Dr. P SUBHASH
@vnrvjiet.ac.in
Associate Professor
VNR Vignana Jyothi Institute of Engineering & Technology
RESEARCH INTERESTS
Network security, Internet of Things, Wireless Sensor Networks
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
V. Surya Narayana Reddy, P. Subhash, Ch. Vinay Kumar, M. Samuel, G. Vishal Reddy, and G. Tharun
Springer Nature Singapore
A. Ranjith, P. Subhash, G. Nagaraju, M. Srinivas, and A. Prashanth
Springer Nature Singapore
P. Subhash, Mohammed Qayyum, C. Likhitha Varsha, K. Mehernadh, J. Sruthi, and A. Nithin
Springer Nature Singapore
P Subhash
IEEE
One of these rapidly evolving technologies, the Internet of Things (IoT), is increasingly demonstrating its capacity to enable more efficient and sustainable smart city infrastructure. Smart cities rely heavily on IoT apps because of their many advantages and ease of integration. The "Internet of Things in Industry," also known as the Industrial Internet of Things (IIoT), is a domain where linked devices are utilized to deliver a range of services related to operational technology, manufacturing, utilities, and machine monitoring. IoT is however susceptible to a number of significant security problems that need to be addressed because of its broad use. In environments where IIoT resources are limited, the Routing Protocol for Low Power and Lossy Networks (RPL), based on IPv6, is the ideal choice. Additionally, RPL is more susceptible to a variety of attacks and, if they are not handled, could result in a decline in network performance. We propose the study of rank attacks and their impacts on a network’s packet delivery ratio (PDR), throughput, inter-packet time, and power consumption are the main objectives of this work. Further, the examination is surely helpful in estimating the performance losses in the network and accordingly devising the mitigation models further. To this end, the results of the study have clearly shown evidence that the attack has significantly lowered the performance of RPL-based IoT and IIoT contexts.
Et al. T.Sunil Kumar
Science Research Society
Numerous studies have been conducted using Deep Learning paradigms to detect Breast Cancer. Breast cancer is a medical condition where abnormal cells in the breast grow uncontrollably, forming tumors. If not treated, these tumors can metastasize and spread to other parts of the body, potentially leading to life-threatening consequences. In the year 2020, there were approximately 2.3 million cases of breast cancer diagnosed in women, leading to around 685,000 deaths worldwide. By the close of 2020, there existed 7.8 million women who had been diagnosed with breast cancer within the preceding five years, solidifying it as the most widespread form of cancer globally. Breast cancer is observed across all countries, affecting women at various ages post-puberty, with incidence rates tending to rise in older age groups. The aim of this paper is to classify and predict the class labels of breast cancer. To achieve this, a ResNet50 model is utilized and mammography images are employed to locate cancer within the image and classify it to emphasize the affected area. The ResNet50 identifies mass regions and classifies them as either ductal carcinoma, inflammatory, triple negative or invasive cancer. The experimentation is carried out on breast cancer dataset and achieved 90.6% accuracy both for classification as well as prediction
Siva Surya Narayana Chintapalli, S. P. Paramesh, G. S. Nijaguna, Jane Rubel Angelina Jeyaraj, and P. Subhash
Springer Science and Business Media LLC
Sasikumar Gurumoorthy, Parimella Subhash, Rocio Pérez de Prado, and Marcin Wozniak
MDPI AG
Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.
P. Subhash, K. Samrat Surya, and A Brahmananda Reddy
IEEE
The recent arrival of Smart Grid has influenced the creation of Big Data in the field of energy consumption. Smart Grid provides with an efficient and reliable end to end two-way delivery system. The electricity consumption for an individual user is available at real time. Also, enables real-time monitoring and controlling of power system from utilities perspective, which helps them reducing the power losses. So, it is going to replace the old methodologies of sharing electricity, to fulfill the exponential needs of electricity in terms of flexibility, reliability and quality, etc. The aim of the study is to find the possible number of appliances used by an individual user and accordingly find the star rating of the individual appliance. Further will be informing user to upgrade specific appliance with a lower star rating to a higher star rating. This would eventually help appliance development companies with respect to targeted advertisement. Also, the cost to the energy consumed would be reduced. So, it is going to help users to save their money on energy consumption and would also help to save energy. This would eventually lead to a healthy environment for the conservation of energy.
P. Subhash, Gollapudi Ramesh Chandra, and K. Samrat Surya
IEEE
Internet of Things (IoT) has become a part of our daily life; these provide information like sensory data which could help us in driving a vehicle with ease, electricity management in homes, health care, and many more. In all the scenarios, the information is generally in the form of log data used to measure certain parameters to figure out the solutions matching to the applications. As this information is very sensitive, there are chances of capturing the device and tampering the data causing sever performance degradation of the network. The possibility of attacks on these devices is mainly of two types one is of physical attack which could occur due to accidents and intentional damage to the device and the second type of possible attack is cyber-attack like tampering the information, denial-of-service, and compromising the device. In this paper, we propose a Power Trust mechanism to assign trust value for each node of the network based on the energy auditing. This energy auditing is done with reference to CPU cycles, data received, data processed, data sent network performance, and power consumption. Using the energy auditing model, we calculate trust values of every node present in the network dynamically and predict the physical attacks and cyber-attacks. With the method introduced, IoT devices would be better monitored and secured.
P. Subhash, P. Navya Sree, and M. Pratyusha
Springer Singapore
P. Subhash, N. Venkata Sailaja, and A. Brahmananda Reddy
Springer Singapore
The scale of social network data that is being generated is increasing exponentially day by day. Public and private opinion of various subjects or issues are expressed in social media. Sentiment analysis is a method of analyzing the sentiment of a statement that it embodies. Twitter is one of the social medias that is gaining popularity nowadays and most people are using this platform to express their opinions. Sentiment analysis on Twitter is an application of analyzing the sentiment of twitter data(tweets) conveyed by the user. The research on this problem statement has grown consistently. The main reason behind this is the challenging format of tweets that are posted, and it makes the processing difficult. The tweet format would be the number of characters, slangs, abbreviations, emojis, http links and so on. In this paper the aim to describe the methodologies adopted, the process and models applied, along with a generalized approach using python. Sentiment analysis aims to determine or measure the attitude of the writer with respect to some topic.
P Subhash and S Ramachandram
IEEE
Authenticated mesh peering exchange (AMPE) is one of the core functionalities of wireless mesh network(WMN) that facilitates mesh routers to discover their peers (neighbours), securely. Even though the AMPE protocol prevents unauthorized neighbours from becoming part of the network, it fails to prevent relay attacks, where an attacker can simply relay frames used to establish peer-links. The motivation of an attacker is to convince two far-away nodes as neighbours, and make them commit to a non-existent link that acts as a wormhole later. In this paper, we address this problem of relay attacks and propose a secure neighbour discovery mechanism that detects non-existent network links. It relies on a ranking mechanism to compute relative distance between neighbours, and employs connectivity information to validate those links.
P. Subhash and S. Ramachandram
Springer International Publishing
P. Subhash and S. Ramachandram
IEEE
Wireless Mesh Networks (WMNs) has become an emerging technology in recent days due to its easy deployment and low setup cost. In WMN, Routing protocols play an important role and these are susceptible to various kinds of internal attacks. One such attack that has severe impact on a WMN is a wormhole attack. A Wormhole is a low-latency link between two parts of the network through which an attacker tunnels network messages from one point to another point. In this paper, we specifically focus on wormhole attack launched by colluding nodes referred to as Byzantine wormhole attack. Unfortunately, most of the existing wormhole defense mechanisms are either centralized, or rely on additional hardware. The major challenge in detecting a byzantine wormhole link is the inability to distinguish nodes involved in the attack process, as they form the legitimate part of network. Being legitimate part of the network, they can bypass all security mechanisms and timing constraints imposed by the network. In this paper, we propose a mechanism to prevent byzantine wormhole attack in WMNs. The proposed mechanism relies on digital signatures and prevents formation of wormholes during route discovery process and it is designed for an on-demand hop-by-hop routing protocol like HWMP (Hybrid Wireless Mesh Protocol-the default routing protocol for WMN). This is simplistic and also applicable to source routing protocols like DSR. This is a software based solution and does not require additional (or) specialized hardware.
Mohammed Qayyum, P. Subhash, and Mohammed Husamuddin
IEEE
Mobile Ad Hoc Networks (MANETs) are exposed to several extra threats as compared to legacy wireless networks due to their nature. The security management in MANETs, where node level security monitoring plays an integral role in maintaining security and the problem of measuring the overall security level of MANETs. This paper presented the major components of the security level of MANETs. Security issues of Data Query Processing and Location Monitoring. The security level estimation architecture, security level classification and in applications is also presented.