Arijeet Chandra Sen's Books

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

By: Dr. Aadam Quraishi,Vikas Raina,Aditya Mahamkali,Arijeet Chandra Sen

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MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

By: Dr. Aadam Quraishi,Vikas Raina,Aditya Mahamkali,Arijeet Chandra Sen

Because the Internet is so widespread in modern life and because of the expansion of technologies that are tied to it, such as smart cities, self-driving cars, health monitoring via wearables, and mobile banking, a growing number of people are becoming reliant on and addicted to the Internet. In spite of the fact that these technologies provide a great deal of improvement to individuals and communities, they are not without their fair share of concerns. By way of illustration, hackers have the ability to steal from or disrupt companies, therefore inflicting damage to people all across the world, if they exploit weaknesses. As a consequence of cyberattacks, businesses can face financial losses as well as damage to their reputation. Consequently, the security of the network has become a significant concern as a result. Organizations place a significant amount of reliance on tried-and-true technologies such as firewalls, encryption, and antivirus software when it comes to securing their network infrastructure. Unfortunately, these solutions are not completely infallible; they are merely a first line of security against malware and other sophisticated threats. Therefore, it is possible that certain persons who have not been sanctioned may still get access, which might result in a breach of security. For the purpose of preventing intrusion detection, computer systems need to be safeguarded against both illegal users, such as hackers, and legitimate users, such as insiders. A breach of a computer system may result in a number of undesirable results, including the loss of data, restricted access to internet services, the loss of sensitive data, and the exploitation of private resources. an initial version of the Intrusion Detection System (IDS) was constructed. In light of the fact that it is a that is essential for the protection of computer networks, it has therefore become a subject of study that is widely pursued. Given the current condition of cybercrime, it is impossible to deny the significance of the intrusion detection system (IDS). A possible example of how the IDS taxonomy is arranged may be found here. The intrusion detection system, often known as an IDS, is a piece of software or hardware that monitors a computer or network environment, searches for indications of intrusion, and then notifies the user of any potential threats. Utilizing this warning report is something that the administrator or user may do in order to repair the vulnerability that exists inside the system or network. In the aftermath of an intrusion, it may be purposeful or unlawful to attempt to access the data

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Page Count

199

Publisher

Xoffencerpublication

Published Date

ISBN 10

8119534905

ISBN 13

9788119534906

SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

By: Dr. Sami Ahmed Haider,Dr. Gargishankar Verma,Arijeet Chandra,Dr. Haewon Byeon

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SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

By: Dr. Sami Ahmed Haider,Dr. Gargishankar Verma,Arijeet Chandra,Dr. Haewon Byeon

The subset of machine learning algorithms known as supervised learning is an essential component that makes a substantial contribution to the resolution of a wide variety of problems that are associated with the study of artificial intelligence (AI). A dataset that has been labeled is given to the algorithm during the supervised learning phase. This dataset contains not only the input data but also the target labels that correlate to those data. Both sets of information are included. The objective of this activity is to construct a model or a mapping that is able to reliably predict the labels for data that has not yet been observed. There are a large number of algorithms that are commonly used for supervised learning, and each of these techniques has a number of benefits as well as some drawbacks. The technique known as linear regression, which is applied in situations involving continuous numerical data, is one method that is frequently used. Creating a linear link between the input features and the variable that you want to change is the method that is used to accomplish this goal. Logistic regression is often utilized when the objective is to categorize individual data points into a number of separate groups or classes. It constructs a model that calculates the probability that a certain data point belongs to a particular category. Decision trees are a type of general-purpose algorithm that can be put to use for a variety of different classification and regression-related projects. They do this by constructing a tree-like structure, where each leaf node represents a projected class or value and each inside node represents a decision that was taken based on a feature. In other words, each node in the structure represents a decision that was made. The performance of prediction tasks can be improved using ensemble methods such as Random Forests and Gradient Boosting. These methods work by combining many decision trees into a single model. They are especially useful when it comes to managing difficult datasets. Support Vector Machines, often known as SVMs, are useful tools for binary classification because they pinpoint the hyperplane that achieves the optimal margin between classes. Because of this, they are able to deliver satisfactory results whenever there is a noticeable divide between the classes.

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209

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Xoffencerpublication

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ISBN 10

811953476X

ISBN 13

9788119534760

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

By: Dr. Aadam Quraishi,Vikas Raina,Aditya Mahamkali,Arijeet Chandra Sen

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MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

By: Dr. Aadam Quraishi,Vikas Raina,Aditya Mahamkali,Arijeet Chandra Sen

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

199

Publisher

Xoffencerpublication

Published Date

ISBN 10

8119534905

ISBN 13

9788119534906

Book's by Arijeet Chandra Sen

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

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