Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Rudra, B.Subject: search.filters.subject.Machine learning

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    Machine Learning Techniques for the Investigation of Phishing Websites
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Ajaykumar, K.B.; Rudra, B.
    Phishing is ordinarily acquainted with increase a position in an organization or administrative systems as a zone of a greater assault, similar to an advanced tireless risk (APT) occasion. An association surrendering to such a partner degree assault generally continues serious money related misfortunes furthermore to declining piece of the pie, notoriety, and customer trust. Depending on scope, a phishing attempt may step up into a security episode from that a business can have an inconvenient time recuperating. So as to locate this kind of assault, we endeavored to make a machine learning model that advises the client that it is suspicious or genuine. Phishing sites contain various indications among their substance also, web program-based information. The motivation behind this investigation is to perform different AI-based order for 30 features incorporating Phishing Websites Data in the UC Irvine AI Repository database. For results appraisal, random forest (RF) was contrasted and elective machine learning ways like linear regression (LR), support vector machine (SVM), Naive Bayes (NB), gradient boosting classifier (GBM), artificial neural network (ANN) and recognized to have the most noteworthy exactness of 97.39. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Intrusion Detection Techniques for Detection of Cyber Attacks
    (Springer Science and Business Media Deutschland GmbH, 2021) Ahmed, S.S.; Kankar, M.; Rudra, B.
    Intrusion detection system (IDS) is a software-related application where we can detect the system or network activities and notice if any suspicious task happens. Excellent broadening and the use of the Internet lift examine the communication and save the digital information securely. Nowadays, attackers use variety of attacks for fetching private data. Most of the IDS techniques, algorithms, and methods assist to find those various attacks. The central aim of the project is to come up with an overall study about the intrusion detection mechanism, various types of attacks, various tools and techniques, and challenges. We used various machine learning algorithms and found performance metrics like accuracy, recall, and F-measure and compared with the existing work. After this research, we got good results that can help to detect the cyber attacks being performed in the network. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Prevention of webshell attack using machine learning techniques
    (Grenze Scientific Society, 2021) Satish, Y.C.; Naik, P.M.; Rudra, B.
    Webshell is a web vulnerability and a security threat to any user or a server that can be accessed by attackers to control our system. And also, they may use our system as a command control device to attack other systems. It is difficult to monitor and identify such threats because attackers always tried to attack in different methods and new technologies. However, we can detect the webshell with Machine Learning Techniques with better accuracy; all we need is more number of samples. With this project, we presented a PHP based webshell detecting model. We used different ML algorithms: Logistic Regression(LR), Random Forest(RF), Support Vector Machine(SVM) and K-Nearest Neighbour(KNN). Addition to this PHP file's standard statistical features, we also added an opcode sequence from the PHP files, consisting of the TF-IDF Vector and the Hash Vector. Depending upon these features, we trained with different machine learning models(SVM, RF, LR, KNN). In these models, we got better results with Random Forest having an accuracy of 96.45\% with a false-positive rate of 3.5\%, which is good results compared to several popular detection techniques. © Grenze Scientific Society, 2021.
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    Prediction of Credibility of Football Player Rating Using Data Analytics
    (Springer Science and Business Media Deutschland GmbH, 2022) Datta, M.; Rudra, B.
    FIFA is the world’s most popular association football regulating body. Football is one of the most popular sports in the world owing credit to primarily FIFA itself. In this regard, understanding the credibility of the player’s rating plays act as a major factor for the performance evaluation criteria. This paper mainly seeks to predict the credibility of the professional football player’s rating analytically by making use of various skills and traits of the football players. The effectiveness of using machine learning models namely Support vector machine, Random Forest, Decision Tree, K nearest neighbour and XGBoost for evaluating performance is used for further analysis. We performed the testing using both external testing and cross validation. The best result is obtained by decision tree and support vector machine for both 10 fold cross validation and external testing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.