himansu sekhar Pattanayak

The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. This article aims to provide a comprehensive study of the IoT and Android systems. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. The article emphasizes the role of the developer in secure application design. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. Th...

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

In recent years, with the growing recognition of cloud computing, data outsourcing has become a widely used service. Also, to preserve privacy and security of sensitive data, it is important to encrypt the data before outsourcing it to the cloud. Due to such requirements, reversible data hiding in encrypted image (RDH-EI) has received significant interest from the researchers. In this paper, a new technique for RDH- EI is suggested based on invertible image conversion unlike the conventional RDH-EI techniques. In this method, instead of generating and storing the encrypted image in unintelligible form (cipher formed image) which may easily attract the attention of intruders or hackers, it allows the user to convert the semantical content of original image into the semantical content of a freely selected target image of the same size. Image block mapping is performed between original and target images using mean absolute deviation and a compression technique is applied for reducing t...

Community structure is a common phenomenon observed in various social networks. In this work, a novel community detection algorithm is proposed by estimating the effect of community structure on the average path length of a network. The Erdos-Renyi graph model is used as a reference to compute the change in the average path length of a network due to community formation. By experimenting with artificial networks, it is found that community structure in a social network contributes towards the lengthening of the average shortest path length. A random graph is found to have a lesser average shortest path length than a social network with community structure. As the size of individual communities increases, there is a decrease in the difference of average shortest path lengths, compared with a random graph containing an equal number of nodes and edges. This relationship is used to predict the average community size and their numbers in a network. The findings mentioned above are applie...

Community detection is one of the key areas of social network analysis. There are various community detection algorithms available in the literature. Numerous community metrics are also available to evaluate the detected communities. In our study, by using synthetic networks, we compare between four well known community metrics, namely; modularity, conductance, coverage and performance. We also compare seven different community detection algorithms based on above mentioned parameters.

Abstract Community detection is one of the key problem areas in social network analysis. It is classified as an NP-hard problem. Although a lot of good approximate algorithms are available for finding clusters in social networks, all those algorithms have serious drawbacks. They have high time complexities and are inefficient in finding shared communities, a phenomenon common in real-world social networks. In this work a novel community detection algorithm is proposed, that is inspired by the fire propagation model, and is named as Fire Spread community detection algorithm (FSA). When a place catches fire, its nearby places are also at risk of getting burned by it. Getting burned by fire depends on several factors, such as distance from the source of fire, the combustive threshold of the material, number of neighbors already in burning state and heat transmission property of the gap between source and current place. Finding a community near a seed node resembles the phenomena of fire propagation originating from a source. Finally, the proposed algorithm is validated by comparing the modularity and conductance scores with some of the state-of-the-art community detection algorithms for which synthetic and real-world networks are used.

There has been much research work done on the improvement of adhoc routing protocols to enhance network performances. But in these networks, performance is very much dependent on wireless physical layer where communication is quite complex. The effect of the combination of different routing strategies (protocols) and physical channel properties can vary the network performance up to a great extend. Gray zone effect is one of such effects, which results in high data loss and degradation of performance. Generally, in adhoc networks, broadcast control packets have lower data rate and user packets have same or higher data rates. Low data rate packets can travel longer distance than high data rate packets. Packet sizes of control packets are smaller compared to data packets, so control packets can cover greater distance than other packets. Due to these reasons, communicating nodes find valid paths which data packet cannot cover. This is known as gray zone effect. In our proposed work, we...

There has been much research work done on the improvement of adhoc routing protocols to enhance network performances. But in these networks, performance is very much dependent on wireless physical layer where communication is quite complex. The effect of the combination of different routing strategies (protocols) and physical channel properties can vary the network performance up to a great extend. Gray zone effect is one of such effects, which results in high data loss and degradation of performance. Generally, in adhoc networks, broadcast control packets have lower data rate and user packets have same or higher data rates. Low data rate packets can travel longer distance than high data rate packets. Packet sizes of control packets are smaller compared to data packets, so control packets can cover greater distance than other packets. Due to these reasons, communicating nodes find valid paths which data packet cannot cover. This is known as gray zone effect. In our proposed work, we...