Sivakumar N

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Padmavathy T.V

Venkatesh P

Bhargava D

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Cluster Computing

As health data are very sensitive, there is a need to prevent and control the health data with end-to-end security solutions. In general, a number of authentication and authorization schemes are available to prevent and protect the sensitive data, which are collected with the help of wearable Internet of Things (IoT) devices. The transport layer security (TLS) protocol is designed to transfer the data from source to destination in more reliable manner. This protocol enables a user to overcome the no lost or reordered messages. The more challenge with TLS is to tolerate unreliability. In order to overcome this issue, Datagram transport layer security (DTLS) protocol has been designed and used in low-power wireless constrained networks. The DTLS protocol consists of a base protocol, record layer, handshake protocol, ChangeCipherSpec and alert protocol. The complex issue with the DTLS protocol is the possibility of an attacker could send a number of ClientHello messages to a server. This scenario would cause a denial-of-service (DOS) attack against the server. This DoS attack enables new connection between the attacker and server, increasing attacker bandwidth, and allocation of resources for every ClientHello message. In order to overcome this issue, we have proposed a smart gateway-based authentication and authorization method to prevent and protect more sensitive physiological data from an attacker and malicious users. The enhanced smart gateway-based DTLS is demonstrated with the help of Contiki Network Simulator. The packet loss ratio is calculated for the CoAP, host identity protocol, CoAP-DTLS and CoAP-enhanced DTLS to evaluate the performance of the proposed work. Data transmission and handshake time are also calculated to evaluate the efficiency of the enhanced DTLS. © 2017, Springer Science+Business Media, LLC.

The Journal of Supercomputing

Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application

Most of the advancements are now carried out by interconnecting physical devices with computers; this is what known as Internet of Things (IoT). The major problems facing by blind people fall in the category of navigating through indoor and outdoor environments consisting of various obstacles and recognition of person in front of them. Identification of objects or person only with perceptive and audio information is difficult. An intelligent, portable, less expensive, self-contained navigation and face recognition system is highly demanded for blind people. This helps blind people to navigate with the help of a Smartphone, global positioning system (GPS) and a system equipped with ultrasonic sensors. Face recognition can be done using neural learning techniques with feature extraction and training modules. The images of friends, relatives are stored in the database of user Smartphone. Whenever a person comes in front of the blind user, the application with the help of neural network gives the voice aid to the user. Thus this system can replace the regular imprecise use of guide dogs as well as white sticks to help the navigation and face recognition process for people with impaired vision.In this paper, we have proposed a novel image recognition and navigation system which provides precise and quick messages in the form of audio to visually challenged people so that they can navigate easily. The performance of the proposed method is comparatively analyzed with the help of ROC analysis. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.

Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things

Honeypot Internet of Things (IoT) (HIoTPOT) keep a secret eye on IoT devices and analyzes the various recent threats which are dangerous to IoT devices. In this paper, implementation of a research honeypot is presented which is used to learn the recent tactics and ethics used by black hat community to attack on IoT devices. As IoT is open and easy for accessing, all the intruders are highly attracted towards IoT. Recently Telnet based attacks are very famous on IoT devices to get easy access and attack on other devices. To reduce these kinds of threats, it is necessary to know in details about intruder, therefore the aim of this research work is to implement novel based secret eye server known as HIoTPOT which will make the IoT environment more safe and secure. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

HIoTPOT: Surveillance on IoT Devices against Recent Threats

Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision-making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model. © 2017 Elsevier B.V.

Journal	Data powered by TypesetCluster Computing
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1386-7857
Open Access	0