# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
### Intrusion Detection Systems (IDS)  
Intrusion detection refers to identifying malicious activities carried out against information systems. These malevolent acts, termed intrusions, are efforts to gain unauthorized access to a computer system. Intruders can be categorized into two main types: internal and external. Internal intruders are individuals within the network who, despite having some legitimate access, aim to elevate their access privileges to misuse resources they are not authorized for. In contrast, external intruders are individuals outside the network aiming to infiltrate it and access system information without permission [33].  
Both types of intruders pose distinct challenges. Internal intruders, already having some degree of legitimate access, can exploit vulnerabilities from within, making their actions more complicated to detect. Their familiarity with the system's architecture and potential weak points can make their intrusions more targeted and potentially more damaging. On the other hand, external intruders, although initially lacking access, often employ a wide range of techniques, from brute-force attacks to sophisticated phishing schemes, to breach the system's defenses [34].  
Moreover, the rise of IoT devices and the expanding digital landscape have further complicated intrusion detection. With more entry points and a diverse range of devices, networks are more susceptible than ever. This underscores the importance of robust security measures, continuous system monitoring, and regular updates to defend against evolving threats. Additionally, organizations must foster a culture of security awareness, ensuring that every internal or external user is well-informed about potential risks and best practices to mitigate them.  
The evolving dynamics of cyber threats necessitate an adaptive and layered approach to security. Intrusion detection systems (IDS) are just one component of a comprehensive cybersecurity strategy. Beyond simple detection, the focus has shifted towards intrusion prevention systems (IPS) that not only detect but also take proactive measures to prevent unauthorized access [35,36].  
Furthermore, with the integration of artificial intelligence and machine learning in security systems, there is an opportunity to predict and identify novel threats before they manifest. These predictive systems analyze patterns and behaviors, allowing them to flag anomalous activities even if they do not match known threat signatures.  
Yet, technology alone is not the panacea. Human factors play a significant role in security breaches. Regular training sessions, workshops, and awareness campaigns should be organized for employees and users. This ensures that they are aware of the potential risks and equipped with the knowledge to recognize and report suspicious activities [37].  
The principle of least privilege (PoLP) should be strictly adhered to, meaning that users should only be granted access to the information and resources necessary for their specific tasks, reducing the potential damage of an internal intrusion.  
In a world where cyber threats continually evolve, staying a step ahead is crucial. This requires cutting-edge technology, strategic planning, and an informed and vigilant user base. By integrating these elements, organizations can fortify their defenses, ensuring data integrity and maintaining the trust of their users [38].