A generic cyber immune framework for anomaly detection using artificial immune systems

Bejoy, B. J.; Raju, G.; Swain, Debabrata; Acharya, Biswaranjan; Hu, Yu‐Chen

doi:10.1016/j.asoc.2022.109680

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…The majority of previous works on immune-inspired IDSs target computer networks [ 192 , 193 ], IoT devices [ 187 ], or CPSs [ 188 ]. In additions, several works apply immune principles to IDSs applicable to WSNs, such as the negative selection-based IDS for WSNs named WSN-NSA [ 183 ], the multi-level IDS for WSNs [ 73 ], or Co-FAIS [ 135 ], a danger-theory-based IDS that utilizes fuzzified network traffic in WSNs.…”

Section: Literature Review On Immune-inspired Approachesmentioning

confidence: 99%

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Widhalm

Goeschka

Kästner

2023

Sensors

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The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

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Section: Literature Review On Immune-inspired Approachesmentioning

confidence: 99%

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Widhalm

Goeschka

Kästner

2023

Sensors

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“…The detection system called cyber immune system (CIS) designed by Bejoy et al ( 2022 ) was tested on the NSL-KDD dataset, combining the negative selection algorithm with the positive selection algorithm. If the CIS detector identifies a regular traffic pattern, it is removed using negative selection.…”

Section: Metaheuristics Machine Learning and Anomaly Detectionmentioning

confidence: 99%

Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques

Cavallaro,

Cutello,

Pavone

et al. 2023

Front. Big Data

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With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as manual labeling is difficult in practice due to their huge volumes. The results obtained from data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, anomaly detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in the literature to classify unknown anomalies in log analysis, process analysis, and time series have been mainly based on machine learning and deep learning techniques. In this study, we provide an overview of current state-of-the-art methodologies, highlighting their advantages and disadvantages and the new challenges. In particular, we will see that there is no absolute best method, i.e., for any given dataset a different method may achieve the best result. Finally, we describe how the use of metaheuristics within machine learning algorithms makes it possible to have more robust and efficient tools.

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“…This suggests that ongoing learning based on continually updated signatures of self and attack is a key principle of defense. Indeed these and other immune-inspired principles have been translated into many artificial immune systems for intrusion detection [78].…”

Section: Open Questions: What (Combinations Of) Defensive Layers Mini...mentioning

confidence: 99%

Challenges in cybersecurity: Lessons from biological defense systems

Schrom

Kinzig

Forrest

et al. 2023

Mathematical Biosciences

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A generic cyber immune framework for anomaly detection using artificial immune systems

Cited by 9 publications

References 29 publications

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques

Challenges in cybersecurity: Lessons from biological defense systems

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