Trust Based Certificate Revocation for Secure Routing in MANET

AbstractMany trust establishment solutions in mobile ad hoc networks (MANETs) rely on public key certificates. Therefore, they should be accompanied by an efficient mechanism for certificate revocation and validation. In order to reduce the hazards from nodes and to enhance the security of network we propose to develop a CA distribution and a Trust based threshold revocation method. Initially the trust value is computed from the direct and indirect trust values. And the certificate authorities distributes the secret key to al the nodes. Followed by this a trust based threshold revocation method is computed. Here the misbehaving nodes are eliminated

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time

Efficient Mining of Sequential Patterns in a Sequence Database with Weight Constraint

Sequence pattern mining is one of the essential data mining tasks with broad applications. Many sequence mining algorithms have been developed to find a set of frequent sub-sequences satisfying the support threshold in a sequence database. The main problem in most of these algorithms is they generate huge number of sequential patterns when the support threshold is low and all the sequence patterns are treated uniformly while real sequential patterns have different importance. In this paper, we propose an algorithm which aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. Unlike the conventional weighted sequential pattern mining, where the weights of items are preassigned according to the priority or importance, in our approach the weights are set according to the real data and during the mining process not only the supports but also weights of patterns are considered. The experimental results show that the algorithm is efficient and effective in generating more interesting patterns

An Improved Differential Evolution Algorithm for Data Stream Clustering

A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%

Smart Acknowledgement Distributed Channel Access Scheme for TCP in MANETs

TCP upon wireless networks is most challenging issue because of random losses and ACK interference. Also, TCP suffers from performance declination in terms of creating delay and overhead in network because of poor characteristics of wireless channel. In order to overcome these issues, we proposed a Smart Acknowledgement Distributed Channel Access (SADCA) scheme for TCP in MANETs. In the proposed scheme, first a separate Access Category (AC) for data less TCP acknowledgement packets is used and then it is assigned with highest priority. In this way, delay during transmission of packet can be reduced and also packet can be acknowledged immediately. Also, to increase the performance, delay window size can be adjusted by considering the parameters such as transmission rate, number of hops, and channel occupied ratio (COR). Hence the proposed scheme helps to avoid any kind of delay and overhead for sending TCP acknowledgemen

IN VITRO HEPATOPROTECTIVE EFFECT OF ECHINOCHLOA COLONA ON ETHANOL-INDUCED OXIDATIVE DAMAGE IN HEPG2 CELLS

  Objective: The current study was aimed to evaluate the methanolic extract of caryopses of Echinochloa colona (ECME) for its in vitro hepatoprotective activity against ethanol in HepG2 cell lines.Methods: In this regard, the cytotoxicity studies were conducted for the extract, ECME using 3-(4,5-dimethythiazol- 2-yl)-2,5-diphenyl tetrazolium bromide assay to determine the inhibitory concentration 50% value based on which, the doses 50, 100, and 200 μg/ml were selected for the hepatoprotective studies in HepG2 cell lines. The toxicity was induced using ethanol (100 mM). The in vitro hepatoprotective activity of the extract was assessed based on the changes in the level of biochemical parameters such as aspartate aminotransferase, alanine aminotransferase, and lactate dehydrogenase.Results: The extract, ECME has shown a dose-dependent cytoprotective activity with maximum protection at 200 μg/ml. The percentage cell viability of the extract, ECME at 200 μg/ml was more, i.e., 69.33% which was well comparable to that of standard drug, silymarin (100 μg/ml).Conclusion: The study revealed that the extract had shown significant hepatoprotective activity at all the test doses against ethanol-induced cytotoxity assay

Two-Party Threshold Key Agreement Protocol for MANETs using Pairings

In MANET environment, the nodes are mobile i.e., nodes move in and out dynamically. This causes difficulty in maintaining a central trusted authority say Certification Authority CA or Key Generation Centre KCG. In addition most of cryptographic techniques need a key to be shared between the two communicating entities. So to introduce security in MANET environment, there is a basic need of sharing a key between the two communicating entities without the use of central trusted authority. So we present a decentralized two-party key agreement protocol using pairings and threshold cryptography ideas. Our model is based on Joux2019;s three-party key agreement protocol which does not authenticate the users and hence is vulnerable to man-in-the-middle attack. This model protects from man-in-the-middle attack using threshold cryptography

HEPATOPROTECTIVE EFFECT OF THE METHANOLIC EXTRACT OF WHOLE PLANT OF BORRERIA ARTICULARIS ON CARBON TETRACHLORIDE INDUCED HEPATOTOXICITY IN ALBINO RATS

The hepatoprotective activity of methanolic extract of Borreria articularis (L.F) F.N. Willams: (Rubiaceae) at doses of 250 mg/kg and 500 mg/kg were evaluated by carbon tetrachloride (CCl4) intoxication in rats. The toxic group which received 25%CCl4inolive oil (1 ml/kg) per oral (p.o), alone exhibited significant increase in serum ALT, AST, ALP, TBlevels. It also exhibited significant (P<0.001) decrease in TP and ALB levels. The groups received pretreatment of Borreria articularis at a dose of 250 and 500 mg/kg b.w.p.o. had reduced the AST, ALT, ALP and TB levels and the effects were compared withstandarddrug(Silymarin100mg/kgb.w.p.o).Thetotal protein (TP) and albumin (ALB) levels were significantly increased in the animalsreceived pretreatment of the extract at the moderate and higher dose levels and the histopathological studies also supported the protective effect of the extract