An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Bakar, Rana Abu; Huang, Xin; Javed, Muhammad Saqib; Hussain, Shafiq; Majeed, Muhammad Faran

doi:10.3390/s23063333

Cited by 19 publications

(5 citation statements)

References 35 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An AI detection model based on logistic regression (LR) and NB has been proposed as a method for detecting attacks as well as normal scenarios [47], [48]. The authors in [49] presented an intelligent agent system that incorporates the K-nearest neighbors (KNN) algorithm to detect distributed denial-of-service (DDoS) attacks. The system uses automatic feature extraction and selection techniques.…”

Section: Ai-based Detection Roadmap and Threat Model Analysismentioning

confidence: 99%

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abdullahi,

Alhussian,

Aziz

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: Ai-based Detection Roadmap and Threat Model Analysismentioning

confidence: 99%

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abdullahi,

Alhussian,

Aziz

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Results from simulations and experiments show that our suggested method obtains a high accuracy rate of 99.86%. Malware, advanced persistent threats, and distributed denial of service (DDoS) attacks all actively jeopardize the security and availability of Internet services [35]. In order to detect DDoS attacks, study in [35] suggests an intelligent agent system that uses automatic feature extraction and selection.…”

Section: Conclusion and Future Work Sectionmentioning

confidence: 99%

A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0

Al-Quayed,

Ahmad,

Humayun

2024

IEEE Access

View full text Add to dashboard Cite

Industry 4.0 is fundamentally based on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency. Industry 4.0-based wireless sensor networks (WSNs) are an integral part of modern industrial operations however, these networks face escalating cybersecurity threats. These networks are always vulnerable to cyber-attacks as they continuously collect data and optimize processes. Increased connections make people more susceptible to cyberattacks, necessitating the use of strong cybersecurity measures to protect sensitive data. This study proposes a predictive framework intended to intelligently prioritize and prevent cybersecurity intrusions on WSNs in Industry 4.0. The proposed framework enhances the cybersecurity of WSNs in Industry 4.0 using a multicriteria approach. It implements machine-learning and deep-learning algorithms for cybersecurity intrusion detection in WSNs of Industry 4.0 and provides prevention by assigning priorities to the threats based on the situation and nature of the attacks. We implemented three models, i.e., Decision Tree, MLP, and Autoencoder, as proposed algorithms in the framework. For multidimensional classification and detection of cybersecurity intrusions, we implemented Decision Tree and MLP models. For binary classification and detection of cybersecurity intrusions in WSNs of Industry 4.0, we implemented Autoencoder model. Simulation results show that the Decision Tree model provides an accuracy of 99.48%, precision of 99.49%, recall of 99.48%, and F1 score of 99.49% in the detection and classification of cybersecurity intrusions. The MLP model provides an accuracy of 99.52%, precision of 99.5%, recall of 99.5%, and F1 score of 99.5% in the detection and classification of cybersecurity intrusions. The implementation of Autoencoder with binary classification yields an accuracy of 91%, a precision of 92%, a recall of 91%, and an F1 score of 91%. The benchmark models, i.e., Random Forest (RF) for multidimensional classification and Logistic Regression (LR) for binary classification, have also been implemented. We compared the performance of the benchmark models with the models implemented in the proposed framework, revealing that the models in the proposed framework significantly outperformed the benchmark models. The framework presents an intelligent prioritizing methodology that is significant for effectively identifying and addressing high-risk intrusions. The proposed framework implements a proactive preventive system that functions as a strong defensive wall by quickly putting countermeasures in place to eliminate threats and increase network resilience.

show abstract

“…Furthermore, in 2023, Abu Bakar et al [ 118 ] proposed an intelligent agent-based detection system for DDoS attacks that uses machine learning algorithms to extract features from network traffic and classify normal and attack traffic. The system first pre-processes the network traffic data to remove noise and irrelevant information.…”

Section: Iot Botnet Detectionmentioning

confidence: 99%

“…[117] Collect traffic metrics 2019 [118] Mix with blockchain 2020 [119] Multi-agent system 2023 [118] Intelligent agent-based and ML Proposed in 2019, the agent-based system in [117] involves installing an agent in each IoT installation, such as a smart home, to monitor the network traffic of the devices. The agents are nodes of a complete undirected graph and can communicate with each other in a Peer-to-Peer (P2P) fashion.…”

mentioning

confidence: 99%

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Gelgi,

Guan,

Arunachala

et al. 2024

Sensors

View full text Add to dashboard Cite

Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.

show abstract

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Cited by 19 publications

References 35 publications

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Contact Info

Product

Resources

About