# Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
## 3 Application of Ensemble Learning in NIDS
### Overview of Intrusion Detection Systems (IDS)  
In today's interconnected and digitally driven world, network security has become a paramount concern for individuals, businesses, and organizations alike. As the volume and complexity of cyber threats continue to grow, the need for robust IDS has become more critical than ever before. Intrusion detection plays a pivotal role in safeguarding computer networks and systems against a wide range of cyber-attacks, ranging from common malware and denial-of-service (DoS) attacks to APTs and zero-day exploits. The primary objective of intrusion detection is to detect and respond to unauthorized, malicious, or suspicious activities within a network or host environment. An intrusion can be an external attack launched from outside the network, an internal attack originating from within the organization, or even unintentional actions that may pose security risks. As such, IDS serve as a vigilant and watchful eye, continuously monitoring network traffic, system logs, and various other data sources to identify anomalies and potential signs of intrusion.  
There are two primary types of IDS: Host-based Intrusion Detection Systems (HIDS) and Network-based Intrusion Detection Systems (NIDS). Host-Based Intrusion Detection Systems (HIDS) are deployed on individual host machines or endpoints and focus on monitoring activities specific to that host. These systems analyze various host-related events, such as file system changes, login attempts, and process activities. HIDS are effective in detecting attacks targeting a single host or instances where malicious activitiesare confined to a particular system. They can provide granular visibility into the security of individual machines, making them valuable tools for endpoint security and forensic investigations. However, HIDS have limitations, especially when it comes to detecting attacks that span across multiple hosts or are distributed across the network. Since HIDS only analyze activities within the scope of a single host, they may miss attacks that manifest at the network level.  
Unlike HIDS, Network-Based Intrusion Detection Systems (NIDS) operate at the network level, analyzing network traffic as it traverses the network. NIDS are strategically positioned at critical points within the network to capture and analyze data packets, headers, and payload information. By inspecting network traffic, NIDS can detect a wide range of attacks that may target multiple hosts or exploit vulnerabilities at the network level. NIDS are particularly valuable for monitoring large-scale networks where numerous hosts are interconnected, as they can provide a centralized view of potential threats across the entire network. They are capable of detecting suspicious patterns, unauthorized access attempts, malware propagation, and other network-based anomalies.  
However, NIDS also face challenges. They may encounter performance bottlenecks when handling high network traffic, and they can be susceptible to encrypted traffic, which may conceal malicious activities from inspection. Moreover, NIDS may produce a significant number of false positives if not appropriately configured or if they lack the intelligence to differentiate between genuine threats and benign network activities. To address these challenges, the integration of machine learning techniques into NIDS has gained prominence. Machine learning enables NIDS to learn from historical network data, recognize patterns of normal behavior, and detect deviations that may indicate potential attacks. Moreover, by leveraging ensemble machine learning, NIDS can enhance their accuracy and resilience against sophisticated cyber-attacks, offering a more effective defense mechanism for safeguarding modern computer networks and critical infrastructure.