Analysis of security and energy efficiency for shortest route discovery in low‐energy adaptive clustering hierarchy protocol using Levenberg‐Marquardt neural network and gated recurrent unit for intrusion detection system

Mittal, Mohit; Iwendi, Celestine; Khan, Suleman; Javed, Abdul Rehman

doi:10.1002/ett.3997

Cited by 88 publications

(55 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RNN normally can handle limited length sequences and will suffer from short‐term memory if the sequence length is long 87 . Different RNN variants like Long short‐term memory (LSTM) 88 and gated recurrent unit (GRU) 89,90 are proposed to solve these issues.…”

Section: Ai Methods For Nidsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

639

215

View full text Add to dashboard Cite

The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

show abstract

Section: Ai Methods For Nidsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

639

215

View full text Add to dashboard Cite

show abstract

“…Machine learning is one of the popular fields of artificial intelligence that is successfully used in solving various computational problems of different areas [56,57]. Now a days it is extended to more deep networks such as deep learning [58], extreme learning [59], deep extreme learning networks etc.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Dixit

Kohli

Acevedo-Duque

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

show abstract

“…As can be seen from Figure 6, r(d) can embody the characteristics of LDoS attack. erefore, attacks in TCP background traffic can be detected by extracting LDoS attack features (such as T and L) from the r(d) sequence [33][34][35].…”

Section: Detection Methods Summarymentioning

confidence: 99%

Coherent Detection of Synchronous Low-Rate DoS Attacks

Yin

2021

Security and Communication Networks

View full text Add to dashboard Cite

Low-rate denial-of-service (LDoS) attacks are characterized by low average rate and periodicity. Under certain conditions, the high concealment of LDoS attacks enables them to transfer the attack stream to the network without being detected at all before the end. In this article, plenty of LDoS attack traffic is spread to the victim end to detect LDoS attacks. Through experimental analysis, it is found that the attack pulses at the victim end have sequence correlation, so the coherence detection technology in spread spectrum communication is proposed to detect LDoS attacks. Therefore, this paper proposes an attack detection method based on coherent detection, which adopts bivariate cyclic convolution algorithm. Similar to the generation of receiving terminal phase dry detection code in spread spectrum communication, we construct a local detection sequence to complete the extraction of LDoS attack stream from the background traffic of the victim terminal, that is, the coherent detection of LDoS attacks. When predicting the features of an LDoS attack, how to construct the parameters of the detection sequence (such as period, pulse duration, amplitude, and so on) is very important. In this paper, we observe the correlation of LDoS attacks and use coherence detection to detect LDoS attacks. By comparing calculated cross-correlation values with designed double threshold rules, the existence of attacks can be determined. The simulation platform and experiments show that this method has high detection performance.

show abstract

Analysis of security and energy efficiency for shortest route discovery in low‐energy adaptive clustering hierarchy protocol using Levenberg‐Marquardt neural network and gated recurrent unit for intrusion detection system

Cited by 88 publications

References 59 publications

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Coherent Detection of Synchronous Low-Rate DoS Attacks

Contact Info

Product

Resources

About