A Review of Cyber Attack Classification Technique Based on Data Mining and Neural Network Approach

Dharamkar, Bhavna; Singh, Rajni

doi:10.14445/22312803/ijctt-v7p106

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…Bhavna et al in [35] reviewed several papers applying machine learning techniques to detect cyber threats. However, they have focused and described more on instruction detection.…”

Section: Includedmentioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

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Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

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“…Bhavna et al in [35] reviewed several papers applying machine learning techniques to detect cyber threats. However, they have focused and described more on instruction detection.…”

Section: Includedmentioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

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“…Dharamkar B. and Singh R.R., in [25], presented various methods of detecting and classifying cyberattacks based on data mining and a neural network approach, as well as criteria for evaluating identifiers and the dataset used to verify identifiers.…”

Section: Brief Overview Of Used Articlesmentioning

confidence: 99%

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

et al. 2022

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Information security is one of the most important attributes of distributed systems that often operate on unreliable networks. Enabling security features during the development of a distributed system requires the careful analysis of potential attacks or threats in different contexts, a process often referred to as «threat modeling». Information protection should be comprehensive, but it is also necessary to take into account the possibility of the emergence of threats specific to a certain information system. Many public and private organizations are still trying to implement system models and the threats directed at them on their own. The main reason for this is the lack of useful and high-quality methodologies that can help developers design system models. This review explores a variety of the literature on confidentiality- and integrity-aware system design methodologies, as well as threat classification methods, and identifies key issues that may be referenced by organizations to make design system processes easier. In particular, this article takes a look at the extent to which existing methodologies cover objects of protection and methods of classifying threats, as well as whether there are such models of systems in which the object itself and the threats directed at it are described. This includes whether the compiled models exhibit symmetry or asymmetry. This literature research shows that methodologies appear to be heterogeneous and versatile, since existing methodologies often only focus on one object of protection (a system). Based on the given analysis, it can be concluded that the existing methodologies only relate superficially to the description of system models and threats, and it is necessary to develop a more complete abstract model of the protected object and threats aimed at it in order to make this model suitable for any organization and protect it against most threats.

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“…Figure 1 shows an example of FNN composed of n input nodes, m nodes in one hidden layer, and k output nodes. FNN is widely employed as a classification tool in a variety of application domains, including medical analysis, credit scoring, pattern recognition, speech recognition, handwriting recognition, product inspection, drug discovery and development, biological classification, natural language processing, document classification, and network security [6,8,20]. In credit risk assessment, FNN is used to develop credit risk models from the historical data and predict the future corporate bankruptcies [1,2,27].…”

Section: Feedforward Artificial Neural Networkmentioning

confidence: 99%

Improve credit scoring using transfer of learned knowledge from self-organizing map

AghaeiRad

Chen

Ribeiro

2016

Neural Comput & Applic

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Credit scoring is important for credit risk evaluation and monitoring in the accounting and finance domain. For financial institutions, the ability to predict the business failure is crucial, as incorrect decisions have direct financial consequences. A variety of pattern recognition techniques including neural networks, decision trees, and support vector machines have been applied to predict whether the borrowers should be considered a good or bad credit risk. This paper presents a hybrid approach to building the credit scoring model and illustrates how the unsupervised learning based on self-organizing map (SOM) can improve the discriminant capability of feedforward neural network (FNN). Within the hybridization scheme, the knowledge (i.e., prototypes of clusters) found by SOM is transferred as input to the subsequent FNN model. Four real-world data sets are used in the experiments for credit approval problems. By varying the parameters, the experimental results demonstrate the predictive model built by the hybrid approach can achieve better performance than the stand-alone FNN particularly when a limited amount of labeled data is available. This gives some insights on how to construct more accurate predictive models when the data collection is difficult in some financial applications. A complete and unique graphical visualization technique is shown which better outlines the trade-off between distinct metrics and attained performance.

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A Review of Cyber Attack Classification Technique Based on Data Mining and Neural Network Approach

Cited by 14 publications

References 5 publications

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

Improve credit scoring using transfer of learned knowledge from self-organizing map

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