scholarly journals Feature Selection Methods Simultaneously Improve the Detection Accuracy and Model Building Time of Machine Learning Classifiers

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1424 ◽  
Author(s):  
Saleh Alabdulwahab ◽  
BongKyo Moon
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Model Building ◽  
Detection System ◽  
Real Life ◽  
Training Dataset ◽  
Filter Method ◽  
Detection Accuracy ◽  
Selection Methods ◽  
Reduction Methods

The detection accuracy and model building time of machine learning (ML) classifiers are vital aspects for an intrusion detection system (IDS) to predict attacks in real life. Recently, researchers have introduced feature selection methods to increase the detection accuracy and minimize the model building time of a limited number of ML classifiers. Therefore, identifying more ML classifiers with very high detection accuracy and the lowest possible model building time is necessary. In this study, the authors tested six supervised classifiers on a full NSL-KDD training dataset (a benchmark record for Internet traffic) using 10-fold cross-validation in the Weka tool with and without feature selection/reduction methods. The authors aimed to identify more options to outperform and secure classifiers with the highest detection accuracy and lowest model building time. The results show that the feature selection/reduction methods, including the wrapper method in combination with the discretize filter, the filter method in combination with the discretize filter, and the discretize filter, can significantly decrease model building time without compromising detection accuracy. The suggested ML algorithms and feature selection/reduction methods are automated pattern recognition approaches to detect network attacks, which are within the scope of the Symmetry journal.

A Study of Feature Selection and Dimensionality Reduction Methods for Classification-Based Phishing Detection System

2021 ◽  
Vol 11 (1) ◽  
pp. 1-35
Author(s):  
Amit Singh ◽  
Abhishek Tiwari
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Detection System ◽  
Parallel Evolution ◽  
Feature Selection Method ◽  
Sensitive Information ◽  
Selection Methods ◽  
Survey Paper ◽  
Reduction Methods

Phishing was introduced in 1996, and now phishing is the biggest cybercrime challenge. Phishing is an abstract way to deceive users over the internet. Purpose of phishers is to extract the sensitive information of the user. Researchers have been working on solutions of phishing problem, but the parallel evolution of cybercrime techniques have made it a tough nut to crack. Recently, machine learning-based solutions are widely adopted to tackle the menace of phishing. This survey paper studies various feature selection method and dimensionality reduction methods and sees how they perform with machine learning-based classifier. The selection of features is vital for developing a good performance machine learning model. This work is comparing three broad categories of feature selection methods, namely filter, wrapper, and embedded feature selection methods, to reduce the dimensionality of data. The effectiveness of these methods has been assessed on several machine learning classifiers using k-fold cross-validation score, accuracy, precision, recall, and time.

scholarly journals An Efficient Malware Detection System using Hybrid Feature Selection Methods

2019 ◽  
Vol 9 (1S3) ◽  
pp. 224-228
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Unique Feature ◽  
Detection System ◽  
Malware Detection ◽  
Selection Methods ◽  
Training Process ◽  
Detection Systems ◽  
Selection Approach ◽  
Feature Selection Approach

Malware is a serious threat to individuals and users. The security researchers present various solutions, striving to achieve efficient malware detection. Malware attackers devise detection avoidance techniques to escape from detection systems. The key challenge is that growth of malware increases every hour, leading to large damages to users’ privacy. The training process takes much longer time, mining the unnecessary features. Feature Selection is effective in achieving unique feature set in detecting malware. In this paper, we propose a malware detection system using hybrid feature selection approach to detect malware efficiently with a reduced feature set. Machine learning based classification is performed on eight classifiers with two malware datasets. The experiments were done without and with feature selection. The empirical results show that the classification using selected feature set and XGB classifier identifies malware efficiently with an accuracy of 98.9% and 99.26% for the two datasets.

Feature Selection Algorithms for Classification and Clustering

2017 ◽  
pp. 143-167
Author(s):  
Arvind Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Filter Method ◽  
Selection Methods ◽  
Wrapper Method ◽  
Embedded Method ◽  
Selection Algorithms

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.

Feature Selection Algorithms for Classification and Clustering

2020 ◽  
pp. 422-442
Author(s):  
Arvind Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Filter Method ◽  
Selection Methods ◽  
Wrapper Method ◽  
Embedded Method ◽  
Selection Algorithms

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.

scholarly journals How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 838 ◽  
Author(s):  
Yekta Said Can ◽  
Dilara Gokay ◽  
Dilruba Reyyan Kılıç ◽  
Deniz Ekiz ◽  
Niaz Chalabianloo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress Level ◽  
Daily Life ◽  
Detection System ◽  
Real Life ◽  
Well Being ◽  
Physiological Signals ◽  
Detection Accuracy ◽  
Machine Learning Classification ◽  
Self Reports

Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies.

scholarly journals Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1764
Author(s):  
Ebrima Jaw ◽  
Xueming Wang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Model Building ◽  
Detection System ◽  
Probability Distributions ◽  
Ensemble Classifier ◽  
Model Complexity ◽  
Testing Time ◽  
Genetic Search

The emergence of ground-breaking technologies such as artificial intelligence, cloud computing, big data powered by the Internet, and its highly valued real-world applications consisting of symmetric and asymmetric data distributions, has significantly changed our lives in many positive aspects. However, it equally comes with the current catastrophic daily escalating cyberattacks. Thus, raising the need for researchers to harness the innovative strengths of machine learning to design and implement intrusion detection systems (IDSs) to help mitigate these unfortunate cyber threats. Nevertheless, trustworthy and effective IDSs is a challenge due to low accuracy engendered by vast, irrelevant, and redundant features; inept detection of all types of novel attacks by individual machine learning classifiers; costly and faulty use of labeled training datasets cum significant false alarm rates (FAR) and the excessive model building and testing time. Therefore, this paper proposed a promising hybrid feature selection (HFS) with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. Initially, we harness the various strengths of CfsSubsetEval, genetic search, and a rule-based engine to effectively select subsets of features with high correlation, which considerably reduced the model complexity and enhanced the generalization of learning algorithms, both of which are symmetry learning attributes. Moreover, using a voting method and average of probabilities, we present an ensemble classifier that used K-means, One-Class SVM, DBSCAN, and Expectation-Maximization, abbreviated (KODE) as an enhanced classifier that consistently classifies the asymmetric probability distributions between malicious and normal instances. HFS-KODE achieves remarkable results using 10-fold cross-validation, CIC-IDS2017, NSL-KDD, and UNSW-NB15 datasets and various metrics. For example, it outclassed all the selected individual classification methods, cutting-edge feature selection, and some current IDSs techniques with an excellent performance accuracy of 99.99%, 99.73%, and 99.997%, and a detection rate of 99.75%, 96.64%, and 99.93% for CIC-IDS2017, NSL-KDD, and UNSW-NB15, respectively based on only 11, 8, 13 selected relevant features from the above datasets. Finally, considering the drastically reduced FAR and time, coupled with no need for labeled datasets, it is self-evident that HFS-KODE proves to have a remarkable performance compared to many current approaches.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

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