Analysis of Secure Routing Scheme for MANET

Mobile ad hoc networks pose various kinds of security problems, caused by their nature of collaborative and open systems and by limited availability of resources. In our work we look at AODV in detail, study and analyses various attacks that can be possible on it. Then we look into some existing mechanism for securing AODV protocol. Our proposed work is an extension to Adaptive-SAODV of the secure AODV protocol extension, which includes tuning strategies aimed at improving its performance. In A-SAODV an intermediate node makes an adaptive reply decision for an incoming request that helps to balance its load that is over-burdened by signing and verification task of incoming messages. Namely, we propose a modification to adaptive mechanism that tunes SAODV behavior. In our paper we have proposed an extension to Adaptive-SAODV of the secure AODV protocol extension, which includes further filtering strategies aimed at further improving its network performance. We have analyzed the how our proposed algorithm can help in further improvement of performance in adaptive SAODV and also compared its performance with existing mechanisms using simulation

Node replica detection in wireless sensor networks

In various applications of wireless sensor network, nodes are mostly deployed unattended and unsupervised in hostile environment. They are exposed to various kinds of security threat, and node replication attack is one among them. In this attack, an adversary captures a legitimate node from the network. Then, she creates a number of clones of the original node, and deploys them back into the network. The adversary can gain control of various network activities and launch other insider attacks using these replicas. Most of the replica detection schemes reported in the literature are centralized and location dependent. Centralized schemes are vulnerable to a single point of failure. Forwarding location information incurs additional overhead in location dependent schemes. Most replica detection schemes require exchange of membership information among nodes. To reduce communication overhead we propose two techniques called transpose bit-pair coding (TBC), and sub-mat coding (SMC) for efficient exchange of group membership information among the nodes in wireless sensor network. These schemes are lossless and do not generate false positive. Next, we propose two replica detection schemes for static wireless sensor networks called zone-based node replica detection (ZBNRD), and node coloring based replica detection (NCBRD). In ZBNRD, nodes are divided into a number of zones. Each zone has a zone-leader, who is responsible for detecting replica. ZBNRD is compared with a few existing schemes such as LSM, P-MPC, SET and RED. It is observed that ZBNRD has higher detec-tion probability and lower communication cost. In NCBRD, each node is assigned with a color (value), which is unique within its neighborhood. A color conflict within the neighborhood of a node is detected as a replica. The performance of NCBRD is compared with LSM, SET, and RED. It is found that NCBRD has higher detection probability than the above schemes and lower communication overhead than LSM and RED. The techniques for replica detection in static wireless sensor networks cannot be applied to mobile wireless sensor networks (MWSN) because of nodes mobility. We propose a technique called energy based replica detection (EBRD) for MWSN. In EBRD, the residual energy of a node is used to detect replicas. Each node in the network monitors and is monitored by a set of nodes. Conflict in the timestamp-residual energy pair of a node is detected as replica. EBRD is compared with two existing schemes EDD, and MTLSD. It is found that EBRD has excellent detection probability in comparison to EDD and MTLSD, and the communication overhead of EBRD is higher than EDD and lower than MTLSD. Simulations were performed using Castalia simulator

A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset

A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset

Viscous dark matter and 21 cm cosmology

by Jitesh R. Bhatt, Arvind Kumar Mishra and Alekha C. Naya