Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions

Borjali, Ahmad; Wang, Jian; Ali, Zain Anwar

doi:10.1155/2018/6383145

Cited by 18 publications

(10 citation statements)

References 32 publications

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“…e firmware is one technique to protect the system, but nowadays, the external mechanisms have emerged and quickly become popular. One important method for data mining in intrusion detection problem proposed in the literature is to use machine learning techniques [2][3][4][5][6][7][8]. e IDS has monitored directly the network transactions where each transaction is either normal or malicious.…”

Section: Introductionmentioning

confidence: 99%

Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network

Thang

Pashchenko

2019

Journal of Computer Networks and Communications

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The aim of machine learning is to develop algorithms that can learn from data and solve specific problems in some context as human do. This paper presents some machine learning models applied to the intrusion detection system in WiFi network. Firstly, we present an incremental semisupervised clustering based on a graph. Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In fact, for traditional clustering such as K-means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there is no incremental semisupervised clustering in the literature. Secondly, by combining a K-means algorithm and a measure of local density score, we propose a fast outlier detection algorithm, named FLDS. The complexity of FLDS is On1.5 while the results obtained are comparable with the algorithm LOF. Thirdly, we introduce a multistage system-based machine learning techniques for mining the intrusion detection data applied for the 802.11 WiFi network. Finally, experiments conducted on some data sets extracted from the 802.11 networks and UCI data sets show the effectiveness of our new proposed methods.

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Section: Introductionmentioning

confidence: 99%

Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network

Thang

Pashchenko

2019

Journal of Computer Networks and Communications

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“…The flooding attack is a type of network intrusion where the attacker consumes the resources of a system in a network by sending frequent network traffic, resulting in a denial of the resources to the legitimate users. ML approaches have many applications in building intelligent systems that can automate network intrusion mitigation such as distributed DDSA [5]. The applications of ML systems are vital to knowledge discovery (KD) from intrusion data records.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks

Salau

Assegie²,

Akindadelo³

et al. 2023

Bulletin EEI

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Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.

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“…It is important for the managers to learn about these while considering the different challenges in data mining and machine learning for security. As Ahmad et al [16] underscored, a large number of industries are already dependent on network connections, especially those that have sensitive business trading and security matters. In this case, communications and networks are extremely vulnerable to the challenges and threats or risks, such as hacking.…”

Section: Overview Of Android Platform and Securitymentioning

confidence: 99%

PlusApps: Towards a Privacy Risk Analysis for Android Plus Applications

Alzahrani¹

2020

IJACSA

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The Android platform leads the mobile operating system marketplace and subsequently has drawn the interest of malware authors and researchers. The significant number of proposed malware detection techniques, classification models and practical reverse engineering solutions are insufficient and there is a lack of perfection. Also, the number of Android apps has increased significantly in recent years, as has the number of apps revealing confidential data. It is essential to investigate the applications and make sure that none of them are leaking privacy data, and consequently a privacy leak analysis approach is needed. Therefore, this paper investigates plus apps behavior and data leakages with a machine-learning algorithm to determine the best features for differentiating plus apps from original apps. The result of the analysis discloses that the SVM classifier presents the greatest accuracy. Further investigation demonstrates that the classifier with the ranking algorithm that uses correlation coefficient (CorEvel) and information gain (InfGain) methods offers more exceptional precision than the other correlation algorithms. The result of this experiment proves that the ranking algorithm is able to decrease the dimension of features and produce an accuracy of 96.60%.

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Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions

Cited by 18 publications

References 32 publications

Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network

Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network

Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks

PlusApps: Towards a Privacy Risk Analysis for Android Plus Applications

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