The application of cloud computing has increased tremendously in both public and private organizations. However, attacks on pose a serious threat to confidentiality data integrity. Therefore, there is need for proper mechanism detecting intrusions. In this paper, we have proposed intrusion detection system (IDS) that focused boosting the classification accuracy by improving feature selection we...

Abstract Network intrusion detection systems (NIDS) are the most common tool used to detect malicious attacks on a network. They help prevent ever-increasing different and provide better security for NIDS classified into signature-based anomaly-based detection. The type of is which based machine learning models able with high accuracy. However, in recent years, has achieved even results detecti...

Cyber-attacks are increasing day by day. The generation of data the population world is immensely escalated. advancements in technology, intern leading to more chances vulnerabilities individual’s personal data. Across it became a very big challenge bring down threats security. These not only targeting user and also destroying whole network infrastructure local or global level, attacks could be...

Influenza virus unspliced NSl mRNA, like retroviral pre-mRNAs, is efficiently exported from the nucleus and translated in the cytoplasm of infected cells. With human immunodeficiency virus (HIV), the transport of viral pre-mRNAs is facilitated by the viral Rev protein. We tested the possibility that the influenza virus NSl protein, a nuclear protein that is encoded by unspliced NSl mRNA, has th...

In this paper, we propose a hybrid approach for designing Intrusion Detection Systems. This approach is based on a Fuzzy Genetic Machine Learning Algorithm to generate fuzzy rules. The rules are able to solve the classification problem in designing an anomaly IDS. The proposed approach supports multiple attack classification. It means that, it is able to detect five classes consist of Denial of...

High dimension, redundancy attributes and high computing cost issues usually exist in the industrial Internet intrusion detection field. For solving these problems, the mutual information-based intrusion detection model for industrial Internet was proposed. Firstly, by using features selection method based on mutual information, the attributes set was reduced and traffic characteristics vector ...

In this paper, we study the Nyström type subsampling for large scale kernel methods to reduce the computational complexities of big data. We discuss the multi-penalty regularization scheme based on Nyström type subsampling which is motivated from well-studied manifold regularization schemes. We develop a theoretical analysis of multi-penalty least-square regularization scheme under the general ...

Increasing internet usage and connectivity demands a network intrusion detection system combating cynical network attacks. Data mining therefore is a popular technique used by intrusion detection system to prevent the network attacks and classify the network events as either normal or attack. Our research study presents a wrapper approach for intrusion detection. In this framework Feature selec...

Bộ tự mã hóa là một mô hình học không giám sát trong đó các tham số được điều chỉnh để vector đầu ra gần giống nhất với vào. Trong bài báo này, chúng tôi sử dụng bộ phát hiện kết nối bất thường mạng Internet. Mức lỗi tái tạo khi hoá sẽ phân lớp thành bình và thường. Chúng trình bày ba phương pháp độ tạo: ngưỡng cho trước, theo kiểm định giả thuyết Bayes Neyman-Pearson. Độ chính xác trung đạt tr...

This research paper presents a framework on how to build up malware dataset. Many researchers took longer time to clean the dataset from any noise or to transform the dataset into a format that can be used straight away for testing. Therefore, this research is proposing a framework to help researchers to speed up the malware dataset cleaning processes which later can be used for testing. It is ...