Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.
The concept of smart city architecture requires a comprehensive solution that can combine real-time response applications for cyber-physical systems. However, the architecture faces challenges that can obstruct the operations in terms of systems, processes, and data flow as far as the breach risk is concerned. Though the field has been researched with the existence of centralized and distributed architectures to support smart cities. Research gaps regarding security concerns, platform assistance, and resource management continue to persist. This research article presents a novel blockchain-based architecture that proposes expansion in the non-fungible tokens (NFTs) to cater to the expansion of IoT-enabled smart assets. It enables NFTs to employ fog computing for all users and smart devices connected to a fog node in a cyber-physical system. The proposed expansion suggested in Non-Fungible Tokens (NFTs) for IoT assets representation in a cyber-physical system, provides devices and user identification and authentication functionality. The proposed NFT architecture has been designed to provide a smart city solution for cyber-physical systems that ensures robust security features (such as CIA) by introducing new attributes and functions for Owner, User, Fog, and IoT device/s authentication. The validation and rigor of the security services, efficiency, and latency have been achieved by deployments on private and public ledgers. The efficiency, and cost-effectiveness of the suggested functions and components have been evaluated in terms of evaluation cost and time complexity which resulted in promising results, obtained and validated on a testnet. The evaluation cost for the devised mint component was approximately 81%, and devised approve() was approximately 23% more efficient than other solutions.
The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones' CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD, specifically while applying B-LSTM and LSTM. The system attains precision values of 89.10% and 90.16%, accuracy rates up to 91.00-91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.
This paper presents a novel synthesis of a quasi-Chebyshev Nth order stub-loaded coupled-line ultra-wideband bandpass filter. A unit element of a proposed filter topology consists of two short-circuited stubs loaded at the edges of coupled lines. A distributed equivalent circuit model of a proposed topology is extracted and used to acquire a generalized filtering function. The extracted filtering function is of rational form. The denominator of the filtering function causes a mismatch with Chebyshev type-I polynomials. For conventional narrowband filters, the denominator term can be neglected because of the close vicinity of band-edge frequencies; however, for the ultra-wideband filter response, the factor in the denominator cannot be neglected and hence requires a new mathematical procedure to compensate for the effect of the frequency-dependent term in the denominator. The electrical parameters are calculated using the proposed synthesis and used to design an ideal filter topology on ADS. To validate the proposed design procedure, fabrication is performed on a high-frequency substrate. The proposed filter is miniaturized in size and has good out-of-band performance. The simulated and measured results provide good agreement.
Ensuring the privacy and trustworthiness of smart city-Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.
Due to globalization in the semiconductor industry, malevolent modifications made in the hardware circuitry, known as hardware Trojans (HTs), have rendered the security of the chip very critical. Over the years, many methods have been proposed to detect and mitigate these HTs in general integrated circuits. However, insufficient effort has been made for hardware Trojans (HTs) in the network-on-chip. In this study, we implement a countermeasure to congeal the network-on-chip hardware design in order to prevent changes from being made to the network-on-chip design. We propose a collaborative method which uses flit integrity and dynamic flit permutation to eliminate the hardware Trojan inserted into the router of the NoC by a disloyal employee or a third-party vendor corporation. The proposed method increases the number of received packets by up to 10% more compared to existing techniques, which contain HTs in the destination address of the flit. Compared to the runtime HT mitigation method, the proposed scheme also decreases the average latency for the hardware Trojan inserted in the flit's header, tail, and destination field up to 14.7%, 8%, and 3%, respectively.
Data stored on physical storage devices and transmitted over communication channels often have a lot of redundant information, which can be reduced through compression techniques to conserve space and reduce the time it takes to transmit the data. The need for adequate security measures, such as secret key control in specific techniques, raises concerns about data exposure to potential attacks. Encryption plays a vital role in safeguarding information and maintaining its confidentiality by utilizing a secret key to make the data unreadable and unalterable. The focus of this paper is to tackle the challenge of simultaneously compressing and encrypting data without affecting the efficacy of either process. The authors propose an efficient and secure compression method incorporating a secret key to accomplish this goal. Encoding input data involves scrambling it with a generated key and then transforming it through the Burrows-Wheeler Transform (BWT). Subsequently, the output from the BWT is compressed through both Move-To-Front Transform and Run-Length Encoding. This method blends the cryptographic principles of confusion and diffusion into the compression process, enhancing its performance. The proposed technique is geared towards providing robust encryption and sufficient compression. Experimentation results show that it outperforms other techniques in terms of compression ratio. A security analysis of the technique has determined that it is susceptible to the secret key and plaintext, as measured by the unicity distance. Additionally, the results of the proposed technique showed a significant improvement with a compression ratio close to 90% after passing all the test text files.
The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.
A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network's security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network's integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.
Deep Learning , Software , Machine Learning , Confidentiality
Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, clinical datasets also suffer from this imbalance problem, and the imbalanced class distributions pose several issues in the training of classifiers. Consequently, classifiers suffer from low accuracy, precision, recall, and a high degree of misclassification, etc. We performed a brief literature review on the class imbalanced learning scenario. This study carries the empirical performance evaluation of six classifiers, namely Decision Tree, k-Nearest Neighbor, Logistic regression, Artificial Neural Network, Support Vector Machine, and Gaussian Naïve Bayes, over five imbalanced clinical datasets, Breast Cancer Disease, Coronary Heart Disease, Indian Liver Patient, Pima Indians Diabetes Database, and Coronary Kidney Disease, with respect to seven different class balancing techniques, namely Undersampling, Random oversampling, SMOTE, ADASYN, SVM-SMOTE, SMOTEEN, and SMOTETOMEK. In addition to this, the appropriate explanations for the superiority of the classifiers as well as data-balancing techniques are also explored. Furthermore, we discuss the possible recommendations on how to tackle the class imbalanced datasets while training the different supervised machine learning methods. Result analysis demonstrates that SMOTEEN balancing method often performed better over all the other six data-balancing techniques with all six classifiers and for all five clinical datasets. Except for SMOTEEN, all other six balancing techniques almost had equal performance but moderately lesser performance than SMOTEEN.
Underwater wireless sensor networks (UWSNs) have emerged as the most widely used wireless network infrastructure in many applications. Sensing nodes are frequently deployed in hostile aquatic environments in order to collect data on resources that are severely limited in terms of transmission time and bandwidth. Since underwater information is very sensitive and unique, the authentication of users is very important to access the data and information. UWSNs have unique communication and computation needs that are not met by the existing digital signature techniques. As a result, a lightweight signature scheme is required to meet the communication and computation requirements. In this research, we present a Certificateless Online/Offline Signature (COOS) mechanism for UWSNs. The proposed scheme is based on the concept of a hyperelliptic curves cryptosystem, which offers the same degree of security as RSA, bilinear pairing, and elliptic curve cryptosystems (ECC) but with a smaller key size. In addition, the proposed scheme was proven secure in the random oracle model under the hyperelliptic curve discrete logarithm problem. A security analysis was also carried out, as well as comparisons with appropriate current online/offline signature schemes. The comparison demonstrated that the proposed scheme is superior to the existing schemes in terms of both security and efficiency. Additionally, we also employed the fuzzy-based Evaluation-based Distance from Average Solutions (EDAS) technique to demonstrate the effectiveness of the proposed scheme.
Smart devices have become an essential part of the architectures such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE). In contrast, these architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. The adoption of these smart devices expands to different cyber-physical systems in smart city architecture, i.e., smart houses, smart healthcare, smart transportation, smart grid, smart agriculture, etc. The edge of the network connects these smart devices (sensors, aggregators, and actuators) that can operate in the physical environment and collects the data, which is further used to make an informed decision through actuation. Here, the security of these devices is immensely important, specifically from an authentication standpoint, as in the case of unauthenticated/malicious assets, the whole infrastructure would be at stake. We provide an updated review of authentication mechanisms by categorizing centralized and distributed architectures. We discuss the security issues regarding the authentication of these IoT-enabled smart devices. We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness. Hence, lightweight solutions in managing, maintaining, processing, and storing authentication data of IoT-enabled assets are an urgent need. From an integration perspective, cloud computing has provided strong support. In contrast, decentralized ledger technology, i.e., blockchain, light-weight cryptosystems, and Artificial Intelligence (AI)-based solutions, are the areas with much more to explore. Finally, we discuss the future research challenges, which will eventually help address the ambiguities for improvement.
Blockchain , Internet of Things , Artificial Intelligence , Cities , Computer Security
When it comes to conveying sentiments and thoughts, facial expressions are quite effective. For human-computer collaboration, data-driven animation, and communication between humans and robots to be successful, the capacity to recognize emotional states in facial expressions must be developed and implemented. Recently published studies have found that deep learning is becoming increasingly popular in the field of image categorization. As a result, to resolve the problem of facial expression recognition (FER) using convolutional neural networks (CNN), increasingly substantial efforts have been made in recent years. Facial expressions may be acquired from databases like CK+ and JAFFE using this novel FER technique based on activations, optimizations, and regularization parameters. The model recognized emotions such as happiness, sadness, surprise, fear, anger, disgust, and neutrality. The performance of the model was evaluated using a variety of methodologies, including activation, optimization, and regularization, as well as other hyperparameters, as detailed in this study. In experiments, the FER technique may be used to recognize emotions with an Adam, Softmax, and Dropout Ratio of 0.1 to 0.2 when combined with other techniques. It also outperforms current FER techniques that rely on handcrafted features and only one channel, as well as has superior network performance compared to the present state-of-the-art techniques.
Facial Expression , Facial Recognition , Anger , Emotions/physiology , Humans , Neural Networks, Computer
Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.
Computer Communication Networks , Deep Learning , Algorithms , Communication , Prospective Studies , Reproducibility of Results , Wireless Technology
In today's world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.
Deep Learning , Diabetes Mellitus , Diabetic Retinopathy , Falconiformes , Algorithms , Animals , Birds , Diabetic Retinopathy/diagnosis , Humans , Machine Learning , Retina
The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.
Purpose of the Study: The significance of creativity and performance in the workplace has been illustrated on various occasions. This study aims to find out if there is a link between transformative leadership, organizational innovation, psychological issues such as hindrance and challenge stressors, and employee creativity and employee performance. There is still a lack of awareness of the factors that influence employee performance in small and medium businesses (SMEs) in Pakistan. Pakistan's SMEs have struggled to survive in their early years, with an initial failure rate of 90 percent to 95 percent. Methodology: The relationship between hindrance and challenge stressors, organizational innovation, transformational leadership, employee creativity, and their effect on overall employee performance is established through employing Structural Equation Modeling (SEM). In this study, constructs were developed from existing theories, hypotheses were generated, data were collected from 424 SME employees, and SEM analysis was conducted to prove the suggested hypothesis. The employees of SMEs are the research study's unit of analysis. Findings: The findings of this study demonstrated that challenge stressors, transformational leadership, and employee creativity all had positive and significant effects on employee performance. Originality/Value: This is one of the first studies to study and extends existing understanding of psychological research in this manner and following correlations in a developing country, Pakistan: the links between transformational leadership and employees' perception of creativity and performance along with organizational innovation and work stressors. Based on theoretical considerations, a model is proposed, and hypotheses are established and explored. The findings of this study can help businesses increase employee performance by informing employee performance improvement methods. Business executives might learn more about how to engage and motivate employees to improve their performance.
WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it's vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs' success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird's eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN's communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.
Computer Security , Wireless Technology , Algorithms , Confidentiality , Humans
The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network's data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors' spatial operations are more intense than residents' spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists' temporal behaviors vary significantly while citizens' movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities.
Big Data , Data Mining/statistics & numerical data , Machine Learning/statistics & numerical data , Social Media/statistics & numerical data , Travel/statistics & numerical data , China , Cities , Computational Biology , Decision Trees , Geographic Information Systems , Humans , Seasons , Social Networking , Spatio-Temporal Analysis
