Automated Social Engineering (ASE) is how social networking sites (SNSs) are exploited for Social Engineering by automated bots. Classical social engineering is an attack on the security of systems, based on exploiting human factors. ASE is an automated form of traditional social engineering which makes use of bots to attack SNS. One such bot is KOOBFACE [1] that infected Facebook for a long time until it was detected in mid of 2011 by Sophos lab. ASE bots can be developed easily using open source web automation and web scrapping tools. These tools combined with appropriate chat logic with enhanced intelligence pose a great threat to the security of SNSs. Countermeasures like Captchas have proved ineffective in preventing bots from infiltrating SNS's. New techniques like Multi Modal Captchas (MMC), and Fast Flux Network (FFN) detection are the future of the ASE prevention. In this paper we present a survey of vulnerabilities, threats and propose some countermeasures for Automated Social Engineering.
Nowadays, automobile thefts are increasing at an alarming rate all over the world. Security of their vehicle has always been a concern to people. We are developing secured vehicle tracking and control system. In this system the user will be able to control his vehicle through an android based Smartphone. A secured mode of communication between Smartphone and vehicle is established via GSM network. Using his/her Smartphone, the owner will be able to lock/unlock the vehicle and track the vehicle in case of theft. If the GSM network is not available momentarily, a secured Bluetooth channel will be used instead. The project will be helpful in digitization of documents of Regional Transport Office.
Automation is the process of providing goods and services with fewer to no human interventions. The major advantage of automation is reduction in human error. The system proposes to extract data from images that are tilted at different angles and noisy. The system reduces human error by storing the data directly in the database. The proposed system will take image input from the user through a user interface. This interface is a web application. The input image is preprocessed and forwarded to a machine learning model. The machine learning model is trained and tested using a character data set and convolutional neural network. The model will detect the characters and will give the output as recognized text. This output will be automatically stored in the database and shared with the user through the same interface.
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