Artificial Intelligence and Data Mining Approaches in Security Frameworks. Группа авторов
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2 Whenever it does so a machine learning model based on its ability to recognize patterns from the past would detect the presence of bots through active monitoring and predictive analysis.
3 If detected, it would terminate the current process and send out an alert.
4 If the bot is not present then it would continue the process and run the anti-virus software, in order to remove any other malicious files.
5 The Disaster recovery plan in the end would ensure that any important data is not lost and is backed up.
1.5.1 System Architecture
Figure 1.2 System architecture [11].
1.5.2 Future Scope
While we are embracing new ways of digital interaction and more of our critical infrastructure is going digital, the parameters of the transformation underway are not understood by most of us. A better understanding of the global cyberspace architecture is required.
1.6 Conclusion
AI finds its applications in almost every field of science and engineering. AI models need precise safeguards in digital security and new technologies to battle antagonistic machine learning, retain confidentiality, and secure organized learning, and so on. In this chapter, the authors examined specific approaches in AI that are promising and proposed a system of preventing certain types of cyber-security attacks.
References
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1 *Corresponding author: [email protected]
2
Privacy Preserving Using Data Mining
Chitra Jalota* and Dr. Rashmi Agrawal
Manav Rachna International Institute of Research and Studies, Faridabad, India
Abstract
On the one hand, data mining techniques are useful to extract hidden knowledge from a large pool of data but on the other hand a number of privacy threats can be introduced by these techniques. The main aim of this chapter is to discuss a few of these issues along with a comprehensive discussion on various data mining techniques and their applications for providing security. An effective classification technique is helpful to categorize the users as normal users or criminals on the basis of the actions which they perform on social networks. It guides users to distinguish among a normal website and a phishing website. It is the task of a classification technique to always alert users from implementing malicious codes by labelling them as malicious. Intrusion detection is the most important application of data mining by applying different data mining techniques to detect it effectively and report the same in actual time so that essential and required arrangements can be made to stop the efforts made by the trespasser.
Keywords: Data mining, security, intrusion detection, anamoly detection, outlier detection, classification, privacy preserving data mining
2.1 Introduction
A computer system has the ability to protect its valuable information, raw data along with its resources in terms of privacy, veracity and authenticity; this ability is known as computer security. A third party cannot read or edit the contents of a database by using the parameters i.e., Privacy/confidentiality and integrity. By using the parameter authenticity, an unauthorised person is not allowed to modify, use or view the contents of a database. When one or more resources of a computer compromises the availability, integrity or confidentiality by an action, it is known as intrusion. These types of attacks can be prevented by using firewall and filtering router policies. Intrusions can happen even in the most secure systems and therefore it is advisable to detect the same in the beginning. By employing data mining techniques, patterns of features of a system can be detected by an intrusion detection system (IDS) so that anomalies can be detected with the help of an appropriate set of classifiers. For easy detection of intrusion, some important data mining techniques such as classification and clustering are helpful.
Test data could be analysed and labelled into known type of classes with the help of classification techniques. For objects grouping into a set of clusters, clustering methods are used. These methods are used in such a way that a cluster has all similar objects. There could be some security challenges for mining of underlying knowledge from large volumes of data as well as extraction of hidden patterns by using data mining techniques (Ardenas et al., 2014). To solve this issue, Privacy Preserving Data Mining (PPDM) is used, which aims to derive important and useful information from an unwanted or informal database