Mitigating DDoS Attacks in Blockchain Environments Using a Multi-level Security Framework | Chapter 8 | Mathematics and Computer Science: Research Updates Vol. 5

In the current era of rapid digital transformation, blockchain technology has emerged as one of the most disruptive innovations across multiple sectors. Apart from its growing prominence, there remains a significant lack of awareness regarding its practical applications and potential benefits. This paper briefly explores the vast applications of blockchain technology with a particular focus on the challenges encountered within the IT industry. It delves into the technical and operational hurdles that organisations face when attempting to adopt and implement blockchain solutions. By combining strategic planning, architectural best practices and risk mitigation techniques, our framework aims to facilitate more secure, scalable and cost-effective incorporation of blockchain into existing IT ecosystems. The paper proposes a comprehensive framework aimed at addressing these challenges, offering strategic insights for more effective and secure integration of blockchain into existing IT infrastructures.

 

Author (s) Details

Satvik V. Khara
Department of Computer Engineering, Silver Oak University, Ahmedabad, Gujarat, India.

Gaurav D. Tivari
Department of Computer Engineering, Silver Oak University, Ahmedabad, Gujarat, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v5/5579

Most Recent Advances in Stepping-stone Intrusion | Chapter 10 | Contemporary Perspective on Science, Technology and Research Vol. 7

Today network intrusions are usually launched by attackers through compromised hosts, called stepping-stones, in order to minimize the chance of being detected. This book chapter presents the most recent research advances in the area of stepping-stone intrusion detection. All the important and significant methods proposed in recent years for stepping-stone intrusion detection are discussed and summarized in this book chapter. The two key techniques used for stepping-stone intrusion detection in recent years are packet matching and packet crossover. Both the packet matching-based detection methods and the packet crossover-based detection algorithms are presented and discussed. In addition to these two categories, other important detection algorithms for stepping-stone intrusion in recent years are also included and discussed. Finally, some important and challenging open problems are presented in this book chapter.

Author(s) Details:

Lixin Wang,
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA.

Jianhua Yang,
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA.

Please see the link here: https://stm.bookpi.org/CPSTR-V7/article/view/13747

Hybridized Swarm Optimization Classifiers with Ensemble Feature Ranking Techniques: Recent Study | Chapter 8 | Emerging Trends in Engineering Research and Technology Vol. 6

Intrusion Detection System (IDS) is a security support mechanism which has become an essential component of security infrastructure to detect attacks, identify and track the intruders. Intrusion Detection Systems are implemented in order to detect malicious activities and it functions behind the firewall, observing for patterns in network traffic that might indicate malicious action. The extreme development of the internet, the high occurrence of the threats over the internet has been the cause in recognizing the need for both IDS and firewall to help in securing a network. Currently many researchers have shown an increasing interest in intrusion detection based on data mining techniques and swarm intelligence techniques. Also, recent research focuses more on the hybridization of techniques to improve the performance of classifiers and it has become commonplace in IDSs which allows researchers to exploit the benefits of individual techniques and approaches. In intrusion detection, the quantity of data is huge that includes thousands of traffic records with number of various features. Selecting a subset of informative features can lead to improved classification accuracy. In this paper ensemble of feature ranking techniques are used to select the most relevant features that can represent the pattern of the network traffic. The efficiency of the presented method is validated on KDDCUP’99 dataset using hybrid swarm based classifier, Simplified Swarm Optimization (SSO) with Ant Colony Optimization (ACO). The performance of the proposed method is compared with the basic classifiers, SSO and hybridization of SSO with Support Vector Machine (SVM). It is shown that the hybridization of SSO with ACO using hybrid feature ranking method outperformed other algorithms and can be efficient in the detection of intrusive behaviour.

Author(s) Details

P. Amudha
Department of CSE, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

S. Sivakumari
Department of CSE, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.

View Book :-  http://bp.bookpi.org/index.php/bpi/catalog/book/200