Artificial Immune Systems and Fuzzy Logic to Detect Flooding Attacks in Software-Defined Networks

Scaranti, Gustavo F.; Carvalho, Luiz F.; Barbon, Sylvio; Proença, Mário Lemes

doi:10.1109/access.2020.2997939

Cited by 31 publications

(20 citation statements)

References 57 publications

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“…AIS-IDS method [31] Fuzzy logic Detect and reduce the flooding attacks more effectively Need to implement the method in real time environment and prevention method is not available.…”

Section: Discussionmentioning

confidence: 99%

FBDR-Fuzzy based DDoS attack Detection and Recovery mechanism for wireless sensor networks

Pajila¹,

Julie

Robinson

2021

Preprint

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Wireless sensor networks (WSN) is considering as one of the exploring technology. WSN has a large number of sensor nodes, which sense the environment and collect the data. The collected data are sending to the sink through the intermediate nodes. Since the sensors node data are exposed to the internet, there is a possibility of vulnerability in the WSN. The common attack that affects most of the sensor nodes is the DDoS attack. In this paper aims to identify the DDoS attack quickly and to recover sensors using the fuzzy logic mechanism. In the Fuzzy based DDoS attack Detection and Recovery mechanism (FBDR) method uses type 1 fuzzy-logic to detect the occurrence of DDoS attack in a node. Similarly fuzzy- type 2 is used for recovery DDoS attack. Both the type 1 fuzzy-based rule and type 2 fuzzy-based rule perform well in terms of identifying the DDoS attack and recover the DDoS attack. It also helps to reduce the energy consumption of each node and improves the lifetime of the network. The proposed FBDR scheme is compared with other related schemes. The experimental results represent that the FBDR method works better than other similar schemes.

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“…AIS-IDS method [31] Fuzzy logic Detect and reduce the flooding attacks more effectively Need to implement the method in real time environment and prevention method is not available.…”

Section: Discussionmentioning

confidence: 99%

FBDR-Fuzzy based DDoS attack Detection and Recovery mechanism for wireless sensor networks

Pajila¹,

Julie

Robinson

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As the human immune system can detect and react to antigens in our body, the AIS can determine and respond to malicious files that are different to the system files used in the training phase [43]. AIS can detect discrepancies in the system behavior and identify attacks without prior knowledge about them, which makes them ideal candidates for detecting unknown malware files.…”

Section: Artificial Immune Systems Methodsmentioning

confidence: 99%

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Alrubayyi

Goteng

Jaber

et al. 2021

JSAN

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The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

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“…They conclude that both schemes provide approximately the same detection accuracy. Scaranti et al 83 inspired by the human body's defense cells and introduced the Artificial Immune System based IDS, the so‐called AIS‐IDS for the detection of network intrusions. To effectively detect the intrusions when there is no boundary between normal and intrusions traffic, the proposed system uses the fuzzy logic theory.…”

Section: Anomaly‐based Intrusion Detection Techniquesmentioning

confidence: 99%

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

Hajj

Sibai

Abdo

et al. 2021

Trans Emerging Tel Tech

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With the Internet's unprecedented growth and nations' reliance on computer networks, new cyber‐attacks are created every day as means for achieving financial gain, imposing political agendas, and developing cyberwarfare arsenals. Network security is thus acquiring increasing attention among researchers, practitioners, network architects, policy makers, and others. To defend organizations' networks from existing, foreseen, and future threats, intrusion detection systems (IDSs) are becoming a must. Existing surveys on anomaly‐based IDS (AIDS) focus on specific components such as detection mechanisms and lack many others. In contrast to existing surveys, this article covers the full scope needed by researchers and practitioners alike when studying AIDS. The scope ranges from the intrusion detection techniques to attacks forms and passing through the relevant attack features, most‐used datasets, challenges, and potential solutions. This article provides an exhaustive review of IDSs and discusses their requirements and performance metrics in deep. It presents a taxonomy of IDSs based on four criteria: information source, detection strategy, detection mode, and architecture. Then, in‐depth analysis and a comparison of network intrusion detection approaches based on anomaly detection techniques are given. The article also introduces a classification of computer network attacks, along with their different forms and the relevant network traffic features to detect them, as well as a summary of the popular datasets used by the researchers to evaluate the IDSs. Finally, the article highlights several research challenges and the possible solutions to deal with them.

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Artificial Immune Systems and Fuzzy Logic to Detect Flooding Attacks in Software-Defined Networks

Cited by 31 publications

References 57 publications

FBDR-Fuzzy based DDoS attack Detection and Recovery mechanism for wireless sensor networks

FBDR-Fuzzy based DDoS attack Detection and Recovery mechanism for wireless sensor networks

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

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