It is essential for Vehicular Ad-hoc networks (VANETs) to have reliable vehicles for communication with vehicles. VANET is dynamical network where the vehicles frequently alter their place. Safe routing is of great essence at the time of routing process to fit in shared trust/belief involving these nodes. Occasionally, the malicious node transmits the counterfeit data amid other nodes. To find out trust/belief is deemed to be a difficult task when the malicious nodes try to distort the discovery of route or transmission of data within the network. Researchers have worked extensively to ensure a safe routing process with trust-oriented applications. We develop the framework based on trust with a fresh mechanism to determine DDoS attacks in VANET. The major trust elements in the evaluation of trust are frequency value statistics, trust hypothesis statistics, residual energy, trust policy, and data factor. Based on the trust elements, the generation of trust evaluation matrix takes place. We develop the suggested trust mechanism in an innovative manner to offer the security in a better manner by avoiding the trespassers in the network. The deterrence design by trust evaluation mechanism in combination with a clustering method is proficiently made use for the identification of the attacker and reduction of the price concerning detection method. The suggested system optimizes the utilization of a bandwidth without compromising the security of the nodes in the network.

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

No abstract

No abstract

No abstract

No abstract

With the increasing demand of data communication in Internet and electronic commerce environments, security of the data is the prime concern. Large-scale collaborative wireless mobile ad hoc networks may face attacks and damages due to harsh behavior of the malicious nodes. To protect the systems from the intrusion of the attackers, security of the system has to be improvised. In researches involving the designing of the intrusion detection system (IDS), performance efficiency of the system is bound to be compromised. For an effective data communication process in the secured system, there is a need for better IDS without reducing the performance metrics. Intrusion detection is the progression of monitoring node movements and data transmission events that occur in a system for possible intrusions. Distributed denial of service (DDOS) attacks are the primary threat for security in the collaborative wireless Mobile Ad hoc networks. The attacks due to DDOS are much severe when compared to the non DDOS attacks. So proper preventive measures are necessary to detect and revoke such attacks. Our proposed approach involves trustbased evaluation wherein the intrusion detection is done using secured trust evaluation policies. In this paper, a novel IDS is designed using the trust evaluation metrics. This is used for the detection of the flooding DDOS attacks in the networked architecture. The proposed system combines the existing Firecol-based security procedures with Dynamic Growing Self-Organizing Tree Algorithm in the trust evaluation-based environment. Simulation results show that the Trust-based IDS is found to be better in terms of Security metrics viz. Detection probability and Performance B M. Poongodi