PCAD: Power control attack detection in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. In this paper we have provided a solution to detect malicious nodes which perform radio transmission power control attack and sinkhole attack in wireless sensor networks. In the proposed approach, data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node in the beginning of the round for forwarding the packet towards sink. Each node adds its identity in the packet as a routing path marker and encrypts before forwarding to parent. Child node observes the parent, handles acknowledgement from 2-hop distance node and decides the trust on parent based on successful and unsuccessful transactions. Each node sends a trust value report via multiple paths to Sink at the end of the round. Sink identifies the malicious node by comparing trust value report received from each node with number of data packets received. Simulated the algorithm in NS-3 and performance analysis compared with other recently proposed approach. Simulation results show that proposed method detect the malicious nodes efficiently and early. © 2016 IEEE

CPMTS: Catching packet modifiers with trust support in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. Packet modification is a common attack in wireless sensor networks. In literature, many schemes have been proposed to mitigate such an attack but very few detect the malicious nodes effectively. In the proposed approach, each node chooses the parent node for forwarding the packet towards sink. Each node adds its identity and trust on parent as a routing path marker and encrypts only the bytes added by node in packet before forwarding to parent. Sink can determine the modifiers based on trust value and node identities marked in packet. Child node observes the parent and decides the trust on parent based on successful and unsuccessful transactions. Data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node at the beginning of a round based on its own observation on parent. Simulated the algorithm in NS-3 and performance analysis is discussed. With the combination of trust factor and fixed path routing to detect malicious activity, analytical results show that proposed method detect modifiers efficiently and early, and also with low percentage of false detection

CMNTS: Catching malicious nodes with trust support in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. In this paper we have considered suite of attacks - packet modification, packet dropping, sybil attack, packet misrouting, and bad mouthing attack, and provided a solution to detect attacks. In literature, many schemes have been proposed to mitigate such attacks but very few detect the malicious nodes effectively and also no single solution detects all attacks. In the proposed approach, each node chooses the parent node for forwarding the packet towards sink. Each node adds its identity and trust on parent as a routing path marker and encrypts only the bytes added by node in packet before forwarding to parent. Sink can identify the malicious node based on trust value and node identities marked in packet. Child node observes the parent and decides the trust on parent based on successful and unsuccessful transactions. Data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node at the beginning of a round based on its own observation on parent. Simulated the algorithm in NS-3 and performance analysis is discussed by comparing the results with other two recently proposed approaches. With the combination of trust factor and fixed path routing to detect malicious activity, simulation results show that proposed method detect malicious nodes efficiently and early, and also with low percentage of false detection

A Study on Stock Co-Movement’s Analysis of Select Bank and IT Company Stocks

The risk of a portfolio depends on the co- movement between the security returns forming the portfolio. The coefficient of correlation is an important measure for studying co movement between securities. Banking and IT company’s shares represent sizable share of market portfolio of common investors. In this perspective the present study has been undertaken to help small retail investors who commonly invest in these two major sectors to understand the co movement of returns among Banking and IT industry stocks. This study covers correlation co movement calculation between selected four Banking shares and four IT companies’ shares for a period from 16th June 2014 to 15th June 2015. The correlation between banking shares are more positive compared to correlation between IT company shares. This implies that the banking stocks return more or less move in the same direction. The correlation between Banking and IT Company stocks are either zero or negative which implies that these two sectors shares are not related or move in the opposite direction in terms of return. This implies that banking and IT industry shares are good combinations for portfolio construction which substantially reduces the risk of that particular portfolio

SDLM: Source detection based local monitoring in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. Local monitoring is one of the powerful technique to secure the data and detect various malicious activities. In local monitoring, neighbour nodes observe the communication between current sender, current receiver and next hop receiver to detect the malicious activity. To make sensors power efficient, sleep-wake scheduling algorithms along with local monitoring are suggested in literature. Solutions in the literature do not address the problem if source node is malicious and do not consider unnecessary wake up of the nodes as malicious activity. This paper tries to achieve without assuming source node as honest and considers unnecessary wake up of the node as a malicious activity. Simulated the algorithm in NS-2 and performance analysis is discussed. Even with additional checks applied to detect malicious activities, analytical results show no degradation in the performance

Robust outlier detection by de-biasing VAE likelihoods

Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image pre-processing technique -- contrast stretching -- extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages -- both with speed and performance -- over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs.Comment: To appear at CVPR 2022. 20 pages and 19 figure

Length-weight relationship of selected commercially important marine fishes from east coast of India

The paper deals with length-weight relationship (LWR) of selected commercially important marine fishes from the east-coast of India. Samples were collected fortnightly from experimental fishing using trawl operated at depth up to 70 M off Vishakhapatnam coast on the east-coast of India during 2015-17. Measurements of total length (TL) (nearest to 0.1 cm) and body weight (nearest to 0.1 g) of individual fish were taken. The LWR showed good fit with r2 values ranging from 0.975 for Lepturacanthus savala Cuvier, 1829 to 0.999 for Upeneus vittatus Forsskål, 1775. The ‘b’ values ranged from 2.618 for Photopectoralis bindus Valenciennes, 1835 to 3.186 for L. savala Cuvier, 1829

A Facile One Step Solution Route to Synthesize Cuprous Oxide Nanofluid

A cuprous oxide nanofluid stabilized by sodium lauryl sulfate, synthesized by using the one step method, has been reported. Nanofluids were synthesized by using a well‐controlled surfactant‐assisted solution phase synthesis. The method involved reduction of copper acetate by glucose in a mixture of water and ethylene glycol serving as the base fluid. The synthesized fluid was characterized by X‐ray and electron diffraction techniques, in addition, transmission and field emission microscopic techniques and Fourier transform infra red spectroscopic analysis was undertaken. The rheological property, as well as the thermal conductivity of the fluid, were measured. The variation of reaction parameters considerably affected the size of the particles as well as the reaction rate. The uniform dispersion of the particles in the base fluid led to a stability period of three months under stationary state, augmenting the thermal conductivity of the nanofluid. The method is found to be simple, reliable and fast for the synthesis of Newtonian nanofluids containing cuprous oxide nanoparticles