Abstract As organizations increasingly rely on network services, the prevalence and severity of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks have emerged as significant threats. The cornerstone of effectively addressing these challenges lies in the timely and precise detection capabilities offered by advanced intrusion detection systems (IDS). Hence, an innovative IDS framework is introduced that seamlessly integrates the extended Berkeley Packet Filter (eBPF) with powerful machine learning algorithms—specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and TwinSVM—enabling unparalleled real‐time detection of DDoS attacks. This cutting‐edge solution provides a robust and scalable IDS framework to combat DoS and DDoS threats with high efficiency, leveraging eBPF's capabilities within the Linux kernel to bypass typical user space constraints. The methodology encompasses several key steps: (a) Collection of data from the renowned CIC‐IDS‐2017 repository; (b) Processing the raw data through a meticulous series of steps, including transmission, cleaning, reduction, and discretization; (c) Utilizing an ANOVA F‐test for the extraction of critical features from the preprocessed data; (d) Application of various ML algorithms (DT, RF, SVM, and TwinSVM) to analyze the extracted features for potential intrusion; (e) Implementing an eBPF program to capture network traffic and harness trained model parameters for efficient attack detection directly within the kernel. The experimental results reveal outstanding accuracy rates of 99.38%, 99.44%, 88.73%, and 93.82% for DT, RF, SVM, and TwinSVM, respectively, alongside remarkable precision values of 99.71%, 99.65%, 84.31%, and 98.49%. This high‐speed, accurate detection model is ideally suited for high‐traffic environments such as data centers. Furthermore, its foundational architecture paves the way for future advancements, including the potential integration of eBPF with XDP to achieve even lower‐latency packet processing. The experimental code is available at the GitHub repository link: https://github.com/NemalikantiAnand/Project .

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model.

Online social networks are one of the prime modes of communication used by people to voice their opinions and sentiments, especially after the advancement of digital gadgets and overall technology. Mining such sentiments and analyzing the polarity of user opinions is a trending research issue with high business value. Identifying, detecting, and understanding sarcasm is an important topic in the field of sentiment analysis. Despite being complex and challenging, automated detection of sarcasm is also a relatively less explored research area. In this article, we present a novel sarcasm pattern detection technique using emoticons to identify sarcasm in microblogging social networks like Twitter. Initially, we classify the tweets only with emoticons based on a decision tree classification approach. Afterward, we incorporate the SentiWordNet library and a separate emoticon library to find the polarities of the tokenized words and emoticons. Finally, we present a comparison of the polarity of the tweets and the polarity of the emoticons to detect sarcasm in tweets.

The convergence of Metaverse and Internet of Things (IoT) presents new opportunities for exchanging data, but it also introduces unprecedented security challenges. With the proliferation of IoT devices, the risk of unauthorized access and data breaches is on the rise, posing significant threats to data confidentiality and integrity. To address these challenges and protect user privacy, comprehensive security solutions are essential. We propose the SafeMetaNet approach, which combines proximity-based authentication, encryption, and blockchain technology to establish secure data exchange in the IoT-Metaverse convergence. SafeMetaNet ensures data confidentiality and integrity through encryption and establishes a tamper-proof record of data exchange using blockchain technology. We evaluated the approach’s performance using various metrics, including latency, throughput, and two security metrics: data confidentiality and data integrity, and compared it with existing approaches. Our findings show that SafeMetaNet outperforms existing approaches, providing improved security. SafeMetaNet is a promising solution for secure data exchange in the IoT-Metaverse convergence.

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

Abstract The internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.

Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.

Abstract In the Industry 4.0 era, an inherited featured technology, blockchain, plays a vital role in knowledge engineering applications. Blockchain provides privacy to sensitive data as an intelligent agent, so its adoption rate increases in all the advanced domains. Especially in the health care department, blockchain technology usage helps avoid attacks like the Wannacry ransomware attack during 2017. Therefore, this paper described a decentralised application (DApp) expert system using public blockchain to create and maintain official health documents, especially medical certificates. Current existing systems, either paper‐based or database or clouds to save the medical certificates, have more scope to do attacks. Hence, proposed a blockchain‐based DApp that acts as an interface between intelligent agents, blockchains, and system related to the medical certificates. The main strength of this paper is implementation results, which are not among the maximum literary works currently available. The associate cost for conducting distributed application operations on the blockchain in terms of Gas comprehensively presented here. Furthermore, it consists of comparing the system's non‐functional functions by considering blockchain and non‐blockchain environments. Also, presented the simulation results with the performance results compared with the existed systems.

Internet of Things (IoT) data is one of the most important assets in business models for offering various ubiquitous and brilliant services. The IoT is provided with the advantage of susceptibility that cybercriminals and other malicious users. Even though smart cities are intended to extend productivity and efficiency, residents and authorities face risks when they avoid cybersecurity. The conventional blockchain methods were introduced to ensure the secure management and examination of the smart city big data. But, the blockchains are found to have computationally high costs, and failed to improve the security, not adequate resource-constrained IoT devices have been designated for smart cities. In order to address these issues, the proposed novel blockchain model called blockchain secured Merkle hash zero correlation distinguisher (BSMH-ZCD) is suitable for IoT devices within the cloud infrastructure. The objective of the BSMH-ZCD method is to enhance security and reduce the run time and computational overhead. Initially, the Merkle hash tree is used to create the hash value with every transaction. Next, the zero correlation distinguisher is applied to perform the data encryption and decryption operation for the ARX block for obtaining proficient secure data access in the IoT devices. Experimental assessment of the proposed BSMH-ZCD method and existing methods are carried out by using the taxi driver data set and Novel Corona Virus 2019 data set with different factors, such as running time, computational complexity, and security with respect to a number of blocks and executions. By using the taxi driver data set, the experimental results reveal that the BSMH-ZCD method performs better with a 19% improvement in security, 20% reduction of computational complexity, and 29% faster running time for IoT compared to existing works.

Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.

Mobile network is a collection of devices with dynamic behavior where devices keep moving, which may lead to the network track to be connected or disconnected. This type of network is called Intermittently Connected Mobile Network (ICMN) . The ICMN network is designed by splitting the region into `n' regions, ensuring it is a disconnected network. This network holds the same topological structure with mobile devices in it. This type of network routing is a challenging task. Though research keeps deriving techniques to achieve efficient routing in ICMN such as Epidemic, Flooding, Spray, copy case, Probabilistic, and Wait, these derived techniques for routing in ICMN are wise with higher packet delivery ratio, minimum latency, lesser overhead, and so on. A new routing schedule has been enacted comprising three optimization techniques such as Privacy-Preserving Ant Routing Protocol (PPARP), Privacy-Preserving Routing Protocol (PPRP), and Privacy-Preserving Bee Routing Protocol (PPBRP) . In this paper, the enacted technique gives an optimal result following various network characteristics. Algorithms embedded with productive routing provide maximum security. Results are pointed out by analysis taken from spreading false devices into the network and its effectiveness at worst case. This paper also aids with the comparative results of enacted algorithms for secure routing in ICMN.

Abstract Internet of things (IoTs) enabled cyber‐physical systems is a system that provides communication between physical devices and cyber environment. They run independently without any user interaction. Because the IoT devices are vulnerable to a variety of attacks, security is a noteworthy factor in the development process during communication. To improve secure communication with minimum time consumption, a novel technique called jackknife regressive Schmidt Samoa cryptography‐based deep artificial structure learning (JRSSC‐DASL) is introduced. Initially, the data is monitored by IoT devices and is collected from the dataset. The proposed deep artificial structure learning technique trains the gathered data with multiple layers. Then, the collected data is analysed in the first hidden layer with the help of the jackknife regression function by learning the feature and it classifies the data with higher accuracy. The classified data is sent to the next hidden layer where encryption is performed using Schmidt Samoa (SS) encryption algorithm. Then, the encrypted data is sent to the cloud server where the decryption is performed using the SS decryption algorithm. The cloud server obtains the original data and it is stored in their database for further processing. This process enhances the security of data communication and achieves high data confidentiality with less processing time. Experimental estimation is performed on the factors such as classification accuracy, confidentiality rate, processing time and memory usage to the number of data sensed from IoT device. Conferred results reveal that the proposed JRSSC‐DASL technique has high confidentiality rate and minimum processing time as well as memory usage when compared to state‐of‐the‐art methods.

Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K-nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

A mobile ad hoc network (MANET) is summarized as a combination device that can move, synchronize and converse without any preceding management. Enhancing the lifetime energy is based on the status of the concerned channel. The node is accomplished of control the control messages. Due to unplanned methods of energy conservation, the node lifespan and quality of packet flow is defaced in the existing solution. It results in a network-to-node-energy trade-off, ensuing in a failure of the post-network. This failure results in reduced time-to-live and higher overhead. This paper discusses an effective buffer management mechanism, in addition to proposing a novel performance modeling in Volunteered Computing MANET and tactile internet Next, the best execution the nodes can accomplish under fractional data is completely portrayed for utilities for a general purpose. To associate the space between network efficiency and energy conservation based on the minimal overhead, this article proposes a switch state promoting mutual Optimized MAC protocol for conservation of a node's energy and the optimal use of available nodes before their energy drain. Simulation results are provided as proof of the proposed solution. The simulation results are compared with the existing system with performance measures of delay, throughput, energy consumption, and availability of the node.

Selecting secure and dynamic watchdogs for detecting attacks using a type of intrusion detection system (IDS). The selection procedure of watchdogs in the random ad‐hoc wireless sensor network is a load creation job in the absence of a centralised controller. In this type of network, the data processing transmission for the routing process and secure watchdog selection process create overhead in each node. It drains the energy of an individual node easily. Founded on these issues, this work concentrates on the secure selection of concealed watchdogs and maintenance of optimal watchdog availability ratio. In the random ad‐hoc wireless sensor network, the secure and authorised watchdogs are selected from the neighbour list of each node on‐demand basis to provide security for the network. In addition to this work concentrates on dynamic uncertain conditions to build a secure and authenticated multi‐watchdog system in the distributed scenario. The proposed system uses the combination of both customised layer masking techniques and secure routing and monitoring techniques for the protection of random ad‐hoc wireless sensor networks.

Internet of Things (IoT) is a domain wherein which the transfer of data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, the elliptic Galois cryptography protocol is introduced and discussed. In this protocol, a cryptography technique is used to encrypt confidential data that came from different medical sources. Next, a Matrix XOR encoding steganography technique is used to embed the encrypted data into a low complexity image. The proposed work also uses an optimization algorithm called Adaptive Firefly to optimize the selection of cover blocks within the image. Based on the results, various parameters are evaluated and compared with the existing techniques. Finally, the data that is hidden in the image is recovered and is then decrypted.

Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.