Online learning and teaching become the popular channel for all participants, because they can access the courses everywhere with the high-speed internet. E-certificate is being prepared for everyone who has participated or passed the requirements of the courses. Because of many benefits from e-certificate, it may become the demand for intruders to counterfeit the certificate. In this paper, Rivest-Shamir-Adleman (RSA)'s digital signature is chosen to sign e-certificate in order to avoid being counterfeited by intruders. There are two applications to manage ecertificate. The first application is the signing application to sign the sub image including only participant's name in e-certificate. In general, the file of digital signature is divided from e-certificate. That means, both of them must be selected to compare each other in checking application. In fact, the solution will be approved when each pixel of participant's name is equal to each part from the decrypted message at the same position. In experimental session, 40 e-certificates are chosen for the implementation. The results reveal that the accuracy is 100% and both of signing and checking processes are completed rapidly fast, especially when signing application is applied with Chinese remainder theorem (CRT) or the special technique of CRT. Therefore, the proposed method is one of the best solutions to protect e-certificate from the forgery by intruders.

No abstract

Throughout recent times, cybersecurity problems have occurred in various business applications. Although previous researchers proposed to cope with the occurrence of cybersecurity issues, their methods repeatedly replicated the training processes for several times to classify datasets of these problems in streaming non-stationary environments. In dynamic environments, the conventional methods possibly deteriorate the adaptive solution to prevent these issues. This research proposes a one-pass-throw-away learning using the dynamical structure of the network to solve these problems in dynamic environments. Furthermore, to speed up the computational time and to maintain a minimum space complexity for streaming data, the new concepts of learning in forms of recursive functions were introduced. The information gain-based feature selection was also applied to reduce the learning time during the training process. The experimental results signified that the proposed algorithm outperformed the others in incremental-like and online ensemble learning algorithms in terms of classification accuracy, space complexity, and computational time.

This research paper presents a packet header anomaly detection approach by using Bayesian belief network which is a probabilistic machine learning model. A DARPA dataset was tested for the performance evaluation in the packet header anomaly detection or DoS intrusion-type. In this respect, the proposed method using Bayesian network gives an outstanding result determining a very high detection rate of reliability at 99.04 % and precision at 97.33 % on average.

No abstract