## 1 Introduction  
Computer networks have developed rapidly over the years, significantly contributing to social and economic development. International trade, healthcare systems, and military capabilities are examples of human activities that increasingly rely on computer networks. This has led to an increasing interest in network security from research and industries. The main role of IDSs is critical since the networks can be vulnerable to be attacked by both internal and external intruders [1, 2]. The IDS has become one of the fundamental components of computer security to detect these malicious threats with the aim of protecting systems from common harms and group vulnerabilities [3]. IDS is to create systems that do not need expert knowledge to create and update signatures but rather learn and update themselves. For example, the system should have low false positive rates to make practice for deployment in a live network environment to improve network security. The goal of intrusion detection is to identify preferably in real time, the unauthorized use, misuse,and abuse of computer systems by both system insiders and external penetrators [4]. The intrusion detection problem is becoming more challenging due to the significant increase in computer network connectivity, the speed of technological advancement, and the ease of finding hackers for hire. Thus, IDSs are security systems used to monitor, recognize, and report malicious activities or policy violations in computer systems and networks. However, the false recognition of IDS results in that it is difficult for network administrators to deal with intrusion reports. These IDSs rely on the signatures of known attacks. Human independent IDSs that incorporate machine learning techniques have been developed as a solution to solve this problem. Besides, machine learning IDSs learn from normal and abnormal traffic by training on a dataset in order to predict an attack by using classification. Several machine learning techniques have been successfully implemented as classifiers on IDSs, but they have numerous flaws such as low throughput and high false detection rates [5; 6; 7; 8]. Another challenge in IDS is the different attack types including Probe, R2L, U2R, and Denial-of-Service (DoS) should be detected well by IDS techniques. However, one of the most difficult attacks to detect is R2L attack because it related to the host level features and network level. Besides, U2R attack is also hard to detect at an early stage because it involved the semantic detail such as content-based and target an application. This becomes one challenge in IDS techniques. Presently, IDSs are generally categorized as signature-based (misuse detection) systems, behavior-based (anomaly detection) systems, or hybrid systems. Misuse IDSs are commonly deployed in practical networks since they are robust or low false alarm rate (FAR). However, the main shortcoming is their inability to detect new attacks. Current research is focused on the anomaly detection approach since it can detect new attacks. However, these approaches suffer from high false positive rates leading to impractical implementation in live network settings. On the other hand, neural networks have been employed in anomaly detection to identify whether the behaviour of data is normal or abnormal. This network can detect both known and unknown attacks with moderate performance. Several researchers have focused on developing IDSs based on deep networks. However, a robust deep network model is how to estimate or optimize its parameters as effectively. This model designing is one of the challenges derived from high-dimensional data leading to the curse of dimensionality phenomenon. In order to avoid this issue, input patterns need to be reduced the dimensionality. This method can also reduce the amount of required computation.