A hybrid reciprocal model of PCA and K-means with an innovative approach of considering sub-datasets for the improvement of K-means initialization and step-by-step labeling to create clusters with high interpretability

Anaraki, Seyed Alireza Mousavian; Haeri, Abdorrahman; Moslehi, Fateme

doi:10.1007/s10044-021-00977-x

Cited by 13 publications

(9 citation statements)

References 60 publications

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“…PCA and k-means clustering were used in [37] to identify malware cases. The dataset should undergo feature engineering and preparation to identify distinguishing features for different malware instances.…”

Section: Making Clusters Using K-means and Pcamentioning

confidence: 99%

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A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Ahmad,

Wan,

Ahmad

et al. 2024

Mathematics

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The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats.

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Section: Making Clusters Using K-means and Pcamentioning

confidence: 99%

“…., Ck), each characterized by m attributes, into k different clusters. In order to facilitate clustering, Equation (7) reduces the overall linear length [37] among instances of malware X j and their center points µ i .…”

Section: Making Clusters Using K-means and Pcamentioning

confidence: 99%

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Ahmad,

Wan,

Ahmad

et al. 2024

Mathematics

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“…Using this method, the resulting clusters' quality may be evaluated. With a high Silhouette Score, linked malware samples have been effectively categorized, and the resulting clusters are distinctive [47]. The output quality of a clustering method is assessed in malware detection using Silhouette Scoring.…”

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confidence: 99%

Enhancing Smart IoT Malware Detection: A GhostNet-based Hybrid Approach

Almazroi,

Ayub

2023

Systems

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The Internet of Things (IoT) constitutes the foundation of a deeply interconnected society in which objects communicate through the Internet. This innovation, coupled with 5G and artificial intelligence (AI), finds application in diverse sectors like smart cities and advanced manufacturing. With increasing IoT adoption comes heightened vulnerabilities, prompting research into identifying IoT malware. While existing models excel at spotting known malicious code, detecting new and modified malware presents challenges. This paper presents a novel six-step framework. It begins with eight malware attack datasets as input, followed by insights from Exploratory Data Analysis (EDA). Feature engineering includes scaling, One-Hot Encoding, target variable analysis, feature importance using MDI and XGBoost, and clustering with K-Means and PCA. Our GhostNet ensemble, combined with the Gated Recurrent Unit Ensembler (GNGRUE), is trained on these datasets and fine-tuned using the Jaya Algorithm (JA) to identify and categorize malware. The tuned GNGRUE-JA is tested on malware datasets. A comprehensive comparison with existing models encompasses performance, evaluation criteria, time complexity, and statistical analysis. Our proposed model demonstrates superior performance through extensive simulations, outperforming existing methods by around 15% across metrics like AUC, accuracy, recall, and hamming loss, with a 10% reduction in time complexity. These results emphasize the significance of our study’s outcomes, particularly in achieving cost-effective solutions for detecting eight malware strains.

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“…Since more than two measured responses are usually studied to achieve a sustainable machining process, the first step is to apply the principal component analysis (PCA) to facilitate performing the clustering step. PCA is a prevalent technique for dimension reduction algorithms [18,19], and it is performed prior to k-means clustering to alleviate the high-dimensionality issue and accelerate computations [20]. The PCA approach converts the measured responses into two main normalized responses (i.e., PCA 1 and PCA 2).…”

mentioning

confidence: 99%

“…The number of clusters must be specified for this algorithm (see the previous step). This technique has been used in a wide variety of applications in a variety of fields [20,22]. The K-means algorithm takes a data set (X) with N samples and separates it into K distinct clusters, with each cluster being defined by the mean of its constituent samples.…”

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confidence: 99%

A Decision-Making Approach for Sustainable Machining Processes Using Data Clustering and Multi-Objective Optimization

2022

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Achieving sustainable machining processes has become crucial in many industries in order to support sustainable development goals (e.g., good health and well-being, decent work and economic growth, affordable and clean energy). Many attempts have been made to optimize the sustainability aspect during machining processes and to offer optimized cutting conditions. However, there is a vital need to develop a decision-making approach that can be flexible and offer optimal sustainable solutions for different machining scenarios. The current study offers a new decision-making approach for sustainable machining processes using data clustering (i.e., K-means clustering) and multi-objective optimization methods (i.e., grey relational analysis). Utilizing the multi-objective optimization after the clustering phase provides the decision maker with optimal and sustainable cutting conditions for different clusters. The developed approach is validated through a case study that includes five design variables (i.e., feed, speed, nose radius, cooling strategy, and rake angle), three machining outputs (i.e., surface roughness, specific energy, and unit volume machining time), and four different scenarios (i.e., finishing, roughing, balanced, and entropy). Three clusters were generated, and the obtained results were compatible with the physical meaning of each studied scenario. Such an approach can provide the decision maker with sufficient flexibility to select the optimal cutting settings for various scenarios, as well as the freedom to switch between clusters and/or scenarios with minimal effort.

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A hybrid reciprocal model of PCA and K-means with an innovative approach of considering sub-datasets for the improvement of K-means initialization and step-by-step labeling to create clusters with high interpretability

Cited by 13 publications

References 60 publications

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Enhancing Smart IoT Malware Detection: A GhostNet-based Hybrid Approach

A Decision-Making Approach for Sustainable Machining Processes Using Data Clustering and Multi-Objective Optimization

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