The Internet of Things (IoT) has revolutionized various industries but also created substantial security vulnerabilities. Traditional security mechanisms struggle to address the evolving threats, prompting interest in machine learning (ML)-based Intrusion Detection Systems (IDS). This paper presents Consistency Bidirectional Mamba Autoencoder Anomaly Detection (CBMAD) model, a novel unsupervised multivariate time-series (MVTS) anomaly detection algorithm, for detecting anomalies in IoT network traffic. CBMAD employs a bidirectional encoder and a gate mechanism (GM) for feature extraction and fusion, coupled with a representation consistency learning framework to capture correlations among normal behaviors. Anomalous patterns are identified due to their inability to reconstruct inputs and maintain consistency across varying input perspectives. Extensive experiments conducted on several publicly available Internet of Medical Things (IoMT) network traffic datasets demonstrate that the proposed method achieves superior detection performance compared to several recent detection algorithms. These results indicate that CBMAD offers an effective solution for enhancing security in medical IoT environments.

Effective anomaly detection is vital to minimize the economic impacts of security issues that Internet of Things (IoT) and Industrial Internet of Things (IIoT) face more frequently in recent years. Conventional anomaly detection methods have difficulties in finding new and complex anomalies. machine learning (ML) algorithms have proven to be excellent in anomaly detection with the development of ML research. However, labeled data is hard to get in real situations, so we need unsupervised learning methods. We suggest a new unsupervised learning method for detecting anomalies in multivariate time series, named Bi-Dual-GM GRU-AE. It has two Gate Mechanisms to better pick the combination part from different aspects of input features. These features are derived from different hidden sizes of GRU encoders that take both forward and backward time windows as inputs. We evaluated the proposed method on real-world IoT and IIoT datasets and showed that it outperforms the latest methods in the multivariate time series anomaly detection task. The proposed approach shows superior detection performance particularly in scenario where anomalies are relatively rare, offering a better detection solution to the modern challenges in IoT and IIoT security.

Fusarium wilt is a threatening plant infection for Phalaenopsis plants. The disease presents with symptoms such as yellowing and wilting of leaves, leading to death and possible spread to neighboring healthy plants. This study explores the use of hyperspectral imaging techniques and deep learning models to develop a non-destructive and efficient method for fusarium wilt detection. To exploit potential correlations and patterns within spectral bands, we use a 2D-CNN model as the model backbone. Finally, the integration of hyperspectral image data collection and detection models enables automated and simplified execution, providing a practical system for detecting and managing wilt in Phalaenopsis plants without manual intervention. This integration enables efficient processing of collected hyperspectral imagery, feeding it into detection models and producing reliable results.

In this paper, we build a platform that can automatically and rapidly detect Fusarium wilt on Phalaenopsis. We have also developed a portable handheld multispectral imaging device (PHMID) that contains six LEDs representing six spectral bands, making it easier to use in the field. The Automatic Target Generation Process (ATGP) and the Spectral Angle Mapper (SAM) are used to obtain the desired signal on a high-spectral image. The Harsany-Farrand-Chang (HFC) method is used for band selection to estimate the number of different spectral bands. We use deep neural networks (DNNs), support vector machines (SVMs), and random forest classifiers (RFCs) for classification. The best detection accuracy of VNIR, SWIR and PHMID was 95.77%, 91.72% and 90.84%, respectively.

In this paper, we combined hyperspectral imaging techniques and deep neural networks (DNN) to detect Fusarium wilt on Phalaenopsis. Spectral angle mapper (SAM) and constrained energy minimization (CEM) were used to find abnormal areas. Band selection (BS) methods include Harsanyi-Farrand-Chang (HFC), band priority (BP) and band decorrelation (BD) were applied to get effective bands. The results showed that, on the fifth day of Phalaenopsis infection, the best accuracy rates for detecting Fusarium wilt using VNIR and SWIR hyperspectral imaging were 93.5% and 94.9%, respectively. In most cases, the accuracy of using DNN is better than using support vector machine (SVM).

Food safety and quality examination has received close attention from general public in recent years. One of examples is the freshness of pleurotus eryngii, which plays an important role in its economic values. The estimation of its freshness could not be based on the number of storage days because fungi could normally keep alive for a while after harvest depending on storage environment. With advances of hardware and software, hyperspectral technologies shed some light on prediction of its nonlinear decay trend. This paper proposed a spectral derivative analysis to improve the temporal accuracy of previous works [5] from weeks to days. Then, the long short-term memory neural network was utilized to predict the decay trend based on the data of beginning few days. The experimental studies demonstrated that the prediction trends were close to the real ones.

In this paper, we propose a method to inspect the quality of Phalaenopsis by using hyperspectral imaging techniques. Phalaenopsis is easy to get infected with Fusarium wilt. We use the k-means clustering method to find out that the reflection spectrum of Phalaenopsis stem changes. The methods of the Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) are then used to find the area of the infected area. The Harsanyi, Farrand and Chang (HFC) methods and virtual dimensions (VD) are used to estimate the amount of spectrum required for band selection (BS). Band priority (BP) is used to calculate the priority of each band, and band de-correlation (BD) will remove band data with high correlation with each other. Then use the support vector machine (SVM) to detect Phalaenopsis wilt. The detection accuracy of VNIR and SWIR is 0.81 and 0.86, respectively, with band selection.

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt.

This paper presents an emerging method to detect pesticide residues on fruit. In order to enhance pesticide signature intensity and make the detection rate of pesticide better, we applied band weighting process and band selection (BS) process base on band prioritization (BP) and band decorrelation (BD) to adjust spectral data. Then four algorithms were used, spectral information divergence (SID), orthogonal subspace projection (OSP), constrained energy minimization (CEM), and support vector machine (SVM) to identify pesticide residues on different fruit. The results show that using CEM method has the highest detection rate of pesticide and has the potential to replace the other traditional methods.

An information theoretical approach to multiple-band selection (MBS) is presented in this paper. It formulates a MBS problem as a channel capacity problem by considering the original band set as a channel input space and the selected multiple band set as a channel output space with the channel transition probabilities specified by band discrimination between original bands and selected bands. Then bands are selected by iteratively finding a best possible input space that yields the maximal channel capacity. As a result, there is no need of band prioritization and de-correlation generally required by traditional band selection (BS). Two iterative algorithms are developed for MBS, sequential channel capacity MBS (SQ-CCMBS) and successive channel band selection (SC-CCMBS).

Cloud computing is an innovative service platform to offer diverse resources such as infrastructure, platform and software as services. However, one challenging aspect of such a service is the impatient user threat, which directly leads to numerous negative impacts such as poor throughput, unpredictable workload and waste of resources. In this paper, the problems of conducting system controls in a cost-effective way and simultaneously satisfying performance guarantees are first studied. System losses are analyzed according to the related performance factors and waiting buffer sizes. A cost model is developed to address a performances/cost tradeoff issue in which the user balking, reneging, system blocking and resources provisioning are all taken into account. The relationship between system controls and throughput variations in a multi-servers system with a finite buffer is demonstrated. A proposed policy combined with a heuristic algorithm allows cloud providers to control the service rate and buffer size within a system loss guarantee by solving constrained optimization problems. Simulation results show that more cost-saving and system throughput enhancement can be verified as compared to a system without applying our policy.

This paper develops band weighting spectral methods, which are wSAM and wSID, for detection of pesticide residues on vegetables. Since the water content of vegetables has significant impact on the measured spectrum, the proposed band weighting measures are able to suppress the effect of water content to enhance detectability of of pesticide residue detection. Compared to the traditional band selection techniques, there are three advantages. First of all, it does not require determining the number of bands to be selected. Second, the proposed methods assigned a weight to each band based on the amount of pesticide information. Third, the band weighting method could help reduce the effect of undesired signal, which is the water content in our case. The experimental study further demonstrates the utilities of our proposed band weighting methods.

Cloud computing is a new paradigm for delivering remote computing resources through a network. However, achieving an energy-efficiency control and simultaneously satisfying a performance guarantee have become critical issues for cloud providers. In this paper, three power-saving policies are implemented in cloud systems to mitigate server idle power. The challenges of controlling service rates and applying the N-policy to optimize operational cost within a performance guarantee are first studied. A cost function has been developed in which the costs of power consumption, system congestion and server startup are all taken into consideration. The effect of energy-efficiency controls on response times, operating modes and incurred costs are all demonstrated. Our objectives are to find the optimal service rate and mode-switching restriction, so as to minimize cost within a response time guarantee under varying arrival rates. An efficient green control (EGC) algorithm is first proposed for solving constrained optimization problems and making costs/performances tradeoffs in systems with different power-saving policies. Simulation results show that the benefits of reducing operational costs and improving response times can be verified by applying the power-saving policies combined with the proposed algorithm as compared to a typical system under a same performance guarantee.

Linear spectral mixture analysis (LSMA) generally performs with signatures assumed to be known to form a linear mixing model to be known. Unfortunately, this is generally not the case in real world applications. An unsupervised fully constrained least squares (UFCLS) method has been proposed to find these desired signatures. Unfortunately, it requires prior knowledge about the number of signatures, p needed to be generated. The recently proposed virtual dimensionality (VD) can be used for this purpose. This paper develops a recursive UFCLS (RUFCLS) method to accomplish these two tasks in one-shot operation, viz., determine the value of p as well as find these p signatures simultaneously. Such RUFCLS can perform data unmixing progressively signature-by-signature via a recursive update equation with signatures used to form a linear mixing model for linear spectral unmixing generated by UFCLS. Most importantly, RUFCLS does not require any matrix inverse operation but only matrix multiplications and outer products of vectors. This significant advantage provides an effective computational means of determining the VD.

Radial basis function (RBF) has been widely used in kernel-based approaches. This paper extended RBF kernels to weighted RBF (WRBF) kernels by introducing a weighting matrix A into RBF kernels. A key to success in implementing WRBF kernels is to design different appropriate weighting matrices to implement WRBF kernels. Three weighting matrices are of particular interest, covariance matrix, correlation matrix and within-class scatter matrix. Experimental results via various applications show that classifiers using WRBF kernels provide better performance than that using un-weigheted RBF kernels.

ABSTRACT In this paper, we propose a secure handoff scheme for data transmission in integration of 3G and 802.11 wireless local area networks (WLANs). The handoff between 802.11 WLAN and the 3G suffers from some drawbacks and has been hijacked through the middle of a communication session. We propose an architecture based on cloud computing to build our scheme to fix the problems in such heterogeneous wireless networks. Adaptive Key Exchange Protocol is proposed to protect data transmission as 3G users hand over to an 802.11 WLAN. The approach includes three phases, and all steps of each phase are protected by robust public‐key encryption. Therefore, no information can be hijacked in such environment. The security analysis shows that data transmission between 802.11 WLAN and 3G is robust and secure in various aspects. Copyright © 2012 John Wiley & Sons, Ltd.

Support vector machine (SVM) has received considerable interest in hyperspectral image classification. In order to make SVM work effectively one challenge is selection of training samples. In supervised classification it is generally done by random sampling for cross validation where two issues must be addressed. One is how many training samples required to allow SVM to produce good performance and the other is how to deal with random selections of training samples which produce inconsistent results. This paper presents a new type of SVM, called iterative SVM (ISVM) to address these two issues. The idea is to implement an SVM iteratively in such a way that the sample size is not necessarily to be large while the random sampling issue can be also resolved. To substantiate the utility of ISVM Purdue data is further used for experiments.

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image.

Abstract Non‐repudiation is one of most important security services in electronic transactions. It provides protection from denial by one of the entities involved in a communication of parties participating in all or part of communication. In practice, a non‐repudiation property is an important evidence for accounting for or tracking a system or tracking to illegal connections. However, a password‐based communication system does not provide the non‐repudiation property for connection evidence. In this paper, a dynamic session key policy (DSKP) with a non‐repudiation signature scheme for secure mobile networks authentication is proposed. The proposed non‐repudiation signature scheme is a mixed method which combines a one‐way hash function and a traditional digital signature technique. Since computational cost usually is an important issue in a mobile environment, the proposed scheme can provide additional non‐repudiation properties satisfying the future needs of electronic evidences such as accounting, auditing, logging, and tracking of connections to a mobile network system at the expense of a small additional computing load for the mobile devices. Besides, it can also achieve the same security services from a security and performance analysis point of view. Copyright © 2008 John Wiley & Sons, Ltd.

We propose a security authentication policy, Dynamic Session Key Policy (DSKP), for a secure handoff between the UMTS and IEEE 802.11 WLAN. This policy is founded and improved from Dynamic Key Exchange Protocol (DKEP). It redeems the confidentiality of the communication association using the asymmetric and symmetric encryption. The one time password system is used for the key exchange of the sessions. The transition of the communication states in DSKP are seamless and cannot be personated. From the security analysis, using the DSKP can avoid possible attack in wireless circumstance. EAP-SIM and EAP-AKA, commonly used for authentication protocol in present WLAN, were also studied to compare with DSKP. From our security analysis, DSKP shows better security grades.

A new growing method for simplex-based endmember extraction algorithms (EEAs), called simplex growing algorithm (SGA), is presented in this paper. It is a sequential algorithm to find a simplex with the maximum volume every time a new vertex is added. In order to terminate this algorithm a recently developed concept, virtual dimensionality (VD), is implemented as a stopping rule to determine the number of vertices required for the algorithm to generate. The SGA improves one commonly used EEA, the N-finder algorithm (N-FINDR) developed by Winter, by including a process of growing simplexes one vertex at a time until it reaches a desired number of vertices estimated by the VD, which results in a tremendous reduction of computational complexity. Additionally, it also judiciously selects an appropriate initial vector to avoid a dilemma caused by the use of random vectors as its initial condition in the N-FINDR where the N-FINDR generally produces different sets of final endmembers if different sets of randomly generated initial endmembers are used. In order to demonstrate the performance of the proposed SGA, the N-FINDR and two other EEAs, pixel purity index, and vertex component analysis are used for comparison.

In this paper, we propose a secure handoff scheme for the integration of UMTS and 802.11 WLAN networks. The handoff between 802.11 WLAN and the UMTS has some drawbacks and could be hijacked through middle of a communication session. An architecture built for a secure handoff scheme is proposed to fix that problem. The Dynamic Key Exchange Protocol (DKEP) is used to protect users during a UMTS handover to a 802.11 WLAN environment. The mobile station (MS) and access point (AP) compute their session key individually. The protocol includes three phases and all the steps of the phases are protected by public-key encryption. Therefore no information can be hijacked between MS and AP. From the security analysis, we know that the handoff between WLAN and UMTS is guaranteed in various aspects. For example, user identity and new registration can be protected, thus avoiding denial of service, key reuse, and so on.

The measurements of various types of network traffic are found to exhibit self-similar characteristics. A key parameter characterizing self-similar processes is the Hurst parameter H, which is designed to capture the degree of self-similarity. In order to determine if a given time series exhibits self-similarity, a method is needed to estimate H for a given time series. We present an estimation tool by using the wavelet transform. An important feature of the wavelet-based tool is the conceptual and practical simplicity, consisting essentially in measuring the slope. Moreover, the Hurst parameter can be accurately estimated from the power-law behavior of the wavelet coefficients. We use an FIR multilayer network to predict the next incoming data, then we apply the wavelet-based tool to measure the Hurst parameters of the predicted data. Based on these Hurst parameters, we apply the Norros (see IEEE JSAC, vol.15, no.2, p.200-8, 1997) formula to estimate the bandwidth requirement for each predicted data. Finally, we utilize the random early detection (RED) algorithm to traffic congestion control based on the predictive bandwidth.