Description:

In today's digital age, the ever-increasing dependency on computer technology has left the average citizen vulnerable to crimes such as data breaches and possible identity theft. These breaches often target social media networks such as Facebook, LinkedIn, Instagram and Twitter. This emerges the incentive for social networks to improve their cyber security. Our project intends to build an artificial intelligence solution to prevent the dangers of a bot in the form of a fake profile on social media. The deep learning algorithm, Artificial Neural Network(ANN) determines the possibility of a social media profile to be authentic. The parameters of the social media network that drive a breach are also identified in the project and a web browser plugin is built to identify these fake profiles.

Name(s): VEMPATI RAMYA SRAVANI(17H61A05B6), SHEEMA TABASSUM( 17H61A05A7),TATA LOKESH CHANDRA(17H61A05A8)

Description:

A new decentralized access control scheme for secure data storage in clouds, which supports anonymous authentication. In the proposed scheme, the cloud verifies the authenticity of the user without knowing the user’s identity before storing the data. This has the added feature of access control in which only valid users are able to decrypt the stored information. The scheme prevents replay attacks and supports creation, modification and reading data stored in the cloud. And also addresses user revocation. It is a decentralized, robust and access control schemes designed for cloud. Access control mechanism are decentralized which makes it robust when compared to centralized access control schemes meant for clouds.

Name(s):A. Madhukar (17H61A0562),S. Mani deep (17H61A05A5), J. Akshitha (17H61A0584)

Description:

Cardiotocography (CTG) is the means of measuring the fetal heart rate, movements, and uterine contractions, thus continuously monitoring the health of the mother and child. The equipment used to perform the monitoring is called a cardiotocograph and work using ultrasound pulses. This is a simple and cost-effective solution for assessing fetal health, thus allowing professionals to take necessary action.The objective is to help clinicians and families to better predict fetal wellness besides the traditional pregnancy tests using machine learning techniques.

Name(s):Team Members: D.NISHITHA(17H61A05C9), D.HANEEF (17H61A05D2), NEHA FIRDOUS (17H61A05F5).

Description:

The project is mainly focussed on credit card fraud detection in real world. A phenomenal growth in the number of credit card transactions, has recently led to a considerable rise in fraudulent activities. The purpose is to obtain goods without paying, or to obtain unauthorized funds from an account. Implementation of efficient fraud detection systems has become imperative for all credit card issuing banks to minimize their losses. One of the most crucial challenges in making the business is that neither the card nor the cardholder needsto be present when the purchase is being made. This makes it impossible for the merchant to verify whether the customer making a purchase is the authentic cardholder or not. With the proposed scheme, using random forest algorithm the accuracy of detecting the fraud can be improved. Classification process of random forest algorithm to analyse data set and user current dataset.

Name(s):R:HEMANTH(17H61A05G2),SUMANTH(17H61A05D7),
G.ENOCH(17H61A05D4)

Description:

The increase in the usage of automated systems has brought an increase in the amount of personal information in the electronic form ; and as a result there is a need for confidentiality. The most common method for authentication is textual passwords . But textual passwords are vulnerable to shoulder surfing , dictionary attacks etc. Most of the graphical passwords are vulnerable to shoulder surfing. To address this problem , we generate session passwords for authentication. Session passwords are one time passwords which are used only once and for every login a new session password is generated. The session passwords provide better security as password changes for every session.

Name(s):GADDAM SITARAM REDDY (17H61A0516) ,MACHA MEGHANA,(17H61A0535) THADIGOPPULA SAITEJA (17H61A0551)