Client Side Script Phishing Attacks Detection Method using Active Content Popularity Monitoring

The phisher can attack the client side script by means of threatening information which affects the majority of online users in sequence. The malicious users steal a variety of sensitive information from financial organizations in order to run nameless client side script in the phishing attack. In most of the time, the consumer will ignore association script and popup windows which in turn run a set of malicious processes and send the sensitive information to the remote sites. To secure consumers by limiting the client side script, an effective Client Side Script Phishing Attack Detection (CSSPAD) method is proposed to detect the client side script phishing attacks. The proposed methodis based on Active Content Popularity Monitoring (ACPM) and client script classification methods. This method categorizes the client side script according to a mixture of factors like the quantity of information being transferred by the script, the parent information of the script is being accessed. The proposed method computes the active time of the script, amount of data transferred and popularity of the webpage

QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-to-performance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for large-scale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques

QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-to-performance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for large-scale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques

Campus Selection Procedure Android App Project

This project is intended to create a web application for university training majors.A system is a web application that, if properly launched, can be accessed throughout theorganization and beyond. This system can be used as a university degree application(implementation supervisor) to handle student training information Participants must be able tosubmit their CV. Visitors/company representatives and students can find/search all informationsubmitted

Security enhancement of cyber-physical system using modified encryption AESGNRSA technique

A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time aspects of these systems. This research has designed a modified encryption technique named the advanced encryption standard in galois counter mode with nonce and rivest-shamir-adleman algorithm (AESGNRSA). A smart medical system is designed to monitor the health of remotely located patients. The AESGNRSA algorithm is applied to the three servers of this system. The data of 1.5 lakh patients is fed to this system to verify the effectiveness of the AESGNRSA algorithm. The performance parameters like encryption and decryption time, encryption and decryption throughput, and encrypted file size are calculated for the AESGNRSA algorithm. The comparative analysis proved that AESGNRSA has the highest performance as compared to other algorithms and it can protect CPS against many cyber-attacks

Profitability of Cotton on a Pest Management Continuum in Guntur District of Andhra Pradesh

The plant protection response of farmers in the Guntur district of Andhra Pradesh has been examined with particular reference to the adoption of Bt cotton varieties and IPM components. The farmers have been found to follow a wide range of practices to manage the insect pests in cotton. The use of chemical insecticides has accounted for, about 37 per cent of the total variable costs. No significant reduction in plant protection expenditure has been recorded on adoption of Bt varieties without IPM practices. The adoption of IPM practices, however, has led to reduced use of insecticides and increased profitability. The saving on plant protection chemicals has more than compensated the cost of adopting IPM components. Consequently, the net returns have been found increased considerably from cotton cultivation.Crop Production/Industries,

An experimental study to assess the effectiveness of nursing strategies on quality of life among elderly living in selected old age homes at Chennai.

Aging compromises the physical and psychological faculties of elderly. Deficits in the quality of social relationships lead to feelings of isolation and loneliness in elderly which is a risk factor for poor physical and mental health. Aims: (a) To assess the level of quality of life (QOL) among experimental and control group in the pre and post test. (b) To determine the effectiveness of nursing strategies among experimental and control group. (c) To associate the level of quality of life with selected demographic variables in experimental group. Methodology: An experimental study was done using Modified WHOQOLBREF scale in two settings. A total of 30 samples were selected by simple random sampling in each setting. Intervention was given to experimental group which included physical exercise, group work and recreational activities for about two weeks. Data was analyzed with descriptive and inferential statistics. Results: About 19(63%) participants in experimental group and 24(80%) in control group had poor QOL in the pre test. The mean overall QOL of experimental group was 57.9 in the post test which was 36.1 in the pre test. There was a significant difference (p>0.001) in the level of QOL among experimental group before and after the nursing strategies. There was a significant difference (p>0.001) in the level of QOL between experimental and control group after the nursing strategies. There was a significant association (p>0.05) between age, educational status, monthly income, duration of stay at old age home and the level of QOL in experimental group. Conclusion: Structured program of activities would be helpful for the elderly in order to overcome the loneliness and for the better QOL

MicroRNA-214 targets PTK6 to inhibit tumorigenic potential and increase drug sensitivity of prostate cancer cells.

Prostate cancer is the most commonly diagnosed cancer in men with African American men disproportionally suffering from the burden of this disease. Biomarkers that could discriminate indolent from aggressive and drug resistance disease are lacking. MicroRNAs are small non-coding RNAs that affect numerous physiological and pathological processes, including cancer development and have been suggested as biomarkers and therapeutic targets. In the present study, we investigated the role of miR-214 on prostate cancer cell survival/migration/invasion, cell cycle regulation, and apoptosis. miR-214 was differentially expressed between Caucasian and African American prostate cancer cells. Importantly, miR-214 overexpression in prostate cancer cells induced apoptosis, inhibiting cell proliferation and colony forming ability. miR-214 expression in prostate cancer cells also inhibited cell migration and 3D spheroid invasion. Mechanistically, miR-214 inhibited prostate cancer cell proliferation by targeting protein tyrosine kinase 6 (PTK6). Restoration of PTK6 expression attenuated the inhibitory effect of miR-214 on cell proliferation. Moreover, simultaneous inhibition of PTK6 by ibrutinib and miR-214 significantly reduced cell proliferation/survival. Our data indicates that miR-214 could act as a tumor suppressor in prostate cancer and could potentially be utilized as a biomarker and therapeutic target