Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review

Saranya, T.; Sridevi, S.; Deisy, C.; Chung, Tran Duc; Khan, Mohamed K.

doi:10.1016/j.procs.2020.04.133

Cited by 249 publications

(95 citation statements)

References 15 publications

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“…Evaluation metrics describe the performance of the classification model. The critical point behind the classification is an evaluation metric used to understand the performance and efficiency of an algorithm [58]. Thus, several evaluation metrics mentioned in the experimental results and discussion section were utilized so as to show the performance of the proposed methods.…”

Section: B Results and Discussionmentioning

confidence: 99%

A New Malware Classification Framework Based on Deep Learning Algorithms

Aslan

Yılmaz²

2021

IEEE Access

129

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Recent technological developments in computer systems transfer human life from real to virtual environments. Covid-19 disease accelerates this process. Cyber criminals' interest shifts from real to virtual life as well. This is because it is easier to commit a crime in cyberspace rather than regular life. Malicious software (malware) is an unwanted software which is frequently used by cyber criminals to launch cyber attacks. Malware variants are continuing to evolve by using advanced obfuscation and packing techniques. These concealing techniques make malware detection and classification significantly challenging. Novel methods which are quite different from traditional methods must be used to effectively combat new malware variants. Traditional artificial intelligence (AI) specifically machine earning (ML) algorithms are no longer effective to detect all new and complex malware. Deep learning (DL) approach which is quite different from traditional ML algorithms can be a promising solution when detecting all variants of malware. In this study, a novel deep-learning-based architecture is proposed which can classify malware variants based on a hybrid model. The main contribution of the study is to propose a new hybrid architecture which integrates two wide-ranging pre-trained network models in an optimized manner. This architecture consists of four main stages: namely, data acquisition, the design of deep neural network architecture, training of the proposed deep neural network architecture, and evaluation of the trained deep neural network. The experimental results show that the suggested method can effectively classify malware with high accuracy which outperforms the state of the art methods in the literature.INDEX TERMS Malware, malware classification, malware detection, malware variants, deep neural networks, transfer learning, deep learning.

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Section: B Results and Discussionmentioning

confidence: 99%

A New Malware Classification Framework Based on Deep Learning Algorithms

Aslan

Yılmaz²

2021

IEEE Access

129

View full text Add to dashboard Cite

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“…Evaluation metrics describe the performance of the classification model. The critical point behind the classification is an evaluation metric used to understand the performance and efficiency of an algorithm [44]. Building an intelligent detection system capable of detecting various types of network intrusions, one must evaluate the performance of the model via using different evaluation metrics, then compare the results to find the best fit model [45], [46].…”

Section: Evaluation Performance Appropriate Metricsmentioning

confidence: 99%

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih

Abdulazeez

2021

JSCDM

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Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.

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“…Nevertheless, Saranyaa et al [76] presented a comparative analysis focused on common domain areas of various ML approaches (such as Linear Discriminant Analysis ( LDA), Classification and Regression Trees (CART) and RF) in IDS. An ML-based experiment was also conducted using the KDD'99 cup dataset for IoT applications.…”

Section: Related Review Workmentioning

confidence: 99%

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

et al. 2021

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The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resourceconstrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques can not be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms-branches of Artificial Intelligence (AI)-can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AIbased security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review

Cited by 249 publications

References 15 publications

A New Malware Classification Framework Based on Deep Learning Algorithms

A New Malware Classification Framework Based on Deep Learning Algorithms

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

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