Multivariate Broadcast Encryption with Group Key Algorithm for Secured IoT

The expanding and ubiquitous availability of the Internet of Things(IoT)have changed everyone’s life easier and more convenient.Same time it also offers a number of issues,such as effectiveness,security,and excessive power consumption,which constitute a danger to intelligent IoT-based apps.Group managing is primarily used for transmitting and multi-pathing communications that are secured with a general group key and it can only be decrypted by an authorized group member.A centralized trustworthy system,which is in charge of key distribution and upgrades,is used to maintain group keys.To provide longitudinal access controls,Software Defined Network(SDN)based security controllers are employed for group administration services.Cloud service providers provide a variety of security features.There are just a few software security answers available.In the proposed system,a hybrid protocols were used in SDN and it embeds edge system to improve the security in the group communication.Tree-based algorithms compared with Group Key Establishment(GKE)and Multivariate public key cryptosystem with Broadcast Encryption in the proposed system.When all factors are considered,Broadcast Encryption(BE)appears to become the most logical solution to the issue.BE enables an initiator to send encrypted messages to a large set of recipients in a efficient and productive way,meanwhile assuring that the data can only be decrypted by defining characteristic.The proposed method improves the security,efficiency of the system and reduces the power consumption and minimizes the cost.

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information.Based on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating process.Since,the large volume of data is generated and transferred through network,the security and performance are remained an issue.IDS(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network systems.The performance and loss are still an issue because of the features space grows while detecting the intruders.In this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing.The proposed system includes three phases such as preprocessing,feature selection and classification.In the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method.In second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks efficiently.The clustering loss and network loss can be reduced with the optimization algorithm.We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics.The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots.

Improved Key Agreement Based Kerberos Protocol for M-Health Security

The development of wireless sensor network with Internet of Things(IoT)predicts various applications in the field of healthcare and cloud computing.This can give promising results on mobile health care(M-health)and Telecare medicine information systems.M-health system on cloud Internet of Things(IoT)through wireless sensor network(WSN)becomes the rising research for the need of modern society.Sensor devices attached to the patients’body which is connected to the mobile device can ease the medical services.Security is the key connect for optimal performance of the m-health system that share the data of patients in wireless networks in order to maintain the anonymity of the patients.This paper proposed a secure transmission of M-health data in wireless networks using proposed key agreement based Kerberos protocol.The patients processed data are stored in cloud server and accessed by doctors and caregivers.The data transfer between the patients,server and the doctors are accessed with proposed protocol in order to maintain the confidentiality and integrity of authentication.The efficiency of the proposed algorithm is compared with the existing protocols.For computing 100 devices it consumes only 91milllisecond for computation.