AI for Intrusion Detection Systems

By: Ameya Sree Kasa, Department of Computer Science & Engineering (Artificial Intelligence), Madanapalle Institute of Technology & Science, Angallu (517325), Andhra Pradesh. ameyasreekasa@gmail.com

Abstract:

The growing level of sophistication in cybercrimes clearly needs increased demands in terms of security solutions beyond the run-of-the-mill operation of defense. Today, an intrusion detection system is one of the expected parts of a security design which responds to the identified potential breach. This article focuses on the application of artificial intelligence towards the development of IDS efficiency. This article provides a list of AI techniques like machine learning and deep learning. Basic functionalities and how they can be included in intrusion detection are also shown. This detailed analysis focuses on the present AI approaches employed in IDS, including their merits, shortcomings, and potential future paths. We look into differences between Traditional and AI Intrusion Detection System approaches.

Keywords: Intrusion Detection Systems, cyber threats, Artificial Intelligence, Network Security

1. Introduction:

The rapid growth in internet and digital infrastructure has rapidly increased the threat terrain, thereby requiring more firm cybersecurity measures than ever. Firewalls and antivirus like those traditional security measures turn out to be of little use against the new operations of cyber enemies. IDS detect probable threats through the monitoring of network traffic and system activities for any abnormal behavior. Traditional IDSs suffer from some problems like high false positive rates, problem diagnosing new or undiscovered attacks, and limited scalability. To resolving these issues, Artificial Intelligence has been going through gradual integration with IDS. Here, AI forms some interesting possibilities in enhancing the accuracy, efficiency, and robustness of IDS through its inherent capabilities to learn and adapt. This article gives detailed analysis of Intrusion Detection System.

2. Analysis of Intrusion Detection Systems (IDS):

An intrusion detection system is one of the basic fundamental concepts driving a robust strategy of cybersecurity. It refers to a system put in place to monitor all activities over the network and the system for any unlawful, unethical actions or against policies. These systems can detect threats flow from unauthorized access, attacks, and resource misuse. Below is a detailed explanation of IDS types, architecture, and usage. [1]

2.1. Traditional Types of Intrusion Detection Systems:

Network Based Detection Systems (NIDS):

A Network-based Intrusion Detection System reads packets while they flow over the network, and tries to detect unusual activity. In most of the cases, it forms at key locations across the network so as to monitor all the incoming and outgoing traffic to and from all the devices. NIDS may further recognize potential threats when looking at packet headers and payloads. Examples include Snort and Suricata. Among other uses, they have become very prominent in detecting network-based threats and keeping the network security in check. [2]

Host based Detection Systems (HIDS):

A host-based intrusion detection system checks individual hosts or devices for signals of suspicious activity. It looks at all of the system-wide events that take place on a single host, such as system logs, file integrity, and the processes running. This will identify illegal modifications to system files, strange behavior of processes, and other host-level symptoms of compromise. Two very famous HIDS instances are OSSEC and Tripwire. They give profound insight into the security stance of specific hosts and are therefore very vital for endpoint protection. [3]

Signature Based Detection Systems (SIDS):

Signature-based intrusion detection systems use a database of attack signatures or patterns which are known previously. During such kinds of IDS, the incoming data is analyzed against its database and sends alarms when a match is discovered. Signature-based detection is very efficient that it can detect comprehensively and quickly attacks that are made against known vulnerabilities. However, it only works against known signatures of attacks and cannot used in detecting any new or unknown threats. Despite this limitation of signature-based IDS, it is still applied in wide ranges for common threats, because it has a very high degree of certainty. [4]

Anomaly Detection:

Anomaly-based IDS constructs a baseline of normal activity and identifies deviations from it as possible threats. Using statistical models, machine learning, or any advanced technique, anomaly-based detection systems can identify anomalous activity that may indicate an attack. [5] This will, therefore, be able to identify new and unknown threats which do not have representative signatures. However, anomaly-based IDS tend to have higher false positive rates because innocuous actions deviating from the defined baseline might get misclassified as malicious. In spite of this problem, anomaly-based detection is very valuable in detecting emerging risks and zero-day assaults. [6]

2.2. Usage of Intrusion Detection Systems:

The below figure1 shows usage of Intrusion Detection system

3. AI Approaches:

3.1. Machine Learning Techniques:

Machine learning techniques improve an intrusion detection system by studying the past data for trends of such malicious activity. In Supervised Learning the algorithms are trained using Decision Trees, SVM, Random Forests, etc., on a labeled dataset consisting of normal as well as malicious behavior. Unsupervised Learning the anomalies are detected as deviations from normal behavior in the absence of any preliminary information about attack patterns. The techniques used in unsupervised learning are K-means clustering and Principal Component Analysis. Through reinforcement Learning, algorithms learn the optimum responses to threats by trial and error; they learn their tactics of detection and response incrementally. [7]

3.2. Deep Learning Techniques:

Deep learning is a specialized type of machine learning techniques which uses neural networks with several layers to learn complex data representations. Convolutional Neural Networks (CNNs) are best at processing geographic data and recognizing patterns in the network traffic. Recurrent Neural Networks (RNNs) master at handling sequence data and time-series analysis which makes them ideal for detecting temporal attack patterns. Autoencoders are unsupervised anomaly detectors which learns concised representations of normal data and recognize deviations as potential hazards.

4. Advantages of AI Approach

• Enhanced Accuracy: Identifies complicated patterns to improve threat detection accuracy.
• Adaptability: Ability to respond to new and emerging dangers[8].
• Reduced False Positives: More accurate in distinguishing between legal and malicious operations.
• Real-Time Detection: Quickly processes massive amounts of data to detect threats as they occur.
• Scalability: The ability to handle big and complex situations efficiently. [6]

5. Challenges and Limitations:

5.1. Challenges:

• Data Quality and Quantity: Effective training requires a big amount of correct data.
• Complexity and Resource Intensity: Requires considerable computational resources and skill.
• Interpretability: Many models lack transparency, making decision processes difficult to explain.
• Adversarial assaults: Open to assaults that exploit model flaws.
• Integration: It can be challenging to integrate with existing security systems. [9]

5.2. Limitations:

• False Negatives: Even when false positives are reduced, some threats may still be missed.
• Overfitting is the risk of models underperforming on new or previously unknown data.
• High costs for development, implementation, and upkeep.
• Maintenance: To be effective, upgrades and retraining are required on a continuous basis.
• Ethical and Privacy Concerns: Addresses concerns about data collecting and user privacy. [10]

6. Differences between Traditional and AI Techniques:

The below table depicts differences between traditional and AI approaches

7. Conclusion:

As cyber-crimes are highly developed, upgrading Intrusion Detection Systems (IDS) along Artificial Intelligence (AI) provides solid benefits over old techniques. Traditional intrusion detection systems (IDS) techniques such as network-based, host-based, signature-based, and anomaly-based systems, frequently faces with high false positives, limited adaptability, and scalability. AI based IDS address these difficulties by increasing detection accuracy, lowering false positives and adjusting to emerging threats in real time. Though AI-based IDS improve accuracy, scalability, and adaptability, it still has limitations like data quality requirements, complexity, and integration issues. Despite of these challenges and limitations, integrating AI into IDS is a big step forward in cybersecurity, providing a more comprehensive and dynamic approach to threat detection, response and mitigation.

References:

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Cite As

Kasa A.S. (2024) AI for Intrusion Detection Systems, Insights2Techinfo, pp.1

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