1. Introduction

With the surging penetration of the internet and the rapid digitization of businesses, cybersecurity has soared in importance. Intrusion Detection Systems (IDS) have become indispensable components, acting as vigilant sentinels of network traffic, always on the lookout for anomalies. The evolution of IDS, from its inception to its current stature, reflects the escalating complexity of cyber threats (Smith, 2018).

The advent of IDS began in the late 1980s when cyber threats were mostly limited to viruses. Today, we are battling an ever-evolving slew of cyber-attacks ranging from sophisticated malware to Distributed Denial of Service (DDoS) attacks, necessitating a robust and agile IDS (Williams, 2019).

Table 1: Evolution of Cyber Threats Over the Years (Source: Williams, 2019)

2. Methodologies

Intrusion Detection Systems are largely categorized into two: Network-based IDS (NIDS) and Host-based IDS (HIDS).

NIDS primarily inspects traffic between hosts, scanning for any signs of an intrusion (Brown, 2020). These are usually deployed at strategic points in a network to monitor inbound and outbound traffic.

HIDS, on the other hand, is installed on individual hosts. It evaluates the incoming and outgoing packets from the device itself and takes protective actions accordingly (Davis, 2021).

Table 2: Comparison of NIDS and HIDS

Apart from these, the recent surge in the usage of Artificial Intelligence and Machine Learning has given birth to advanced IDS that can predict and adapt to new, unseen threats (Johnson & Goel, 2020).

3. Challenges in IDS

IDS, though effective, are not without challenges. One of the most daunting issues is the sheer volume of false positives. These can divert attention from real threats, draining valuable resources (Turner, 2021).

Moreover, the dynamic nature of cyber threats means that IDS must be consistently updated to recognize and fend off novel attack vectors. Without regular updates, even the most sophisticated IDS can become obsolete (Miller, 2020).

Table 3: Common Challenges in IDS Deployment

Related work on intrusion detection in cybersecurity

Intrusion Detection Systems (IDS) have evolved considerably over the past few decades, both in response to, and in anticipation of, the increasingly complex cyber threat landscape. This section aims to review the extant literature and research surrounding the development, methodologies, and challenges associated with IDS.

3.1 Historical perspective

The inception of IDS is traced back to the 1980s. According to Anderson (1980), early intrusion detection was a set of "watchdog" processes running on host computers. These systems mainly monitored system logs and user activities for any deviations from predefined 'normal' patterns (Denning, 1987). This pioneering work laid the foundation for anomaly-based detection systems, which are still in use today alongside signature-based systems.

3.2 Types of IDS: NIDS vs. HIDS

Network-based IDS (NIDS) and Host-based IDS (HIDS) are two primary classifications in the IDS domain. Axelsson (2000) posited that while NIDS offer scalability, particularly for large-scale networks, they might be less effective against insider threats. On the other hand, HIDS, as studied by Balas & Vieira (2005), are better suited to detect insider threats but can be resource-intensive.

3.3 AI and machine learning in IDS

With the deluge of data traversing modern networks, traditional IDS approaches have sometimes struggled to keep up. As a solution, several researchers have proposed the integration of artificial intelligence (AI) and machine learning (ML) techniques. Sommer & Paxson (2010) discussed the potential benefits of using machine learning for anomaly detection. They highlighted how ML algorithms could learn from existing data patterns and adjust to new, previously unseen attack vectors. Similarly, Laskov & Šrndić (2011) explored the use of support vector machines (SVM) in IDS, demonstrating their potential effectiveness in detecting network intrusions.

Table 4: AI Techniques used in IDS

3.4 Challenges in modern IDS

Despite advancements, IDS still grapple with several challenges. False positives, which lead to unnecessary investigations and resource allocation, remain a significant concern. Scarfone & Mell (2007) explored methodologies to minimize false positives without compromising on detection rates. In contrast, McHugh (2000) discussed the concern of false negatives, wherein actual threats go undetected, potentially causing more significant harm.

3.5 IDS in IoT environments

As the IoT landscape proliferates, securing interconnected devices has become paramount. According to Mitchell & Chen (2014), traditional IDS approaches might not be directly applicable to IoT scenarios due to the sheer number and heterogeneity of devices. They suggested tailored IDS models specifically designed for IoT contexts, emphasizing the importance of real-time monitoring and proactive threat detection.

In conclusion, the body of literature surrounding IDS is both vast and diverse, reflecting the system's evolving nature in response to the dynamic field of cybersecurity. While the challenges persist, continuous research and innovation offer promising avenues for more resilient and efficient intrusion detection mechanisms.

4. Future trajectories

The future of IDS is undoubtedly bright. With the integration of AI and ML, IDS are becoming smarter and more proactive. Moreover, the integration of threat intelligence platforms with IDS will make threat detection more precise and timely (Roberts, 2023).

Another promising avenue is the marriage of IDS with Internet of Things (IoT). As our homes and cities become smarter, securing them becomes paramount. IDS tailored for IoT environments will play a pivotal role here (Lewis & Clark, 2022).

5. Conclusion

IDS stand as the vanguard of cybersecurity defense. While they face challenges, the future trajectories, underlined by advancements in AI, ML, and IoT, promise a robust and agile ecosystem that can adeptly combat the escalating cyber threats.