated using one node at a time, resulting in mean localization errors between 0.65 m and 0.98 m for white noise and between 1.18 m and 1.52 m for vocal sounds.

Samudrala, A. N., Blum, R. S..  2017.  Asymptotic Analysis of a New Low Complexity Encryption Approach for the Internet of Things, Smart Cities and Smart Grid. 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC). :200–204.

Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $ε$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.

Cai, Y., Huang, H., Cai, H., Qi, Y..  2017.  A K-nearest neighbor locally search regression algorithm for short-term traffic flow forecasting. 2017 9th International Conference on Modelling, Identification and Control (ICMIC). :624–629.

Accurate short-term traffic flow forecasting is of great significance for real-time traffic control, guidance and management. The k-nearest neighbor (k-NN) model is a classic data-driven method which is relatively effective yet simple to implement for short-term traffic flow forecasting. For conventional prediction mechanism of k-NN model, the k nearest neighbors' outputs weighted by similarities between the current traffic flow vector and historical traffic flow vectors is directly used to generate prediction values, so that the prediction results are always not ideal. It is observed that there are always some outliers in k nearest neighbors' outputs, which may have a bad influences on the prediction value, and the local similarities between current traffic flow and historical traffic flows at the current sampling period should have a greater relevant to the prediction value. In this paper, we focus on improving the prediction mechanism of k-NN model and proposed a k-nearest neighbor locally search regression algorithm (k-LSR). The k-LSR algorithm can use locally search strategy to search for optimal nearest neighbors' outputs and use optimal nearest neighbors' outputs weighted by local similarities to forecast short-term traffic flow so as to improve the prediction mechanism of k-NN model. The proposed algorithm is tested on the actual data and compared with other algorithms in performance. We use the root mean squared error (RMSE) as the evaluation indicator. The comparison results show that the k-LSR algorithm is more successful than the k-NN and k-nearest neighbor locally weighted regression algorithm (k-LWR) in forecasting short-term traffic flow, and which prove the superiority and good practicability of the proposed algorithm.

Saleh, C., Mohsen, M..  2017.  FBG security fence for intrusion detection. 2017 International Conference on Engineering MIS (ICEMIS). :1–5.

The following topics are dealt with: feature extraction; data mining; support vector machines; mobile computing; photovoltaic power systems; mean square error methods; fault diagnosis; natural language processing; control system synthesis; and Internet of Things.

Liren, Z., Xin, Y., Yang, P., Li, Z..  2017.  Magnetic performance measurement and mathematical model establishment of main core of magnetic modulator. 2017 13th IEEE International Conference on Electronic Measurement Instruments (ICEMI). :12–16.

In order to investigate the relationship and effect on the performance of magnetic modulator among applied DC current, excitation source, excitation loop current, sensitivity and induced voltage of detecting winding, this paper measured initial permeability, maximum permeability, saturation magnetic induction intensity, remanent magnetic induction intensity, coercivity, saturated magnetic field intensity, magnetization curve, permeability curve and hysteresis loop of main core 1J85 permalloy of magnetic modulator based on ballistic method. On this foundation, employ curve fitting tool of MATLAB; adopt multiple regression method to comprehensively compare and analyze the sum of squares due to error (SSE), coefficient of determination (R-square), degree-of-freedom adjusted coefficient of determination (Adjusted R-square), and root mean squared error (RMSE) of fitting results. Finally, establish B-H curve mathematical model based on the sum of arc-hyperbolic sine function and polynomial.

Hosseinpourpia, M., Oskoei, M. A..  2017.  GA Based Parameter Estimation for Multi-Faceted Trust Model of Recommender Systems. 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). :160–165.

Recommender system is to suggest items that might be interest of the users in social networks. Collaborative filtering is an approach that works based on similarity and recommends items liked by other similar users. Trust model adopts users' trust network in place of similarity. Multi-faceted trust model considers multiple and heterogeneous trust relationship among the users and recommend items based on rating exist in the network of trustees of a specific facet. This paper applies genetic algorithm to estimate parameters of multi-faceted trust model, in which the trust weights are calculated based on the ratings and the trust network for each facet, separately. The model was built on Epinions data set that includes consumers' opinion, rating for items and the web of trust network. It was used to predict users' rating for items in different facets and root mean squared of prediction error (RMSE) was considered as a measure of performance. Empirical evaluations demonstrated that multi-facet models improve performance of the recommender system.

Chauhan, A. S., Sahula, V..  2015.  High density impulsive Noise removal using decision based iterated conditional modes. 2015 International Conference on Signal Processing, Computing and Control (ISPCC). :24–29.

Salt and Pepper Noise is very common during transmission of images through a noisy channel or due to impairment in camera sensor module. For noise removal, methods have been proposed in literature, with two stage cascade various configuration. These methods, can remove low density impulse noise, are not suited for high density noise in terms of visible performance. We propose an efficient method for removal of high as well as low density impulse noise. Our approach is based on novel extension over iterated conditional modes (ICM). It is cascade configuration of two stages - noise detection and noise removal. Noise detection process is a combination of iterative decision based approach, while noise removal process is based on iterative noisy pixel estimation. Using improvised approach, up to 95% corrupted image have been recovered with good results, while 98% corrupted image have been recovered with quite satisfactory results. To benchmark the image quality, we have considered various metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and SSIM (Structure Similarity Index Measure).

Honauer, K., Maier-Hein, L., Kondermann, D..  2015.  The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms. 2015 IEEE International Conference on Computer Vision (ICCV). :2120–2128.

Performance characterization of stereo methods is mandatory to decide which algorithm is useful for which application. Prevalent benchmarks mainly use the root mean squared error (RMS) with respect to ground truth disparity maps to quantify algorithm performance. We show that the RMS is of limited expressiveness for algorithm selection and introduce the HCI Stereo Metrics. These metrics assess stereo results by harnessing three semantic cues: depth discontinuities, planar surfaces, and fine geometric structures. For each cue, we extract the relevant set of pixels from existing ground truth. We then apply our evaluation functions to quantify characteristics such as edge fattening and surface smoothness. We demonstrate that our approach supports practitioners in selecting the most suitable algorithm for their application. Using the new Middlebury dataset, we show that rankings based on our metrics reveal specific algorithm strengths and weaknesses which are not quantified by existing metrics. We finally show how stacked bar charts and radar charts visually support multidimensional performance evaluation. An interactive stereo benchmark based on the proposed metrics and visualizations is available at:

M. B. Amin, W. Zirwas, M. Haardt.  2015.  "Advanced channel prediction concepts for 5G radio systems". 2015 International Symposium on Wireless Communication Systems (ISWCS). :166-170.

Massive MIMO and tight cooperation between transmission nodes are expected to become an integral part of a future 5G radio system. As part of an overall interference mitigation scheme substantial gains in coverage, spectral as well as energy efficiency have been reported. One of the main limitations for massive MIMO and coordinated multi-point (CoMP) systems is the aging of the channel state information at the transmitter (CSIT), which can be overcome partly by state of the art channel prediction techniques. For a clean slate 5G radio system, we propose to integrate channel prediction from the scratch in a flexible manner to benefit from future improvements in this area. As any prediction is unreliable by nature, further improvements over the state of the art are needed for a convincing solution. In this paper, we explain how the basic ingredients of 5G like base stations with massive MIMO antenna arrays, and multiple UE antennas can help to stretch today's limits with an approximately 10 dB lower normalized mean square error (NMSE) of the predicted channel. In combination with the novel introduced concept of artificially mutually coupled antennas, adding super-directivity gains to virtual beamforming, robust and accurate prediction over 10 ms with an NMSE of -20 dB up to 15 km/h at 2.6 GHz RF frequency could be achieved. This result has been achieved for measured channels without massive MIMO, but a comparison with ray-traced channels for the same scenario is provided as well.

Nower, N., Yasuo Tan, Lim, A.O..  2014.  Efficient Temporal and Spatial Data Recovery Scheme for Stochastic and Incomplete Feedback Data of Cyber-physical Systems. Service Oriented System Engineering (SOSE), 2014 IEEE 8th International Symposium on. :192-197.

Feedback loss can severely degrade the overall system performance, in addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications including stochastic and time-critical traffic patterns. Stochastic data has a randomness in its nature which make a great challenge to maintain the real-time control whenever the data is lost. In this paper, we propose a data recovery scheme, called the Efficient Temporal and Spatial Data Recovery (ETSDR) scheme for stochastic incomplete feedback of CPS. In this scheme, we identify the temporal model based on the traffic patterns and consider the spatial effect of the nearest neighbor. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithm regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.

Liming Shi, Yun Lin.  2014.  Convex Combination of Adaptive Filters under the Maximum Correntropy Criterion in Impulsive Interference. Signal Processing Letters, IEEE. 21:1385-1388.

A robust adaptive filtering algorithm based on the convex combination of two adaptive filters under the maximum correntropy criterion (MCC) is proposed. Compared with conventional minimum mean square error (MSE) criterion-based adaptive filtering algorithm, the MCC-based algorithm shows a better robustness against impulsive interference. However, its major drawback is the conflicting requirements between convergence speed and steady-state mean square error. In this letter, we use the convex combination method to overcome the tradeoff problem. Instead of minimizing the squared error to update the mixing parameter in conventional convex combination scheme, the method of maximizing the correntropy is introduced to make the proposed algorithm more robust against impulsive interference. Additionally, we report a novel weight transfer method to further improve the tracking performance. The good performance in terms of convergence rate and steady-state mean square error is demonstrated in plant identification scenarios that include impulsive interference and abrupt changes.

Bhotto, M.Z.A., Antoniou, A..  2014.  Affine-Projection-Like Adaptive-Filtering Algorithms Using Gradient-Based Step Size. Circuits and Systems I: Regular Papers, IEEE Transactions on. 61:2048-2056.

A new class of affine-projection-like (APL) adaptive-filtering algorithms is proposed. The new algorithms are obtained by eliminating the constraint of forcing the a posteriori error vector to zero in the affine-projection algorithm proposed by Ozeki and Umeda. In this way, direct or indirect inversion of the input signal matrix is not required and, consequently, the amount of computation required per iteration can be reduced. In addition, as demonstrated by extensive simulation results, the proposed algorithms offer reduced steady-state misalignment in system-identification, channel-equalization, and acoustic-echo-cancelation applications. A mean-square-error analysis of the proposed APL algorithms is also carried out and its accuracy is verified by using simulation results in a system-identification application.

Huang, T., Drake, B., Aalfs, D., Vidakovic, B..  2014.  Nonlinear Adaptive Filtering with Dimension Reduction in the Wavelet Domain. Data Compression Conference (DCC), 2014. :408-408.

Recent advances in adaptive filter theory and the hardware for signal acquisition have led to the realization that purely linear algorithms are often not adequate in these domains. Nonlinearities in the input space have become apparent with today's real world problems. Algorithms that process the data must keep pace with the advances in signal acquisition. Recently kernel adaptive (online) filtering algorithms have been proposed that make no assumptions regarding the linearity of the input space. Additionally, advances in wavelet data compression/dimension reduction have also led to new algorithms that are appropriate for producing a hybrid nonlinear filtering framework. In this paper we utilize a combination of wavelet dimension reduction and kernel adaptive filtering. We derive algorithms in which the dimension of the data is reduced by a wavelet transform. We follow this by kernel adaptive filtering algorithms on the reduced-domain data to find the appropriate model parameters demonstrating improved minimization of the mean-squared error (MSE). Another important feature of our methods is that the wavelet filter is also chosen based on the data, on-the-fly. In particular, it is shown that by using a few optimal wavelet coefficients from the constructed wavelet filter for both training and testing data sets as the input to the kernel adaptive filter, convergence to the near optimal learning curve (MSE) results. We demonstrate these algorithms on simulated and a real data set from food processing.

Guang Hua, Goh, J., Thing, V.L.L..  2014.  A Dynamic Matching Algorithm for Audio Timestamp Identification Using the ENF Criterion. Information Forensics and Security, IEEE Transactions on. 9:1045-1055.

The electric network frequency (ENF) criterion is a recently developed technique for audio timestamp identification, which involves the matching between extracted ENF signal and reference data. For nearly a decade, conventional matching criterion has been based on the minimum mean squared error (MMSE) or maximum correlation coefficient. However, the corresponding performance is highly limited by low signal-to-noise ratio, short recording durations, frequency resolution problems, and so on. This paper presents a threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates. The threshold is chosen according to the frequency resolution determined by the short-time Fourier transform (STFT) window size. A penalty coefficient is introduced to monitor the autocorrection process and finally determine the estimated timestamp. It is then shown that the DMA generalizes the conventional MMSE method. By considering the mainlobe width in the STFT caused by limited frequency resolution, the DMA achieves improved identification accuracy and robustness against higher levels of noise and the offset problem. Synthetic performance analysis and practical experimental results are provided to illustrate the advantages of the DMA.