Conference Papers

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

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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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    Survey on Game Theory-Based Security Framework for IoT
    (Springer, 2023) Joshi, P.; Kamediya, S.; Kumar, R.; Chandavarkar, B.R.
    The large network of smart devices and the complexity of networks have made it almost impossible to make the data and communication between devices more secure. The number of cyber-attacks on these IoT devices has been steadily increasing. So here a survey on game theory model is presented, and the attackers are rational human beings, so they try to harm the system to their best of ability and so their moves can be predicted which can be used by the defenders with some game theory concepts to take proper security decisions and make an efficient security framework. This chapter is a survey on game theory-based security frameworks and explores and evaluates different types of threats, security requirements and constraints related to IoT, how Game Theory can be used in field of IoT, along with the game theory models used is to develop security frameworks for IoT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Light-Weight Deep Learning Models for Visual Malware Classification
    (Springer Science and Business Media Deutschland GmbH, 2023) Akshay Kumar, E.; Ramalingam, J.
    Malware attacks are on the rise every day in the Internet-based digital world. Regular Internet users are at risk due to the evolution of new infections. In recent years, the use of machine learning algorithms to identify malware has gained popularity because numerous studies have demonstrated its efficacy. This work provides two deep learning models to categorize the malware turned into images. Our method uses fewer resources and takes less time to accomplish the same performance as state-of-the-art results. The primary advantage of malware images is that no additional feature engineering is required. Our models for categorizing image-based malware are less complex and can be used in computational systems with limited computational capabilities, such as Android devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.