Middle-box based attacks create serious functional defects in the devices such as firewalls, address translators, load balancers, server units, and other data inspecting devices. Middle-box based ...attacks are more severe than terminal node-based attacks. These attacks are mainly initiated to malfunction the internal events of middle-level devices. Notably, Internet of Healthcare Things (IoHT) is a completely distributed and heterogeneous environment that leads into more vulnerable events. In the distributed medical system field, the middle-box devices manage secret medical data transactions, internal network communication, patient data protection principles and packet inspection procedures. The medical data collected from each patient needs secrecy and stability in various aspects in the IoHT environment. The middle-box based attacks injected into the IoHT nodes create inefficiency in maintaining patient data, Denial of Service (DoS) at middle-box nodes, excessive diagnosis time, and lack of data protection. For handling these issues, a new security architecture and a new attack detection model are proposed in this paper which has been developed using ranking subsets and Convolutional Neural Network (CNN) principles. The proposed CNN with ranking principles model is designed with binary class fuzzy fisher face optimization technique and flower pollination optimization method to initiate feature extraction. The extracted features of each data flow are analyzed using CNN and ranking subset methodologies. In this proposed model, the continuous involvement of middle-box events in respective devices are classified under various anomaly cases and legitimate cases. The proposed IoHT model attains maximum success rate than the existing models as indicated in implementation section.
Wireless Sensor Network (WSN) communication encounters security vulnerabilities, particularly with network traffic being susceptible to attacks during routing. The effective use of Deep Learning (DL) ...methods has been demonstrated in developing Intrusion Detection Systems (IDSs) to manage security attacks in Wireless Sensor Networks (WSN). Consequently, the development of new IDS becomes imperative, with DL and optimization algorithms offering superior attack detection capabilities. To address this need, we propose one new IDS by integrating Fuzzy Temporal rules and Artificial Bee Colony (ABC) optimization algorithm with Convolutional Neural Network (CNN) optimized with (FT-ABC-CNN) to enhance the classifier performance. To assess its effectiveness, a comparative analysis was conducted between the newly proposed FT-ABC-CNN algorithm and other classification algorithms commonly employed in Intrusion Detection System design, such as CNN, Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). Experimental evaluations revealed that the FT-ABC-CNN algorithm surpassed these comparable classifiers in terms of accuracy enhancement and reduction in false positive rates.
Two-dimensional (2D) materials possess exceptional physical and chemical properties, making them promising for various applications in electronic and optoelectronic devices. In this study, ...nanohybrids of 1D NiSe on different weight percentages (wt %) of 2D g-C
3
N
4
(0%, 1%, 3%, and 5%) were synthesized using a hydrothermal method. The structural, morphological, and optical properties of the synthesized hybrid sample were thoroughly investigated using various basic characterization techniques such as XRD, SEM, TEM, UV–Vis, Photoluminescence, N
2
adsorption–desorption, and XPS. The resulting hybrid material exhibited a high aspect ratio, few stacking layers, a considerable surface area, and an increased band gap, indicating its potential for optoelectronic applications. The exceptional physical and chemical properties of two-dimensional (2D) materials make them highly promising for various applications in electronics and optoelectronics devices. The UV–Vis analysis revealed that the 5%wt g-C
3
N
4
/NiSe hybrid material demonstrated the highest absorption and the smallest optical band gap of 2.30 eV, indicating its suitability for photo-sensing applications. The I-V characteristics analysis of the synthesized nanoparticles with an applied voltage of ± 4 V revealed an improvement in the 3 and 5 wt% g-C
3
N
4/
NiSe samples, attributed to the increase in injected charge density. Furthermore, the diode created using the 5 wt% composite showed a maximum barrier height (
Φ
b) of 0.79 eV when subjected to xenon lamp light irradiation, demonstrating its potential for utilization in the upcoming optoelectronics industry.
Graphical abstract
Wireless Sensor Networks (WSNs) is a collection of tiny distributed sensor nodes that have been used to sense the physical parameters of the environment where it has been deployed. Data dissemination ...is an important activity performed in WSNs in order to administer and manage them. Gossiping makes the network to transmit the same data item multiple times by multiple sensor nodes to their neighbors until they reach the required nodes which are in need of them. These multiple transmissions result in a problem called a Redundant Broadcast Storm Problem (RBSP). Moreover, the RBSP results in too many senders’ problem and also leads to the consumption of more energy in the network. In data dissemination, providing energy efficiency and security are the two major challenging issues. In such a scenario, the attackers may make use of the weakness in security provisions available in the network and they can perform unauthorized activities to disrupt the process of data dissemination. Hence, it is necessary to address the issues of RBSP, energy consumption, security and too many senders problem in order to enhance the reliability and security of communication in WSN for data dissemination. In this paper, a novel protocol named Cluster based Secured Data dissemination Protocol (CSDP) has been proposed for providing energy efficient and secured dissemination of data. The proposed protocol is a distributed protocol which considers the route discovery process, cluster formation, cluster head selection, cluster based routing and security through the design of a new digital signature based authentication algorithm, trust based security enhancement and encryption techniques for effective key management. The major contributions of the proposed work include the proposal of cryptography based public key and private key generation algorithms, techniques for trust score computation and malicious node identification and finally the effective prevention of malicious activities for enhancing the security of the network. Moreover, this work considers node identification techniques for effective clustering of nodes and performs optimal route discovery and secured transmission of packets. This work is novel with respect to multicast based data dissemination protocol, proposal of combined signature generation and verification schemes, encryption based key management and distributed data collection and communication techniques. In addition, an Intelligent Fuzzy based Unequal Clustering algorithm is used to perform effective clustering process and the traffic analyzer to identify the intruders by monitoring the node’s behaviors and their trust values. The proposed protocol has been extensively tested with realistic simulation parameters using NS2 simulator. The simulation results obtained from this work have proved that the proposed protocol improves the level of security through the proposal of a time efficient encryption and decryption algorithm with increase in packet delivery ratio and network throughput and at the same time it reduces the energy consumption as well as delay in data dissemination.
The management of diabetes involves much compliance, disease awareness, and patient empowerment. Blood glucose levels in people with diabetes are abnormal, resulting in various health problems that ...affect their kidneys, hearts, and vision. Monitoring the patient’s health has the benefit of using the Internet of Things (IoT) and diabetes patient monitoring systems. Because this system uses machine learning to classify data, its value goes beyond patient monitoring. So, this paper proposes a novel deep learning model with an effective feature selection mechanism for diabetes mellitus (DM) prediction in IoT-based healthcare environment. The proposed work starts by collecting the real-time IoT data of the patients from the Pima Indian Diabetes database. The data imbalance problem of the collected dataset is rectified by applying the synthetic minority oversampling technique. Afterward, the balanced dataset is preprocessed by applying missing value imputation and data standardization to improve the classification performance. Then, the hybrid algorithm called
k
-means clustering-based sailfish optimization is utilized, which performs clustering and optimization to select the essential features from the preprocessed dataset. Finally, the selected features are fed into the kaiming and switan included bidirectional long short-term memory for DM prediction. The proposed system achieves better results than the existing state-of-the-art techniques, which shows its effectiveness in predicting DM in real-time IoT datasets.
Abstract
Labelling of graphs has been the allocation of integers to the nodes or edges, or perhaps both, liable to revision. Graph coloring is indeed a specialized version of graph labelling, which ...is an allocation of labels consists of assessment to as colors to graph that are subject to several limitations. From its simplified sense it is indeed a method to color the nodes of a graph. This article deals with the harmonious coloring of central graph of some Mesh derived Architectures. A coloring is a harmonious coloring if it is a proper coloring with the intension of a couple of colors emerges towards one edge at the most.
Wavelet method is a recently developed tool in applied mathematics. The mathematical model of the steady-state immobilized enzyme electrodes is discussed. This theoretical model is based on ...one-dimensional heat conduction equations containing a non-linear term related to Michaelis–Menten kinetics. An efficient Chebyshev wavelet-based technique is applied to solve the non-linear diffusion equation for the steady-state condition. A simple expression of the substrate concentration is obtained as a function of the Thiele modulus
(
ϕ
p
)
and
β
(kinetic parameter). The wavelet results are compared with the numerical and HPM solutions and found to be in good agreement.
Lactic acid bacteria (LAB) have the potential to degrade intestinal oxalate and this is increasingly being studied as a promising probiotic solution to manage kidney stone disease. In this study, ...oxalate degrading LAB were isolated from human faeces and south Indian fermented foods, subsequently assessed for potential probiotic property in vitro and in vivo. Based on preliminary characteristics, 251 out of 673 bacterial isolates were identified as LAB. A total of 17 strains were found to degrade oxalate significantly between 40.38% and 62.90% and were subjected to acid and bile tolerance test. Among them, nine strains exhibited considerable tolerance up to pH 3.0 and at 0.3% bile. These were identified as Lactobacillus fermentum and Lactobacillus salivarius using 16S rDNA sequencing. Three strains, Lactobacillus fermentum TY5, Lactobacillus fermentum AB1, and Lactobacillus salivarius AB11, exhibited good adhesion to HT-29 cells and strong antimicrobial activity. They also conferred resistance to kanamycin, rifampicin, and ampicillin, but were sensitive to chloramphenicol and erythromycin. The faecal recovery rate of these strains was observed as 15.16% (TY5), 6.71% (AB1), and 9.3% (AB11) which indicates the colonization ability. In conclusion, three efficient oxalate degrading LAB were identified and their safety assessments suggest that they may serve as good probiotic candidates for preventing hyperoxaluria.
Mobile edge computing has been widely used in various IoT devices due to its excellent computing power and good interaction speed. Task offloading is the core of mobile edge computing. However, most ...of the existing task offloading strategies only focus on improving the unilateral performance of MEC, such as security, delay, and overhead. Therefore, focus on the security, delay and overhead of MEC, we propose a task offloading strategy based on differential privacy and reinforcement learning. This strategy optimizes the overhead required for the task offloading process while protecting user privacy. Specifically, before task offloading, differential privacy is used to interfere with the user's location information to avoid malicious edge servers from stealing user privacy. Then, on the basis of ensuring user privacy and security, combined with the resource environment of the MEC network, reinforcement learning is used to select appropriate edge servers for task offloading. Simulation results show that our scheme improves the performance of MEC in many aspects, especially in security and resource consumption. Compared with the typical privacy protection scheme, the security is improved by 7%, and the resource consumption is reduced by 9% compared with the typical task offloading strategy.
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