Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

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Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms

International Journal of Computer Applications ◽

10.5120/ijca2016910764 ◽

2016 ◽

Vol 150 (12) ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Koushal Kumar ◽

Jaspreet Singh

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intrusion Detection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Feature Selection Techniques

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Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124595 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4595

Author(s):

Malka N. Halgamuge

Keyword(s):

Machine Learning ◽

Experimental Data ◽

Feature Selection ◽

Exposure Time ◽

New Technologies ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Animal Cells ◽

Feature Selection Techniques

The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990–2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier’s prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 − specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.

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Benchmark of feature selection techniques with machine learning algorithms for cancer datasets

Proceedings of the International Conference on Artificial Intelligence and Robotics and the International Conference on Automation, Control and Robotics Engineering - ICAIR-CACRE '16 ◽

10.1145/2952744.2952753 ◽

2016 ◽

Cited By ~ 1

Author(s):

Munirah Mohd Yusof ◽

Rozlini Mohamed ◽

Noorhaniza Wahid

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Feature Selection Techniques

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Detecting Network Anomalies using Multilayer Feature Selection Techniques and Machine Learning Algorithms

10.1109/gcat52182.2021.9587542 ◽

2021 ◽

Author(s):

Vikrant Singh ◽

Shavik Balyan ◽

Mayank Mathur

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Feature Selection Techniques ◽

Network Anomalies

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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

IEEE Access ◽

10.1109/access.2021.3053759 ◽

2021 ◽

Vol 9 ◽

pp. 19304-19326

Author(s):

Pronab Ghosh ◽

Sami Azam ◽

Mirjam Jonkman ◽

Asif Karim ◽

F. M. Javed Mehedi Shamrat ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Feature Selection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Efficient Prediction ◽

Feature Selection Techniques

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Retail Site Selection using Machine Learning Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7186.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 2422-2431

Keyword(s):

Machine Learning ◽

Decision Making ◽

Feature Selection ◽

Site Selection ◽

Random Search ◽

Machine Learning Algorithms ◽

Retail Business ◽

Sales Prediction ◽

Retail Location ◽

Feature Selection Techniques

Selecting a new site for retail business expansion has always been a challenge for decision-makers. It requires not only the sales data but the geographic data in order to decide the potential location for their respective purposes. Proper use of the data could lead to better decision-making. To date, common techniques such as geographic information system (GIS) and multi-criteria decision making (MCDM) have been applied to site selection. These methods, however, require not only extensive human effort but more importantly, difficult to validate the importance of identified variables. In this work, sales performance is proposed as a function of geospatial features to determine the suitability of a retail location. The main aim of this study was to identify features attributed to optimal site selection which in turn facilitate sales prediction for a telecommunication company in Malaysia. In this research, various feature selection techniques and machine learning models were deployed for sales prediction in order to determine the suitability of the new location. The findings show the top 3 feature selections are prediction step in VSURF, random search, and fuse learner with search strategy; the top 3 families are boosting, random forest and bagging; and the top 3 classifiers are C5.0, rf, and parRF. The crossover combination of the top feature selection-classifier can produce the AUC of more than 0.75. The highest AUC, 0.8354 was obtained through random search-parRF.

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