Hari Om

A Secure Two-Factor Remote User Authentication and Session Key Agreement Scheme

In this article, the authors have proposed a secure two-factor remote user authentication and session key agreement protocol. As they have shown in the presented scheme, is precise and secure according to both formal and informal security analysis. For formal security analysis, they have applied BAN Burrows-Abadi-Needham logic which certifies that the presented scheme provides the amenity of mutual authentication and session key agreement safely. The informal security verification has shown that the proposed scheme is more vigorous against various sort of cruel threats. Moreover, the authors have simulated the presented scheme using broadly accepted AVISPA tool, whose simulation results make sure that the protocol is not dangerous from active and passive attacks together with replay and man-in-the-middle attacks. In addition, the performance evaluation and the security comparison have revealed that the presented scheme gives strong security as well as better complexity in the context of smart card memory requirement, communication cost and computation cost.

A secure two-factor mutual authentication and session key agreement protocol using Elliptic curve cryptography

In this paper, we propose a secure two-factor remote user authentication protocol using Elliptic curve cryptography. The proposed protocol provides the mutual authentication of participants, session key agreement and user anonymity. The security of our proposed protocol is based on one way hash function and elliptic curve cryptosystem and it is secure against all relevant security attacks. Compared with other relevant protocol, the security analysis and performance evaluation show that our proposed protocol can provide high level of security with less communication and computation cost. In addition, the BAN logic is applied to demonstrate the validity of the proposed protocol.

Hybrid data labeling algorithm for clustering large mixed type data

Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA.

A Distributed Fault-Tolerant Multi-objective Clustering Algorithm for Wireless Sensor Networks

Energy conservation is the most significant issue in wireless sensor networks due to limited power sources of sensor nodes that cannot be recharged or replaced. Clustering is considered to be energy-efficient in WSN as it reduces the number of sensor nodes taking part in long range communication. During clustering, each sensor node selects a cluster head (CH) to join a cluster among several candidate CHs. The selection of cluster head uses different parameters such as residual energy of cluster head and distance between node and cluster head. A poor selection of cluster head will lead to increased energy consumption in network that will degrade the network lifetime. Furthermore, sensor nodes are likely to fail due to different factors such as limited energy and environment hazards. So, the fault tolerance is another challenge for long run of the WSNs. In this paper, we propose a distributed fault-tolerant multi-objective clustering algorithm (DFMCA). In this algorithm, each sensor takes its decision using local information only. We also propose a recovery algorithm in case of sudden death of the cluster heads in the network. A simulation result demonstrates that our algorithm outperforms the existing state-of-the-art algorithms, namely FTCA and LBCA, in terms of network lifetime.

An energy efficient GA-based algorithm for clustering in wireless sensor networks

Energy efficient clustering has gained enormous attention in wireless sensor networks (WSNs) in the last few decades. In clustered WSNs, cluster heads (CHs) bear some additional load such as data sensing, data aggregation and sending data to base station. However, improper formation of clusters can overload some CHs and resulting higher energy consumption that may lead to deaths of such CHs. Load balanced clustering for large scale WSNs is an NP-hard problem. Evolutionary algorithms can be applied to solve such problem in a fast and efficient way. In this paper, we propose a genetic algorithm (GA) based load balancing clustering algorithm for WSNs to prolong the lifetime of the network. The proposed clustering algorithm, binds each sensor node with a CH via single or multi-hop communication, where a CH transmit all data packets of its cluster members to the base station. We perform extensive experiments on the proposed algorithm to evaluate the performance of the proposed algorithm along with some other related clustering algorithm in terms of different metrics such as network lifetime, energy efficiency etc.

A secure, efficient and lightweight user authentication scheme for wireless LAN

As demand for various multimedia applications with dissimilar quality of service rising quickly, provisioning of rapid and secure to the end clients is becoming a main challenge for the wireless network service suppliers. WLAN technologies are getting to be popular and the device is widely used everywhere the place these days, therefore a lot of issues are considered in the matter of wireless security. To prevent unauthorized access to network resources, user authentication is necessary. Since the current security protocol in WLAN particularly 802.1x is thought to be quite insecure, the IEEE 802.11i was proposed to resolve few problems in WLAN. However, the 802.1i still have some serious problems and will be accessed through the middle of the communication session. To improve a traditional security mechanism, we propose a novel lightweight authentication scheme that performs the authentications and key allotment through 2 round-trip messages between the mobile user and the authentication server. Our scheme is able to accomplish the quick rejoin process with frequently visited APs. Compared with 802.11i and other schemes, the proposed scheme not only improves efficiency by reducing computation cost and transmission cost, but also promotes security property.

Distributed energy-efficient clustering algorithm for mobile-sink based wireless sensor networks

In the energy-constrained wireless sensor networks (WSNs), clustering is an efficient technique to minimize the energy consumption of the sensor nodes. But, the clustering algorithms for WSNs with a static sink frequently suffers from uneven energy consumption problems, where cluster heads (CHs) further away from sink consume more energy in a single hop communication, with the CHs sending its data directly to the sink. In order to solve such problem, we propose a Distributed Energy-efficient Clustering Algorithm for mobile-sink based WSNs, where the sink moves around the target area with a fixed path and speed. The proposed clustering algorithm, binds each sensor node with a CH via single or multi-hop communication, where a CH transmit its data packet to the sink when distance between them is minimal. Thus, the controlled movement of the sink around the network helps in balancing the energy consumption of the sensor networks. The experimental results demonstrate the efficiency of our proposed algorithm over the existing state-of-the-art algorithms in terms of different metrics like, network lifetime, energy consumption, etc.