Abstract Since published in 1988, the FFT Accumulation Method (FAM) has been used extensively to compute the Spectral Correlation Function (SCF) and the Spectral Coherence Function (SCoF) to obtain or detect cyclic features of cyclostationary signals. When the input is a Gaussian random variable (r.v.), the SCF (or SCoF) estimates are also random variables with some probability density function (pdf). Although the FAM is considered the most computationally efficient method, there has been no in-depth statistical analysis of the algorithm. This paper analyzes the statistics of spectral estimates of the SCF using the FAM algorithm by obtaining the pdf for the points covering the frequency and cycle frequency $$\left(f;\alpha\right)$$ f ; α plane, and application examples with simulation results are provided. The method proposed in the paper can be extended to other algorithms, provided they can be given by a quadratic form.
Telecare medical information systems (TMIS) integrate telecommunication and information technology with Internet of Things (IoT) devices, such as wearable sensors, to enable the remote delivery of healthcare services by seamlessly collecting and transmitting health data to the system. However, the integration of IoT in e-health raises significant security concerns, including increased accessibility of patients’ private information to adversaries, inadequate authentication mechanisms, and high server time complexity. Addressing these challenges, we propose a new group-based ultralightweight authentication protocol (GBULAP) for TMIS. The protocol introduces a novel indexing mechanism for the medical server, enabling it to directly identify the doctor’s ID and thereby reducing server time complexity. Furthermore, it ensures robust authentication among the doctor, server, and patient. We conduct a comprehensive security analysis of the proposed protocol using the real-or-random (ROR) model and BAN (Burrows-Abadi-Needham) logic. The results confirm the protocol’s resilience against common attacks targeting TMIS and IoT systems. Furthermore, formal security analysis of GBULAP, conducted using the widely adopted AVISPA (Automated Validation of Internet Security Protocols and Applications) framework, confirms the protocol’s robustness against replay and man-in-the-middle attacks. Additionally, performance analysis demonstrates that the protocol significantly reduces processing and communication costs.
Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network’s performance has improved because training and testing time do not increase when we include more nodes in the network.
Q. M. Kamrunnahar, Firoz Haider, Rifat Ahmmed Aoni
et al.
A dual-channel propagation controlled photonic crystal fiber (PCF)-based plasmonic sensor was presented to detect multiple analytes simultaneously. Plasmonic micro-channels were placed on the outer surface of the PCF, which facilitates an easy sensing mechanism. The sensor was numerically investigated by the finite element method (FEM) with the perfectly matched layer (PML) boundary conditions. The proposed sensor performances were analyzed based on optimized sensor parameters, such as confinement loss, resonance coupling, resolution, sensitivity, and figure of merit (FOM). The proposed sensor showed a maximum wavelength sensitivity (WS) of 25,000 nm/refractive index unit (RIU) with a maximum sensor resolution (SR) of 4.0 × 10<sup>−6</sup> RIU for channel 2 (Ch-2), and WS of 3000 nm/RIU with SR of 3.33 × 10<sup>−5</sup> RIU for channel 1 (Ch-1). To the best of our knowledge, the proposed sensor exhibits the highest WS compared with the previously reported multi-analyte based PCF surface plasmon resonance (SPR) sensors. The proposed sensor could detect the unknown analytes within the refractive index (RI) range of 1.32 to 1.39 in the visible to near infrared region (550 to 1300 nm). In addition, the proposed sensor offers the maximum Figure of Merit (FOM) of 150 and 500 RIU<sup>−1</sup> with the limit of detection (LOD) of 1.11 × 10<sup>−8</sup> RIU<sup>2</sup>/nm and 1.6 × 10<sup>−10</sup> RIU<sup>2</sup>/nm for Ch-1 and Ch-2, respectively. Due to its highly sensitive nature, the proposed multi-analyte PCF SPR sensor could be a prominent candidate in the field of biosensing to detect biomolecule interactions and chemical sensing.
Kiasu-BC algorithm is an internal tweakable block cipher of authenticated encryption algorithm Kiasu as one of first-round candidates in the CAESAR competition.The precomputation complexity is reduced by utilizing the freedom of the tweak and the internal key restriction through the research on structural characteristics of Kiasu-BC algorithm based on AES-128 round function.Combined with the differential enumeration technique, a new 5-round meet-in-the-middle distinguisher was constructed to improve the meet-in-the-middle attack on 8-round Kiasu-BC algorithm.The improved attack requires the time complexity of 2<sup>114</sup>, the memory complexity of 2<sup>63</sup> and the data complexity of 2<sup>108</sup>.
Despite the significant progress in camera pose estimation and structure-from-motion reconstruction from unstructured images, methods that exploit a priori information on camera arrangements have been overlooked. Conventional state-of-the-art methods do not exploit the geometric structure to recover accurate camera poses from a set of patch images in an array for mosaic-based imaging that creates a wide field-of-view image by sewing together a collection of regular images. We propose a camera pose estimation framework that exploits the array-structured image settings in each incremental reconstruction step. It consists of the two-way registration, the 3D point outlier elimination and the bundle adjustment with a constraint term for consistent rotation vectors to reduce reprojection errors during optimization. We demonstrate that by using individual images' connected structures at different camera pose estimation steps, we can estimate camera poses more accurately from all structured mosaic-based image sets, including omnidirectional scenes.
Maurizio Capra, Beatrice Bussolino, Alberto Marchisio
et al.
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them.
The capability of providing network service extensibility is the major demand of next generation network,it is also the main challenge of network service model technology.The extensible network service model was proposed in response to the demand of network development.Firstly,an overview of the basic principles of the extensible network service model was expounded.And then,from the point of view of principle analysis,the service extension capability of the service model was qualitatively analyzed.Finally,using the analytic method of network service extensible capability,the service extension capability of the service model was quantitative analyzed from two perspectives of theory and practical application.The qualitative analysis and the quantitative analysis show that the extensible network service model has strong service extensible capability.
Miguel Angel Murillo-Escobar, Manuel Omar Meranza-Castillón, Rosa Martha López-Gutiérrez
et al.
Currently, chaos-based cryptosystems are being proposed in the literature to provide confidentiality for digital images, since the diffusion effect in the Advance Encryption Standard (AES) algorithm is weak. Security is the most important challenge to assess in cryptosystems according to the National Institute of Standard and Technology (NIST), then cost and performance, and finally algorithm and implementation. Recent chaos-based image encryption algorithms present basic security analysis, which could make them insecure for some applications. In this paper, we suggest an integral analysis framework related to comprehensive security analysis, cost and performance, and the algorithm and implementation for chaos-based image cryptosystems. The proposed guideline based on 20 analysis points can assist new cryptographic designers to present an integral analysis of new algorithms. Future comparisons of new schemes can be more consistent in terms of security and efficiency. In addition, we present aspects regarding digital chaos implementation, chaos validation, and key definition to improve the security of the overall cryptosystem. The suggested guideline does not guarantee security, and it does not intend to limit the liberty to implement new analysis. However, it provides for the first time in the literature a solid basis about integral analysis for chaos-based image cryptosystems as an effective approach to improve security.
I-PRESENT was a lightweight SPN block cipher for resource-constraint environments such as RFID tags and sensor networks.The biclique structures of I-PRESENT with sieve-in-the-middle technique was an constracted.The biclique cryptanalysis schemes on full-round I-PRESENT-80 and I-PRESENT-128 were proposed for the first time.The results show that the data complexity of the biclique cryptanalysis on I-PRESENT-80 and I-PRESENT-128 is 2 <sup>26</sup> and 2<sup>36</sup> chosen ciphertexts respectively，and the time complexity on them is 2 <sup>79.48</sup> and 2 <sup>127.33</sup> encryptions respectively.The time and data complexity are better than that of the exhaustive attack.In addition,the time complexity on them can be reduced to 2 <sup>78.61</sup> and 2<sup>126.48</sup> encryptions by using related-key technology of I-PRESENT.
With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple and other companies have introduced differential privacy technology into the latest products,and differential privacy technology has become the focus of academia and industry again.Firstly,the traditional centralized model of differential privacy was summarized,from the perspective of analysis of data mining and data released in the differential privacy way.Then the latest local differential privacy regarding data collection and data analysis based on the local model was described,involving crowdsourcing with random response technology,BloomFilter,statistical inference techniques.Finally,the main problems and solutions of differential privacy technology were summarized.
The transmission of image/video over unreliable medium like wireless networks generally results in receiving a damaged image/video. In this paper, a novel image error concealment scheme based on the idea of data hiding and Set Partitioning In Hierarchical Trees (SPIHT) coding is investigated. In the encoder side, the coefficients of wavelet decomposed image are partitioned into “perfect trees”. The SPIHT coder is applied to encode each per-fect tree independently and generate an efficiently compressed reference code. This code is then embedded into the coefficients of another perfect tree which is located in a different place, using a robust data hiding scheme based on Quantization Index Modulation (QIM). In the decoder side, if a part of the image is lost, the algorithm extracts the embedded code for reference trees related to this part to reconstruct the lost information. Performance results show that for an error prone transmission, the proposed technique is promising to efficiently conceal the lost areas of the transmitted image.
Patricia López-Rodríguez, David Escot-Bocanegra, Raúl Fernández-Recio
et al.
Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising.
The purpose of this paper is to present a comparison among two models and its measurement to calculate the shielding effectiveness of electromagnetic barriers, applying it to conductive textiles. Each one, models a conductive textile as either a (1) wire mesh screen or (2) compact material. Therefore, the objective is to perform an analysis of the models in order to determine which one is a better approximation for electromagnetic shielding fabrics. In order to provide results for the comparison, the shielding effectiveness of the sample has been measured by means of the standard ASTM D4935-99.