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Detection of ICMPv6-based DDoS attacks using anomaly based intrusion detection system: A comprehensive review
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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Proposed using feature selection technique based on bio-inspired algorithms for selecting an optimal solution which selects subset to have a positive impact of the detection accuracy ICMPv6 DDoS attack ...
The review outlines the features and protection constraints of IPv6 intrusion detection systems focusing mainly on DDoS attacks. ...
The funding for this research was provided by Universiti Sains Malaysia (USM) and Iraq Airways Company (IA). ...
doi:10.11591/ijece.v11i6.pp5216-5228
fatcat:swfydpt27vfe5oc3wuqqiseck4
A Novel Intrusion Detection System Based on Neural Networks
2019
MATEC Web of Conferences
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This paper proposes a novel intrusion detection system (IDS) based on Artificial Neural Networks (ANNs). The system is still under development. ...
Two types of attacks have been tested so far: DDoS and PortScan. ...
These issues are open research challenges in the field of anomaly-based IDS. Anomaly detection techniques with high accuracy, less false alarms and lower detection time are required. ...
doi:10.1051/matecconf/201929203017
fatcat:zbw7n2ajifeyhkhyh34fu5qq34
DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison
2023
Bulletin of Electrical Engineering and Informatics
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For the purpose of identifying and analyzing DDoS attacks, this paper will discuss various machine learning (ML) and deep learning (DL) techniques. ...
The security of the internet is seriously threatened by a distributed denial of service (DDoS) attacks. ...
The main motivation of this paper is to investigate the most often used machine learning (ML) and deep learning (DL) techniques for intrusion detection system (IDS), as well as to discuss when it is appropriate ...
doi:10.11591/eei.v12i2.4466
fatcat:62bvomw4xra5ngjqta4ctwaoxq
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review
2021
IEEE Access
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Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep ...
learning techniques for data pre-processing and malware detection has been discussed. ...
DDoS attacks discussion, 3. Intrusion Detection System, 4. Database discussion for IDS, 5. Machine Learning Techniques for IDS, 6. ...
doi:10.1109/access.2021.3073408
fatcat:ebzvtidh2relplv3kn3t6plygu
A Survey on Cloud Attack Detection using Machine Learning Techniques
2020
International Journal of Computer Applications
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This work also reviews the security solutions developed by machine learning and deep learning techniques for the cloud environment. ...
Despite the fact, the existing cloud security research still faces the shortcomings in improving the detection accuracy and detecting the new or unknown attacks in the cloud. ...
Deep learning-based DDoS attack detection framework [47] use a pattern matching mechanism for discovering the anomalous behavior. ...
doi:10.5120/ijca2020920887
fatcat:5kk66mrxcffhnajfovglapfasu
Deep Learning Algorithms Used in Intrusion Detection Systems – A Review
[article]
2024
arXiv
pre-print
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The increase in network attacks has necessitated the development of robust and efficient intrusion detection systems (IDS) capable of identifying malicious activities in real-time. ...
Additionally, this paper highlights prominent datasets and benchmarking frameworks commonly utilized for evaluating the performance of deep learning-based IDS. ...
IDS techniques fall into two categories: Network Intrusion Detection Systems based on signatures and Anomaly Detection Systems based on anomaly detection. ...
arXiv:2402.17020v1
fatcat:lo7hdziqfjhq3cepe6ofrp47lm
Detection and Mitigation of Known and Unknown DDoS Attacks on Advanced Metering Infrastructure Systems in the Nigeria Using Hybrid Machine Learning (Ai) Techniques
2022
Network and Complex Systems
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This paper proposed a DDoS detection model based on hybrid machine learning technique on AMI systems in the Nigeria Utility Business. ...
Detecting DDoS attacks is difficult and complicated, primarily different DDoS attacks do not common characteristics through which they can be detected. ...
The author further described that DDoSNet is a deep learning-based intrusion detection system that combines the Recurrent Neural Network (RNN) with an autoencoder. ...
doi:10.7176/ncs/13-04
fatcat:ui3ktvcrjnbtdpwb37sz5tg5cu
Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction
2022
Sensors
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Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network's integrity, availability, and confidentiality. ...
Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. ...
system [144] DDoS Attack Detection OpenFlow Controller (NOX) Used deep auto-encoder approach for feature reduction For vast networks, there is a controller bottleneck [146] Intrusion Detection OpenFlow ...
doi:10.3390/s22207896
fatcat:stzssjxlorhmtnpk4j4wv56wle
Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends
2024
Sensors
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It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. ...
AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection ...
A new system was proposed by the study in [83] on deep transfer learning-based intrusion detection system architecture to address the shortcomings of traditional network intrusion detection techniques ...
doi:10.3390/s24061968
pmid:38544231
pmcid:PMC10976162
fatcat:n3sk7nt5vrgxnkj72jfhwhll5i
Vulnerability Analysis, Intrusion Detection and Privacy Preservation of Modern Communication Systems
2017
EAI Endorsed Transactions on Security and Safety
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In particular, in the area of novel security and privacy methods the issue presents (i) a deep learning based DDoS detection system for multi-vector attack detection in an SDN environment, (ii) an adaptive ...
In article A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) by Quamar Niyaz, Weiqing Sun and Ahmad Y. ...
doi:10.4108/eai.28-12-2017.153514
fatcat:dcxhb2fb6zhmzf2jswvtblaxva
ARLIF-IDS – Attention augmented Real-Time Isolation Forest Intrusion Detection System
[article]
2022
arXiv
pre-print
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Emerging technologies such as the Internet of Things and Software Defined Networking leverage lightweight strategies for the early detection of DDoS attacks. ...
In this work, a novel Attention-based Isolation Forest Intrusion Detection System is proposed. The model considerably reduces training time and memory consumption of the generated model. ...
Many attacks occur each day and pose security challenges for client-networks. Therefore, the existence of accurate intrusion detection systems becomes indispensable. ...
arXiv:2204.09737v1
fatcat:jbhkvuqllzd6vh7saj77s5kiaq
Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review
2021
Applied Sciences
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Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. ...
In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT ...
detection" AND "Deep learning" "Anomaly intrusion detection system" AND "Deep learning" 6 2 2 71 "Anomaly-based" AND "Internet of things" 36 "Anomaly-based" AND "Deep learning" 22 "Anomaly intrusion detection ...
doi:10.3390/app11188383
fatcat:qwti7rwd2nfvvaa4c2jjh6gaom
Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey
2018
International Journal of Advanced Technology and Engineering Exploration
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The different deep learning algorithm based cyber-attack detection schemes have been designed to detect and mitigate the different types of cyber-attacks through CPSs, smart grids, power systems, etc. ...
This article presents a detailed survey of various deep learning algorithms proposed for CPSs to achieve cyber defense. ...
According to this analysis, DNN with the transfer-entropy measure based anomaly detection in CPS has better performance than all other cyber-attack detection systems. ...
doi:10.19101/ijatee.2018.547030
fatcat:ivfb6skvyrcdve6mo5qtrajdty
A Review of Anomaly Detection Techniques and Distributed Denial of Service (DDoS) on Software Defined Network (SDN)
2018
Engineering, Technology & Applied Science Research
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This research explains DDoS attacks and the anomaly detection as one of the famous detection techniques for intelligent networks. ...
One of the famous attacks is distributed denial of service (DDoS). ...
Authors in [37] proposed a deep learning based multi-vector DDoS detection system in (SDN) environment. ...
doi:10.48084/etasr.1840
fatcat:6fkigapikrcdxcu5bo4yzue6nq
Effective Attack Detection in Internet of Medical Things Smart Environment using a Deep Belief Neural Network
2020
IEEE Access
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In this work, we have proposed a deep learning-based method Deep Belief Network (DBN) algorithm model for the intrusion detection system. ...
INDEX TERMS IoT, deep learning, anomaly detection, intrusion detection, DBN. ...
Within these techniques, intrusion detection which is dependent on deep learning performs better than various other techniques, because deep learning has a high capacity for self-learning, self-adaption ...
doi:10.1109/access.2020.2986013
fatcat:e4svv3od3jfsjavrowmqgvsaii
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