An Empirical Investigation on the Origins and Effects of Cybersecurity Culture in It Organizations

Balamuralikrishna; Ravi Kiran; D. Lokesh Sai; Tenali; P.; S.

doi:https://doi.org/10.54216/JCIM.160106

Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Variational Autoencoder with Hippopotamus Optimizer-Based Dimensionality Reduction Model for Attention Deficit Hyperactivity Disorder Diagnosis Data

Adverse Drug Reactions (ADRs) are very hazardous to patients. Thus, the detection of ADR intends to automatically distinguish, which is an intensive study for public health monitoring functions.  Detecting ADRs is the most significant information to determine the patient’s opinion on some drugs. As patients can experience projected and occasionally unpredicted negative results from taking some drugs, late detection of ADRs may place life-threatening dangers to patients; posing significant financial, social, and legal consequences to the regulatory agencies and manufacturing companies. The usage of medical data, like states and electronic health records (EHR), became normal in offering a richer understanding of health services and assisting ADR analysis. Developments in deep learning (DL) and machine learning (ML) have made several analytic models have the potential to apply higher-dimensional data to predict adverse effects. In this study, we present a Hippopotamus Optimizer-Based Feature Selection for Adverse Drug Reaction Detection Using a Variational Autoencoder (HOFS-ADRDVAE) model. The main intention of the HOFS-ADRDVAE model is to provide an automatic system for the detection of ADR using state-of-the-art techniques. Initially, the data normalization stage employs min-max normalization for converting input data into a beneficial format. In addition, the feature selection process has been executed by the hippopotamus optimization (HO) algorithm. Besides, the proposed HOFS-ADRDVAE model designs a variational autoencoder (VAE) technique for the classification procedure. At last, the Hunger Games search (HGS) algorithm-based hyperparameter selection process is executed to optimize the classification results of the VAE system. A wide-ranging experiment was implemented to point out the performance of the HOFS-ADRDVAE method. The experimental outcomes specified that the HOFS-ADRDVAE model emphasized improvement over another existing method.

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N. Deepaletchumi mail -

R. Mala mail

link https://doi.org/10.54216/JISIoT.160107

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Enhanced Feature Selection Approach using Artificial Hummingbirds with Genetic Algorithm

Feature selection (FS) is a crucial preprocessing step in data mining to eliminate redundant or irrelevant features from high-dimensional data. Many optimization algorithms for FS often lack balance in their search processes. This paper proposes a hybrid algorithm, the Artificial Hummingbird Algorithm based on the Genetic Algorithm (AHA-GA), to address this imbalance and solve the FS problem. The main goal of AHA-GA is to select the most crucial characteristics to improve overall model categorization. The UCI datasets are used to assess the performance of the proposed FS method. The proposed feature selection algorithm was compared with five feature selection optimization algorithms: BWOAHHO, HSGW, WOA-CM, BDA-SA, and ASGW. AHA-GA achieved a classification accuracy of 96% across 18 datasets, which was higher than BWOAHHO (93.2%), HSGW (92.5%), WOA-CM (94.4%), BDA-SA (93%), and ASGW (91.6%). When comparing the proposed AHA-GA algorithm to the results obtained by the other five algorithms in terms of selected attribute size, the average feature sizes were as follows: AHA-GA (15.10889), BWOAHHO (16.74222), HSGW (19.43111), WOA-CM (17.05389), BDA-SA (17.275), and ASGW (19.7585). The statistical and experimental tests demonstrated that the proposed AHA-GA performs better than competitive algorithms in selecting effective features.

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Ismael Salih Aref mail -

Dheyab Salman Ibrahim mail -

Bashar Talib Al-Nuaimi mail

link https://doi.org/10.54216/JISIoT.160108

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Multi-Dimensional Trust based Data Dissemination mechanism (MDTD) for Ensuring Authentication by Eliminating Blackhole Attack in VANET

Vehicular Ad Hoc Networks also known as VANET, and it is a special type of ad hoc networks since it is deployed on demand.  Here the nodes are representing as vehicles, and they are communicating with each other to ensure the reliable and secure safety driving. Since it is open environment, ensuring secure routing is always a challenging task. Routing is one of the essential things in ad hoc networks because it is carrying road safety information always. However, most of the time, it is affected by attacks. Black hole is one of the attacks where the malicious nodes that is black hole vehicles advertise itself that having the shortest path to the destination by the way it tries to disturb the entire environment. In this paper, multi-dimensional trust-based data dissemination mechanism is proposed. The main objective is to ensure authentication by eliminating black hole attack. The proposed method makes use of multiple trusts such as direct, indirect, integrity, intimacy, and mobility over Dynamic Source Routing (DSR) protocol by the way authentication can be achieved. Simulation results shows that the proposed model works efficiently compare with existing models.

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C. Balakumar mail -

S. Vydehi mail

link https://doi.org/10.54216/JISIoT.160109

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Robust Zero-Day Attack Detection with Optimal Deep Learning for Securing Internet of Things Environment

The Internet of Things (IoT) aims to provide connectivity between all computing entities. However, this facilitates cyberthreats, which exploits the existence of vulnerability over a period. The zero-day threat is one of the vulnerabilities that can result in zero-day attacks that are destructive to the network security and an enterprise. This attack may have potentially compromised critical infrastructure, far-reaching consequences, national security, and even personal privacy. To alleviate the risks, organizations and manufacturers should prioritize proactive security measures, involving robust authentication mechanisms, ongoing monitoring, and timely software updates, to defend the IoT ecosystem from emerging threats. In present scenario, deep learning (DL)-based models have improved robustness in learning data giving it an improved capability to identify unknown information, since it can able to extract knowledge of non-linear data to identify unknown information. The study presents a Robust Zero-Day Attack Detection with Optimal Deep Learning (RZDAD-ODL) technique for the IoT framework. The primary intention of the RZDAD-ODL model lies in the automatic and effectual detection of zero-day attacks in the IoT framework. In the presented RZDAD-ODL technique, the honey badger algorithm (HBA) can be used for the optimum range of the features. Besides, the RZDAD-ODL technique exploits the conditional variational autoencoder (CVAE) model for attack detection and its parameter tuning process can be performed by using a rider optimization algorithm (ROA). The experimentation results of the RZDAD-ODL system can be validated on a benchmark dataset. Extensive comparison studies reported the better attack detection performance of the RZDAD-ODL model over other current techniques.

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Nahla J. Abid mail -

Nawaf Alhebaishi mail -

Turki Althaqafi mail

link https://doi.org/10.54216/JISIoT.160110

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

AI-Driven Features for Intrusion Detection and Prevention Using Random Forest

In this research, we investigate sophisticated methods for Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), leveraging AI-based feature optimization and diverse machine learning strategies to bolster network intrusion detection and prevention. The study primarily utilizes the NSL-KDD dataset, an enhanced version of the KDD Cup 1999 dataset, chosen for its realistic portrayal of various attack types and for addressing the shortcomings of the original dataset. The methodology includes AI-based feature optimization using Particle Swarm Optimization and Genetic Algorithm, focusing on maximizing information gain and entropy. This is integrated with the use of Random Forest (RF) to reduce class overlapping, further enhanced by boosting techniques. Grey Wolves Optimization (GWO) alongside Random Forest. This innovative approach, inspired by grey wolf hunting strategies, is employed for classification tasks on the NSL-KDD dataset. The performance metrics for each intrusion class are meticulously evaluated, revealing that the GWO-RF combination achieves an accuracy of 0.94, precision of 0.95, recall of 0.93, and an F1 score of 0.94.

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Mohammed B. Al-Doori mail -

Khattab M. Ali Alheeti mail

link https://doi.org/10.54216/JCIM.160101

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Critical Feature Selection Technique for Improving Performance Classification Model in Adaptive Intrusion Detection System

A firewall is one of the devices that supports network security, especially at the organizational level. A Firewall's effectiveness in supporting network security is highly dependent on the capabilities and abilities of the Network Administrator. Unfortunately, the high complexity of creating rules and the process of configuring Firewall rules carried out statically by the Network Administrator weakens the effectiveness of the Firewall, and it cannot adapt to increasingly dynamic network pattern changes. Machine Learning is one of the potentials that can be used so that the Firewall can work adaptively. Adaptive Firewall configuration in recognizing various attacks in the network will undoubtedly increase the effectiveness of the Firewall in ensuring network security. The success of the machine learning model performance cannot be separated from the dataset used during the learning process. The dataset used in learning often has a large dimension, but various noises and attributes are irrelevant in representing one class of data. Therefore, it is necessary to support the feature selection technique, which will show the presence of relevant characteristics in the dataset and maximize the machine learning model's performance. This study will be conducted on adding feature selection techniques to develop machine learning models on the Benchmark dataset related to network security. Various popular feature selection techniques will be evaluated, and their performance will be compared based on scenarios between feature selection techniques or scenarios that only use a single classification.

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Anggit Ferdita Nugraha mail -

Yoga Pristyanto mail -

Beti Wulansari mail -

Dian Prasetya mail

link https://doi.org/10.54216/JCIM.160102

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Efficient Algorithms for Fuzzy Centrality Measures in Large-Scale Social Networks

Numerous criteria are in place for social network applications. They require identification of network's core nodes. Traditional centrality measurements focus on specific node's direct connections or reachability. Often this disregards inherent ambiguity and complexity in real-world social networks. To address these constraints, we have introduced new method called Node Pack Fuzzy Information Centrality based on Pythagorean Neutrosophic Fuzzy Theory. Three essential values truth, falsity and indeterminacy have been added to this approach. This new approach provides a thorough depiction of social networks and it also offers a more sophisticated comprehension of connections between nodes. Complex and ambiguous interactions between entities can be effectively expressed using Pythagorean Neutrosophic values. Unlike traditional values, Pythagorean Neutrosophic values consider several uncertainty dimensions; this is a major improvement over traditional fuzzy value. Our approach handles relational complexity well and it includes self-weight for every node too. It represents each node's unique value, significance, or impact on the network. The network assessment is now more precise and contextual so we can assess centrality with greater precision. We applied this approach to a small academic network called university faculty/researchers. The application of Node Pack Fuzzy Information Centrality yielded promising results. It can enhance various activities associated with social network analysis. It can also offer valuable insights into the network architecture.

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Songa Venkata Rao mail -

Bodapati Prajna mail

link https://doi.org/10.54216/JCIM.160103

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

A Constraint Satisfaction Approach for Estimating the RSA Prime Factors towards Known Bits Factorization Attacks

The Rivest–Shamir–Adleman (RSA) cryptosystem is one of the most prevalently utilized public-key cryptographic systems in current practice. Prior investigations into vulnerabilities of this cryptosystem have concentrated on diminishing the complexity associated with the integer factorization challenge, which is integral to the RSA modulus, expressed as 𝑁=𝑝𝑞. Possessing partial knowledge about the least significant digits (LSDs) of both p and q is a common assumption attacker’s advantage to enable the polynomial-time factorization of N, ultimately undermining the security of RSA. This paper presents a novel heuristic algorithm predicated on the Constraint Satisfaction Problem (CSP) principles, which estimates k-LSD pairs of the RSA prime factors,  and . The proposed Generate and Test (GT) and Backtracking with Heuristic Variable Ordering (BHVO) solver guarantees polynomial-time factorization of known bits by iteratively refining candidate pairs and eliminating invalid combinations through effective constraint propagation. The proposed approach obviates the requirement for specialized hardware for side-channel attacks to reveal a portion of  and . In our results, we have successfully estimated up to 5-LSDs of  and  with a reduced number of iterations and factored 2048 bits, N based on the known 4-LSDs of the prime in polynomial time. Our research lays the groundwork for factorization algorithms that require partial knowledge of the prime factors. We have highlighted the possible vulnerabilities linked to existing RSA key generation techniques. These may make RSA moduli susceptible to the attacks discussed in this study and proposed countermeasures to ensure secure prime generation.

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Daniel Asiedu mail -

Patrick Kwabena Mensah mail -

Peter Appiahene mail -

Peter Nimbe mail

link https://doi.org/10.54216/JCIM.160104

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Implementing Comparative Analysis on Feature Engineering Techniques and Multi-Model Evaluation Framework for IDS

In recent years, most of the current intrusion detection methods run for critical information infrastructure are tested for IDS datasets, but does not provide desired protection against emerging cyber- threats. Most machine and deep learning-based intrusion detection methods are inefficient on networks due to their high imbalanced or noisy IDS datasets. Therefore, in this paper, our proposed work implements a comprehensive framework, using multiple models of machine learning and deep learning by taking advantage of advanced feature engineering approaches. Our research explores the impacts of a variety of feature engineering approaches on dimensionality reduction methods used to train and test model performance with execution time taken on the CICIDS2017 dataset to reduce the time complexity and enhance performance to detect intrusion by experiment and leveraging feature engineering techniques like PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), t_SNE (t-Distributed Stochastic Neighbor Embedding), and Autoencoders. This framework also resolves the class imbalance issues by using SMOTE (Synthetic Minority Oversampling Technique), generates synthetic samples of those classes, which have a very low number of samples to balance the class for a better model performance. Our comparative analysis is performed on metrics like accuracy, training time and memory usage for machine learning models like Gradient Boosting, Logistic Regression, XGBoost and deep learning models. DL with LDA feature engineering approach achieved the highest test accuracy of 95.99% and Gradient Boosting shows strong performance by attaining a high-test accuracy of 90.8%. Illustrated DL model had higher memory usage, but LR and XG- Boost models performed computationally efficient. Further, it is observed that LDA performed better with ML and DL models in comparison to other feature engineering techniques to enhance the intrusion detection efficiency.

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Neha Sharma mail -

Abhishek Kajal mail

link https://doi.org/10.54216/JCIM.160105

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

An Empirical Investigation on the Origins and Effects of Cybersecurity Culture in It Organizations

This observe investigates the reasons and effects of cybersecurity way of life in IT agencies. Given the developing threats to cybersecurity and the essential role that organizational lifestyle plays in decreasing these risks, it's miles essential to realise the connection that exists among policy elements, employee conduct, and cyber security overall performance. By concentrating at the connections between distinct factors impacting cybersecurity culture and there have an effect on the efficacy of cyber security measures, the examine fills in gaps in empirical studies. This take a look act’s principal purpose is to behaviour an empirical investigation into the methods that many sides of cyber security culture, along with policy concerns, employee behaviour, and cyber security attention, have an effect on how properly cyber security measures work in IT companies. The studies in particular examines 3 hypotheses: (1) that coverage factors positively correlate with usual effectiveness; (2) that cyber security attention and engagement in preventive measures are predictively correlated; and (three) that behavioural worries are undoubtedly correlated with the implementation of powerful cyber security measures. Data had been collected the usage of a pass-sectional survey the use of a quantitative studies method. A stratified random pattern strategy became used inside the studies to select 100 IT employees from special corporations. A systematic questionnaire overlaying coverage variables, behavioural worries, cyber security recognition, preventative measures, and the perceived efficacy of cyber security strategies become used to collect information. The conclusions of the primary records had been in addition supported and given that means with the aid of secondary information taken from organizational reviews and already published literature. An enormous wonderful connection was discovered in the research between coverage variables and cyber security measures' efficacy, suggesting that robust regulations enhance cyber security overall performance as a whole. It has been proven that employee participation in preventative actions is extensively anticipated by cyber security recognition. The adoption of successful cyber security tactics turned into strongly correlated with behavioural issues. Aside from declaring regions where cyber security lifestyle needs to be stepped forward, the research additionally found gaps in preventative measures' efficacy. The study emphasizes how crucial it is to have clear policy guidelines and raise awareness of cyber security issues in order to encourage efficient cyber security practices in IT companies. The results provide insightful information on the dynamics of cyber security culture and offer doable recommendations for improving cyber security procedures and guidelines. Organizations may enhance their cyber security frameworks and strengthen their Defences against emerging threats by filling up the holes found in the report.

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Balamuralikrishna Thati mail -

Ravi Kiran Koppolu mail -

D. Lokesh Sai Kumar mail -

Tenali Nagamani mail -

P. Muthukumar mail -

S. Lalitha mail

link https://doi.org/10.54216/JCIM.160106

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new