# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 1 Introduction  
The number of services that are available to consumers globally has increased due to internet access as a result of the quick expansion of communications and computing infrastructure. The Internet of Things (IoT) has dramatically altered how devices communicate with one another and with humans. However, the quantity and variety of cyberattacks, including malicious attacks, malicious eavesdropping, IoT network viruses, and others, are gravely threatening the protection of people's information and the preservation of their property. As a result, communication and information IoT security are now essential for both people and society as a whole [1]. One primary type of IoT security which is frequently used is firewalls. However, in units that need strong protection (such as state buildings,etc. ), it is no longer appropriate because of the problems with personal settings and the latency for new types of crimes [2]. The need for robust network security measures has become critical in recent years, as the number of connected devices has increased. While traditional security measures such as firewalls and data encryption techniques remain useful, they have proven ineffective against the evolving and sophisticated nature of network threats [3]. This has resulted in the development of more sophisticated security solutions, such as intrusion detection systems (IDSs), which are specifically designed to detect and prevent malicious network activity. IDSs work by analyzing network traffic and detecting patterns of behavior that are indicative of potential security threats such as unauthorized access attempts, malware infections, and data breaches [4; 5]. Because of the increasing complexity and diversity of network threats, security experts are finding it increasingly difficult to detect and respond to these incidents manually. IDSs help to alleviate this problem by automating the threat detection and response process with machine learning algorithms and artificial intelligence (AI). Furthermore, IDSs are capable of detecting new and emerging threats that traditional security measures may miss [6]. They can also provide valuable insights into network traffic patterns and potential vulnerabilities, allowing network administrators to take proactive measures to prevent security incidents from occurring. Machine learning (ML) has gained popularity in intrusion detection systems (IDSs) due to its ability to analyze large amounts of data and detect patterns that traditional methods may not be able to detect. ML-based intrusion detection systems (IDSs) use algorithms to learn from data and identify suspicious behavior, which can improve detection rates and response times [7]. However, ML-based intrusion detection systems face a number of challenges, including low detection rates and the need for extensive feature engineering. Low detection rates can occur when the IDS is not properly trained or when the data being analyzed do not accurately represent real-world scenarios. Furthermore, ML-based intrusion detection systems require large amounts of data to learn effectively, which can be costly and time-consuming to acquire [8]. The process of selecting and extracting relevant features from data that can be used to train the ML algorithm is known as feature engineering. In intrusion detection systems, feature engineering entails selecting and extracting relevant network traffic data to train the ML algorithm to detect potential security threats. This process, however, can be difficult and requires expert knowledge of both network security and machine learning techniques. To address these challenges, researchers have developed a variety of techniques to improve the performance of ML-based intrusion detection systems. Deep learning, for example, can automatically extract relevant features from raw data, reducing the need for extensive feature engineering. To improve detection rates, another approach is to use hybrid systems that combine different ML algorithms, such as supervised and unsupervised learning [9].