Anomaly-Based Intrusion Detection Systems Using Machine Learning

Alsamir Alqahtani; Hanan AlShaher

doi:https://doi.org/10.54216/JCIM.140102

International Journal of Neutrosophic Science | 2024

Generalized p-Transmuted Neutrosophic Distributions: Theory and its Applications

The study of neutrosophy offers a fresh approach for handling uncertain data with adaptability. This article explores the application of neutrosophic probability distribution in constructing a transmuted neutrosophic framework. Specifically, it introduces a generalized transmuted neutrosophic distribution. Building upon this generalization, quadratic and cubic transmuted distributions are developed and examined alongside certain lifetime distributions serving as foundational neutrosophic models. Additionally, an empirical investigation is conducted to assess the practicality and versatility of these distributions in real-world contexts.

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Abdul Kani Jabarali mail -

K. Mohana mail -

David Winster Praveenraj Devanayan mail -

Ajitha Krishnaprasad mail -

D. Sandhya mail -

Pradeep Kumar SV mail

link https://doi.org/10.54216/IJNS.240318

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Extension of arithmetic and geometric aggregating operators using new type interval-valued neutrosophic sets.

The purpose of this article is to present a novel approach to the (δ,ε)  interval-valued neutrosophic set (IVNS). This is an extension of the IVNS. As a result of this article, we will discuss the concept of (δ,ε)   interval valued neutrosophic weighted averaging (IVNWA), (δ,ε)  interval-valued neutrosophic weighted geometric (IVNWG), (δ,ε)  generalized interval-valued neutrosophic weighted averaging (GIVNWA) and (δ,ε)  generalized interval-valued neutrosophic weighted geometric (GIVNWG). Additionally, the (δ,ε) IVNS approach is characterized by idempotency, boundedness, commutativity and monotonicity.

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M. Palanikumar mail -

T. T. Raman mail -

A. Swaminathan mail -

Aiyared Iampan mail

link https://doi.org/10.54216/IJNS.240319

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Local Search Algorithms For Solving A Function With Five-Objectives And Release Dates on One-Machine

In this research, the issue of scheduling n-jobs on one-machine is represented to minimize Five-Objectives-Function (FOF), for finding approximation solutions for the sum of completion time, total tardiness, total earliness, number of late jobs and late work with release date, this issue denoted by:   Hanan and Hussein used a branch and bound technique (B-a-B) to discovery an optimal solution path. Computational results showed the (B-a-B) technique was efficient in solving issues with up to (16- jobs). Because our issue is of a very difficult type (NP-hard), we suggest local search algorithms to discovery near optimal solution.  The execution of local search techniques can be tested on large group of test issues. Computational results showed with up to (30000 jobs) in acceptable time.

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Hussein Abdullah Jaafar mail -

Hanan Ali Chachan mail

link https://doi.org/10.54216/GJMSA.0100204

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

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R. Padmaraj mail -

K. Selvakumar mail

link https://doi.org/10.54216/FPA.160116

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

On the Compactness and Continuity of Uryson's Operator in Orlicz Spaces

Uryson's operators are very famous in the theory of fuzzy functional analysis. This paper is dedicated to studying and generalizing many results about the compactness and the continuity of Uryson's operator in two-variables defined with integral equation on G with the norm ‖u‖f= sup(ρ(v,f)≤) |∫u(x)v(x)dxG |   ;v(x)∈L*g   ,u(x)∈Lf*. Also, we study the convergence of Urysons' sequences Kn defined with the family of functions Kn (x, y; u) by using the convergence with respect to the defined measure and Caratheodory Condition.

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Raed Hatamleh mail

link https://doi.org/10.54216/IJNS.240320

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2024

Cosine trigonometric rules applied to average and geometric aggregating operators using extension q-rung interval-valued neutrosophic set approach.

We introduce the concept of cosine trigonometric q-rung Diophantine neutrosophic interval-valued set (CosTq-rung DioNSIVS). The fact that CosTq-rung DioNSIVS combines q-rung neutrosophic interval-valued set, q-rung neutrosophic set and neutrosophic interval-valued set is one of its distinguishing characteristics. A new idea of CosTq-rung DioNSIVWA, CosTq-rung DioNSIVWG, GCosTq-rung DioNSIVWA and GCosTq-rung DioNSIVWG is proposed in this study. We also look at the idempotency, boundedness, commutativity, and monotonicity of the CosTq-rung DioNSIVS based on algebraic operations. We considered new kinds of two distances in the proposed models, besides Euclidean and Hamming distances. The CosTq-rung DioNSIVS method was used to analyze the cosine trigonometric aggregation procedures. The study's concluding results include several fascinating and captivating discoveries.

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M. Palanikumar mail -

K. Arulmozhi mail -

Aiyared Iampan mail

link https://doi.org/10.54216/IJNS.240321

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Effective Integration of Database Security Tools into SDLC Phases: A Structured Framework

As organizations increasingly rely on digital data, securing database systems has become a critical priority for protecting sensitive information, ensuring system integrity, and meeting regulatory compliance standards. This paper explores a comprehensive framework for database security, focusing on developing, assessing, and testing effective security tools. We begin by outlining the essential steps in creating robust security tools, including defining specific requirements based on database types and access needs and implementing real-time monitoring systems for immediate threat detection. The paper also emphasizes the importance of regular vulnerability assessments and advanced security analytics to identify and address potential risks proactively. Insights from a recent survey conducted among database administrators revealed that key areas of concern include access control, real-time monitoring, and vulnerability assessments. Furthermore, we highlight the significance of integrating security practices throughout the Software Development Life Cycle (SDLC). Additionally, best practices for evaluating and testing database security, including penetration testing to uncover vulnerabilities and stress testing to assess performance under load, are discussed. By synthesizing these strategies and survey feedback, this paper provides a comprehensive approach to enhancing database security, ensuring data protection, and maintaining system resilience against evolving cyber threats

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Ahmed Naguib mail -

Haba K. Aslan mail -

Khaled M. Fouad mail

link https://doi.org/10.54216/JCIM.160114

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2025

Review: AI-Driven Advances in Physiotherapy Stimulation Devices

This embraces rehabilitation medicine as it significantly boosts the doctor's way of working and presents new ways or new tools that the doctor might consider to enhance or augment the results that the patients benefit from physically. This review focuses on applying AI technologies such as robotic systems, virtual reality (VR), machine learning algorithms, wearable devices and predictive analytics in different fields, including stroke recovery, neuromuscular disorder rehabilitation, orthopedic and critical care. AI utilization improves patient treatment, the accuracy of therapy, and the administration of evaluation to deal with issues such as a lack of therapists, comparative analysis, and the expensive nature of conventional treatment. Although the outlook for its progress is positive, there are twofold problems: ethical questions, data privacy and policy concerns, and regulatory challenges. Future directions indicate directions for research and practice and call for increased interdisciplinary cooperation, large-scale validation studies and appropriate ethical standards to unlock the full potential of AI in reinventing rehabilitation medicine and rendering patient-centered care possible.

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Mohamed Saber mail

link https://doi.org/10.54216/MOR.030103

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2024

The impact of AI-based cyber security on the banking and financial sectors

BD and AI are now transforming the banking and finance industry at a very fast pace, which is leading to change in the banking and finance institutions. This change is making them better, customer-oriented and financially rewarding organizations. Big data and AI have been useful in the banking and financial institutions to assess and manage the risks. Through the analysis of big amounts of unstructured data in real time, AI algorithms are capable of identifying risks. This makes it easy to put preventive measures in place to avert the risks. In addition, big data and AI have come a long way in solving the problem of fraud in banking and finance. This paper showed how big data and AI improve risk management, Cyber threat, and fraud in banking and finance by using data analysis and data pattern identification in real-time. That is why our work emphasizes the importance of implementing secure privacy and explaining the AI algorithm to eliminate ethical and Cyber security issues. Using analytical approaches, AI can identify the transactions with the help of comparison with the previous data and the behavioral characteristics related to the fraud. This approach to fraud prevention has been effective in reducing losses while at the same time improving the customer’s confidence in the company. On the other hand, there are disadvantages of big data and AI such as privacy, security, and ethical issues. Measures that can be used to safeguard customer information have to be employed in order to effectively safeguard the consumer data. Furthermore, transparency and accountability of the AI algorithms are crucial in order to avoid unfair decisions.

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Haya saleh alrafi mail -

Shailendra Mishra mail

link https://doi.org/10.54216/JCIM.140101

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Anomaly-Based Intrusion Detection Systems Using Machine Learning

With the increased use of the Internet, unauthorized access has increased, allowing malicious users to hack networks and carry out malicious activities. One of the essential modern approaches in today's cybersecurity efforts is the limitation of access by suspect users. In this study, the approach toward real-time intrusion detection was to consider behavioral patterns of past users on the network. We classified the users as two categories: intervention and non-intervention, and employed the machine learning techniques Artificial Neural Networks [ANN], Support Vector Machines [SVM], and Decision Trees [DT]. The Decision Trees model was chosen as it had a mature capability concerning complex pattern recognition and an enhancement capability of the intrusion detection systems. The efficiency of these algorithms is examined via the key performance metrics: confusion matrix, F1-score, and Area Under the Curve [AUC]. Decision Tree, which came up as the best model for both the training and testing phases, produced an outstanding F1-score of 99.96% and AUC score of 99.93% in the testing phase. SVM and ANN gave good results; the F1 scores of SVM and ANN in the testing phase were 92.76% and 93.33%, while the AUC was 90.57% and 94.78%, respectively. This research will enlighten us on the influence of machine learning on the scope of intrusion detection, fostering more development efforts toward more responsive and dynamic intrusion detection systems. The comparative evaluation of these models will help in providing vital information for the further enhancement of cybersecurity strategies, ensuring better defenses against these ever-evolving cyber threats.

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Alsamir Alqahtani mail -

Hanan AlShaher mail

link https://doi.org/10.54216/JCIM.140102

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new