Научная статья на тему 'INTRUSION DETECTION IN INFORMATION SECURITY: BENCHMARKING MODERN SOLUTIONS'

INTRUSION DETECTION IN INFORMATION SECURITY: BENCHMARKING MODERN SOLUTIONS Текст научной статьи по специальности «Компьютерные и информационные науки»

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Ключевые слова
intrusion detection systems / cybersecurity / information security / signature-based detection / anomaly detection / machine learning / AI-enhanced systems / behavioral analysis / benchmarking / scalability / adaptability

Аннотация научной статьи по компьютерным и информационным наукам, автор научной работы — Pernebek M.E., Tokseit D.K.

Intrusion detection remains a cornerstone of information security, addressing the persistent challenge of identifying and mitigating unauthorized access and cyberattacks. With the growing complexity of modern threats, this article examines and benchmarks advanced intrusion detection systems (IDS) from a theoretical perspective, highlighting key technologies such as machine learning, anomaly detection, and artificial intelligence. By exploring the design principles, operational frameworks, and theoretical underpinnings of these solutions, the study seeks to understand their effectiveness in adapting to evolving attack methodologies. Emphasis is placed on the comparative evaluation of system architectures, detection techniques, and their theoretical capabilities against emerging cybersecurity challenges. This analysis provides a foundational understanding of current IDS innovations, offering insights into future research directions and the development of more resilient security solutions.

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Текст научной работы на тему «INTRUSION DETECTION IN INFORMATION SECURITY: BENCHMARKING MODERN SOLUTIONS»

УДК 004 Pernebek M.E., Tokseit D.K.

Pernebek M.E.

master student of the Department of Information Technology L.N. Gumilyov Eurasian National University (Astana, Kazakhstan)

Tokseit D.K.

Senior Lecturer, PhD of the Department of Information Technology L.N. Gumilyov Eurasian National University (Astana, Kazakhstan)

INTRUSION DETECTION IN INFORMATION SECURITY: BENCHMARKING MODERN SOLUTIONS

Аннотация: intrusion detection remains a cornerstone of information security, addressing the persistent challenge of identifying and mitigating unauthorized access and cyberattacks. With the growing complexity of modern threats, this article examines and benchmarks advanced intrusion detection systems (IDS) from a theoretical perspective, highlighting key technologies such as machine learning, anomaly detection, and artificial intelligence. By exploring the design principles, operational frameworks, and theoretical underpinnings of these solutions, the study seeks to understand their effectiveness in adapting to evolving attack methodologies. Emphasis is placed on the comparative evaluation of system architectures, detection techniques, and their theoretical capabilities against emerging cybersecurity challenges. This analysis provides a foundational understanding of current IDS innovations, offering insights into future research directions and the development of more resilient security solutions.

Ключевые слова: intrusion detection systems, cybersecurity, information security, signature-based detection, anomaly detection, machine learning, AI-enhanced systems, behavioral analysis, benchmarking, scalability, adaptability.

Introduction.

Intrusion detection systems (IDS) are an integral part of modern cybersecurity, designed to detect and prevent unauthorized access and malicious activities in network environments. The landscape of cyber threats is rapidly evolving, making traditional detection methods such as signature-based systems inadequate in addressing novel and complex attacks [1]. These traditional systems are efficient for known threats but falter when dealing with zero-day exploits and dynamic attack strategies [2].

Modern approaches to intrusion detection leverage advanced technologies like machine learning (ML) and artificial intelligence (AI). These systems promise enhanced adaptability and precision in identifying threats by analyzing patterns, detecting anomalies, and predicting potential attacks [3]. Their theoretical basis lies in statistical modeling, behavioral analysis, and computational algorithms that mimic cognitive functions.

Benchmarking modern IDS involves examining their effectiveness through theoretical metrics such as detection accuracy, scalability, resource efficiency, and adaptability to emerging threats. This article undertakes a systematic review of contemporary IDS solutions, comparing their conceptual foundations and frameworks to provide a comprehensive understanding of their strengths, limitations, and future prospects.

Key Modern Intrusion Detection Approaches.

Signature-Based Detection: Description, Advantages, and Limitations.

Signature-based detection is one of the most traditional techniques in Intrusion Detection Systems (IDS). It relies on a database of predefined attack signatures, which are patterns that describe known malicious activity such as viruses, worms, or unauthorized network access. When the system detects data matching these signatures, it raises an alert [4].

Advantages: High Accuracy for Known Threats - Signature-based systems excel in detecting well-known, predefined attack types, providing high accuracy when faced with recognized threats. Low False Positive Rate - Since signatures correspond

to specific, established threats, the likelihood of generating false positives is relatively low compared to other detection methods.

Limitations: Ineffective Against Unknown Threats - Signature-based detection struggles to detect new, previously unseen threats like zero-day attacks or polymorphic malware that change their characteristics after each attack [3]. Required Frequent Updates - To maintain its effectiveness, signature databases need constant updates to include new attack signatures, posing challenges in dynamic environments. Scalability Issues - As the volume of network traffic increases, managing and updating a growing signature database becomes increasingly complex and resource-intensive.

Anomaly-Based Detection: How It Works, Strengths, and Challenges.

Anomaly-based detection monitors the network or system behavior and establishes a baseline of normal activity. When it detects deviations from this normal behavior, it flags the event as a potential intrusion. This method works by identifying outliers or deviations from expected patterns, regardless of whether they are previously known attacks. The system first defines what is considered "normal" based on network traffic, user behavior, and system usage over time. It can then detect novel attacks by observing anomalies that deviate significantly from this norm [5].

Strengths: Detection of Unknown Attacks - Unlike signature-based methods, anomaly detection can potentially identify new or unknown threats based on unusual system behavior. Adaptability - With proper tuning, anomaly-based systems can adapt to different environments and evolving patterns, making them effective in dynamic settings.

Challenges: High False Positive Rate - Due to frequent changes in system operations, benign anomalies (e.g., software updates, configuration changes) may be flagged as threats, leading to false positives. High Complexity - Developing and maintaining a reliable baseline for system behavior can be difficult, especially in complex environments with varying workloads and conditions.

Machine Learning-Based Detection: Insights into Its Theoretical Potential and Drawbacks.

Machine learning (ML)-based detection involves using statistical models to identify patterns in network traffic, system logs, or other data sources. These systems "learn" from data to improve detection over time and can be categorized as supervised or unsupervised depending on the type of input data and the learning approach used [6].

Insights into Its Theoretical Potential: Adaptability and Scalability - ML techniques have the ability to process massive datasets and learn dynamically, making them powerful for handling large, complex networks where traditional methods may falter. Prediction of Future Attacks - With well-trained models, machine learning can also help in anticipating potential threats based on historical patterns, making systems proactive rather than purely reactive.

Drawbacks: Data Quality Dependence - ML-based systems depend heavily on large, labeled datasets. Poor-quality or imbalanced data can lead to biased models that miss key attack patterns. Training Time and Resources - Machine learning models require substantial computational resources for training and can take a long time to adapt to new kinds of attacks if the data is scarce or insufficient.

Behavioral Analysis: Its Focus on User Patterns and Operational Considerations.

Behavioral analysis-based detection focuses on identifying deviations in the behavior of users, applications, and systems. It examines both typical user activities (like login patterns, file access, and communication behaviors) and system operations (e.g., CPU or memory usage) to flag anomalies. The system continuously monitors interactions and compares them against historical user or system behavior profiles, detecting any deviations that may indicate malicious activities, such as insider threats or compromised user accounts [7].

Strengths: Precision in Targeted Detection - By focusing on individual user and system behavior, this method can more precisely detect attacks like account takeovers or insider threats that do not always manifest as large-scale disruptions.

Dynamic Adaptation - Behavioral models evolve with the user or system, so long-term monitoring allows these models to refine themselves based on changing usage patterns.

Challenges: Overfitting Risk - Excessive focus on individual users or applications can lead to overfitting, where the system becomes too tailored to a specific user's behaviors and fails to detect abnormal but legitimate activities. Privacy Issues -Constantly monitoring user behavior raises ethical and privacy concerns, especially in sensitive environments or jurisdictions with strict data protection regulations.

AI-Enhanced Systems: Adaptability, Computational Demands, and Ethical Concerns.

Artificial Intelligence (AI)-enhanced intrusion detection systems leverage advanced algorithms, such as deep learning, to automatically process and learn from vast amounts of security data. These systems offer predictive capabilities and sophisticated decision-making based on continuous data flow [8].

Adaptability: AI systems can improve their detection capabilities over time, with deep learning networks particularly suited to learning and evolving to detect complex and unknown attack methods. They can adjust automatically to new and dynamic threat environments without requiring significant reconfiguration.

Computational Demands: AI-enhanced systems are resource-intensive, requiring substantial computational power for both training and real-time operation. The need for large datasets to ensure high-quality training results can also strain system resources, particularly in high-throughput or real-time environments.

Ethical Concerns: The extensive data analysis required for AI-driven systems raises concerns related to data privacy, surveillance, and the potential misuse of sensitive information. These issues are particularly pressing in the context of regulatory compliance and ethics in artificial intelligence implementation in cybersecurity.

Evaluation Criteria for Benchmarking.

Accuracy: Measures how well the IDS detects true threats and minimizes false positives/negatives. Crucial for assessing the effectiveness of detection.

Scalability: Evaluates how well the IDS performs as the network grows in size and complexity, including handling high volumes of data.

Speed (Latency): Focuses on the time taken by the IDS to analyze traffic and identify threats, critical for real-time environments.

Resource Efficiency: Assesses the amount of computational power, memory, and storage required for the IDS to function effectively.

Adaptability: The ability of the IDS to handle emerging and novel threats without requiring major updates or changes.

Table 1: Benchmarking Modern Solutions.

IDS Type Accuracy Scalability Speed (Latency) Resource Efficiency Adaptability

Signature- Based Detection High for known attacks, low for zero-day/novel attacks Moderate: issues with large signature databases Low latency for known threats High under normal conditions, but resource-heavy with large DB Low adaptability to new, evolving threats

Anomaly- Based Detection Moderate: higher false positives/negatives Moderate: issues with large and complex environments Moderate: processing required to check baselines Moderate to low: needs continual updates to baselines High adaptability to novel or zero-day attacks

Machine Learning-Based Detection High: excellent at detecting known and novel threats High: scalable for large datasets and traffic Moderate to high: some delay due to model complexity Moderate: training resource-intensive but runtime efficient Very high: adapts continuously with new data

Behavioral Analysis Moderate: effective for insider threats, inconsistent behavior Low to moderate: relies on constant profiling Moderate: needs time for assessing user patterns Moderate: continuous profiling can be resource-heavy High: adapts based on observed user behavior

AI- Enhanced Systems Very high: detects complex and novel attacks Very high: highly scalable with advanced architectures Moderate: some latency due to complex processing Moderate to low: computationally intensive Very high: improves as more data and experiences are analyzed

Summary of Benchmarking Insights.

Signature-Based Detection: Best suited for known attacks but lacks scalability and adaptability to novel threats.

Anomaly-Based Detection: Offers higher flexibility for evolving attacks but faces challenges in accuracy and efficiency.

Machine Learning-Based Detection: Balances scalability and accuracy but incurs latency and resource costs.

Behavioral Analysis: Effective in detecting insider threats, but struggles with scalability and speed as network size grows.

AI-Enhanced Systems: Provide the most advanced detection capabilities and scalability, with strong adaptability, but are computationally expensive.

Challenges in Modern Intrusion Detection.

Evolving Threat Landscape and its Implications: As cyber threats become increasingly sophisticated, traditional detection methods struggle to keep pace. Emerging attack techniques, like polymorphic malware or zero-day exploits, bypass conventional systems.

Balancing Trade-offs in Detection, Efficiency, and Resources: IDS needs to strike a balance between detection accuracy and operational efficiency. Higher detection accuracy may result in increased false positives or require more processing power, potentially compromising system performance [5].

Ethical Concerns Around Data Collection and Privacy: IDS systems often require continuous monitoring of user behavior and network traffic, raising privacy concerns. Ethical issues arise regarding the extent of surveillance and potential misuse of sensitive personal or corporate data.

Theoretical Insights and Future Directions.

Exploration of Potential Hybrid IDS Systems: Combining signature-based, anomaly-based, and machine learning-based approaches into hybrid systems may enhance detection rates and reduce vulnerabilities inherent in each individual model. This could involve integrating multiple techniques to leverage their strengths and

mitigate their weaknesses, potentially leading to more adaptive and accurate threat detection [7].

Discussing Transparency and Interpretability in AI-driven Systems: AI-

powered IDS often suffer from the "black-box" problem, where the decision-making process isn't easily understandable by users or administrators [8]. Future systems will need to prioritize interpretability, allowing security teams to trust and validate decisions made by AI, especially in critical security contexts. This will build confidence in automated systems' recommendations.

Addressing Practical and Ethical Constraints for Future Advancements: As IDS technology becomes more complex, we face not only technical challenges— like high computational demands and the need for continuous updates—but also ethical considerations around data ownership and consent. Future IDS models must operate within ethical boundaries, addressing transparency, user consent, and potential bias, while being practical and effective enough to withstand advanced threats.

Conclusion.

In conclusion, modern IDS solutions are integral to detecting and mitigating sophisticated cyber threats. The continuous evolution of threat landscapes calls for systems that are both adaptive and scalable. The future of intrusion detection lies in hybrid systems that combine diverse approaches and incorporate AI with explainability. Moreover, as technology advances, security professionals must balance effectiveness with ethical and privacy concerns, ensuring that new detection methods support both security and trust.

СПИСОК ЛИТЕРАТУРЫ:

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2. Dangwal, G., Wazid, M., Nizam, S., Chamola, V., & Das, A. K. (2024). Automotive Cybersecurity Scheme for intrusion detection in CAN-Driven Artificial Intelligence of Things. Security and Privacy, 8(1). https://doi.org/10.1002/spy2.483;

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7. Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. I.E.E.E. Access, 7, 41525-41550. https://doi.org/10.1109/access.2019.2895334;

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