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 trust-based 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 metrics viz. Packet Data Ratio, Average Delay, Throughput and Energy Consumption.

A Novel Intrusion Detection System Based on Trust Evaluationto Defend Against DDoS Attack in MANET

Detection and Prevention System towards the Truth of Convergence on Decision Using Aumann Agreement Theorem

The DDoS attack is more vulnerable attack in the self structured and infra structure less MANET environment. The bot net based flooding attack is very cheap and easy to implement. Many detection and prevention mechanisms has been proposed to avoid DDoS attack. Here we have proposed the stochastic model of covariance matrix analysis in which the entropy difference has been computed by the frequency distribution of rule metrics against the threshold entropy periodically which in turn difference is highest can be controlled by reCAPTCHA controller to defend against DDoS attack. The prevention mechanism by trust evaluation using clustering methodology used to identify the attacker and reduce the cost of detection mechanism. The Aumann agreement theorem based trust re-evaluation is also proposed in order to reduce the false positive and negative probabilities, such that the accuracy of the system is enhanced. Finally the nodes identified as attacker by their trust values will be evicted and rekeying has been done. The experimental analysis in NS2 with CAIDA dataset shows true positive rate and detection rate is high in the proposed mechanism.

Stochastic model: reCAPTCHA controller based co-variance matrix analysis on frequency distribution using trust evaluation and re-eval by Aumann agreement theorem against DDoS attack in MANET

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%) 

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Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT

Wireless mobile networks consist of autonomous with a limited battery life; there are many challenges in identifying false data. False data is generally detected and dropped by the sink, but the false data travelling in the network causes a lot of battery wastage. The work presented in this paper concerns about filtering the false data in the routing nodes before it reaches the sink and saves the resources. The proposed solution involves using advanced encryption standard (AES) by deploying keys to the mobile ad hoc nodes when the network is first deployed. The data is verified by usage of message authentication code. Location and the time stamp of the forwarding nodes is monitored to detect unusual activity by the forwarding nodes.

S. Bose

Papers

The effective intrusion detection system using optimal feature selection algorithm