Online ISSN: 2515-8260

Keywords : Web Application

Applied Machine Learning Predictive Analytics to SQL Injection Attack Detection and Prevention

*T.P. Latchoumi; Manoj Sahit Reddy; K. Balamurugan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3543-3553

These days the world is very much dependent on web applications. Hence providing security to
these applications is of great importance. Information is maintained in the backend databases in
the majority of applications. Among the vulnerabilities is the Structured Query Language
Injection Attack (SQLIA). There are several applications to retrieve session/HTTP cookies
nowadays. There is quite a range of techniques used to stop these attacks.The proposed work
discusses the flaws in a few of these techniques that handle these attacks and implement an
efficient hashing technique to prevent this technique. To overcome the above-mentioned attacks,
the machine learning concept with the Support Vector Machine (SVM) algorithm was
introduced. It is used to detect and prevent SQL injection. In this technique, the SVM algorithm
will be trained with all possible malicious expressions and then generate the model. Whenever a
user gives any new query then SVM will be applied to that model to predict whether a given
query contains any malicious expressions or not. If the user invents the new technique then also
SVM can detect that malicious expression by matching with a minimum number of syntax.

Autonomous Monitoring Unit for Power Loom

C.J. Vignesh; S. Nevash; V.N. Soorya; V. Sakthi Anandakumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5133-5140

Automation plays a major role in today’s world. Power loom is the most important industry for the economic development. To modernize the existing technology with power loom machines, an autonomous monitoring system is newly developed. The main goal of the project is to monitor the power loom unit in an autonomous manner. So, the monitoring of the production in the power loom industry is done automatically. By developing the web and mobile application for power loom to store the database and to analyze the production through bar graph option in it. The database of production in power loom is stored in cloud and separate user id is given for customers to maintain the database in the mobile and web application. The analysis can also be done by using the bar graph option present in it to get the idea about the previous productions. The web application which also has machine parameters such as voltage and current which is sensed from the motor, power and efficiency of a particular machine. By using this web application, we can improve the total production and we can reduce the power consumption of the loom.

Remote Based Intelligent Agriculture Monitoring System

P. Senthil Kumar; N. Kumaresh; M. Karthik Raj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5236-5245

The most important challenges in the field of agriculture are to improve the productivity of the farm and to maintain the crop filed. The key objective of the proposed system is to control the cropland functionalities such as the cultivation period, pump functionalities, future plantation suggestions using the Internet of Things. The proposed IoT-based agriculture monitoring scheme uses the concept of sensor networks. Smart agriculture is a new strategy which is getting popular amidst the agriculturists. A farm which has deployed sensor networks can have better control over the crops, help to collect useful data, and automate various farming routines. The mode of communication involves mail alerts over the suggestion and cultivation period. The humidity, temperature, pH value of the soil is continuously acquired as data from the sensors deployed in the field. This helps to reduce the expenditure and human intervention which ensures maximum yield to the farmers.