In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal compression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able...

The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users’ activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information fr...

Second generation of Multi-agent heterogeneous intrusion detection system (M-AHIDS) is a prototype proposed to detect untrusted and unusual network behaviour. The M-AHIDS is based on online traffic statistics in sFlow format acquired by network device with the sFlow agent and is able to perform a real-time surveillance of the 10 Gb networks. However, after an immense reimplementation it is capa...

Intrusion detection systems (IDS) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially healthcare, where sensitive information (patient’s medical history, prescriptions, electronic health records, bills/debts, and other data points) open to compromise from adversaries. In big era, classical machine learning...

The enormous increase in the use of Internet daily life has provided an opportunity for intruder attempt to compromise security principles availability, confidentiality, and integrity. As a result, organizations are working level by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors analyzes network flow attacks detection. There lot researches pr...

Digitalization, Industry 4.0 and Internet of things (IoT) have become more popular in the recent years. Most these systems depend on micro-controllers sensors. These sensors are mostly cheap, low RAM CPU systems; thus, they resource constrained environments. In this study, a supervised learning classifier comparison technique suitable for environments is proposed. This technique, Decision Analy...

Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is use resampling, which adjusts ratio between different classes, making more balanced. This research looks at resampli...

Since there is a close relationship between network information security attack events and time complexity, it necessary to count the degree of correlation current connection record within certain period before. Only in this way can data be better reflected. In paper, rough Fourier fast algorithm based on set theory proposed. Based characteristic attributes intrusion detection with most value c...

Security in IoT networks is currently mandatory, due to the high amount of data that has be handled. These systems are vulnerable several cybersecurity attacks, which increasing number and sophistication. Due this reason, new intrusion detection techniques have developed, being as accurate possible for these scenarios. Intrusion based on machine learning algorithms already shown a performance t...

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The ...