Titlebook: Cyberspace Safety and Security; 11th International S Jaideep Vaidya,Xiao Zhang,Jin Li Conference proceedings 2019 Springer Nature Switzerla

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Sándor Berényi,Tibor Gyula Nagyetection from large-scale log data plays a key role in building secure and trustworthy systems. Anomaly detection model based on machine learning has achieved good results in practical applications. However, logs generated by modern large-scale distributed systems are more complex than ever before i

Hypopnea

https://doi.org/10.1007/978-1-349-04124-4ized as a vehicle for malicious behavior. In this paper, machine learning algorithm will be used to detect malicious PDF document, and evaluated on experimental data. The main work of this paper is to implement a malware detection method, which utilizes static pre-processing and machine learning alg

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https://doi.org/10.1007/978-1-349-04124-4nformation processing and sharing by web applications, the situation for web attack detection or prevention becomes increasingly severe. We present a prototype implementation called DeepWAF to detect web attacks based on deep learning techniques. We systematically discuss the approach for effective

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Sándor Berényi,Tibor Gyula Nagy The accuracy of the traditional rule-based intrusion detection system is affected. And the false alarm rate of machine learning-based intrusion detection system is high due to the lack of causal link analysis among sampled data and attack events. Aiming at the problem, this paper proposes an intell

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Sándor Berényi,Tibor Gyula Nagyted for Back Propagation) neural network, which has the problem of easy to fall into local optimization when constructing software defect prediction model, and finally affects the prediction accuracy. Firstly, the optimization ability of GA (abbreviated for Genetic Algorithms) is introduced to optim

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