Performance Comparison for Intrusion Detection System Using Neural Network With KDD Dataset

Devaraju, S.; Ramakrishnan, S.

doi:10.21917/ijsc.2014.0106

Cited by 32 publications

(3 citation statements)

References 12 publications

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“…Optionally, the handshake also allows the client to prove his identity on the server. Each Tor client generates a self-signed SSL, using a random algorithmically generated domain that changes every three minutes or so; therefore, a network traffic statistical analysis based on the specifics and characteristics of SSL can identify Tor sessions on a network combined with HTTPS traffic [8,[15][16][17].…”

Section: Literature Reviewsmentioning

confidence: 99%

“…There is an increasing interest in research related to the dark web. A big part of the conducted literature review in cybersecurity was focused on anomaly-based network intrusion detection systems [9,[17][18][19][20][21]. In addition, there is research dedicated to network traffic classification [22][23][24], whereas the Internet of Things (IoT) has recently attracted a significant amount of attention in machine learning and in network traffic analysis [13,15,16,25].…”

Section: Literature Reviewsmentioning

confidence: 99%

“…In the survey work of Kwon et al [19], a review was conducted on various intrusion detection models and methodologies for classification and data volume reduction. Most of these works used the KDD-Cup 1999 dataset [20], or its successor NSL-KDD [6], which resolves some of the inherent issues of the first and has been widely adopted by the research community [17,21]. However, Zhang et al [27] reported inefficiencies in most anomaly-based network intrusion detection systems employing supervised algorithms and suggested an unsupervised outlier detection scheme as a measure to overcome these inefficiencies.…”

Section: Literature Reviewsmentioning

confidence: 99%

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Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

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Section: Literature Reviewsmentioning

confidence: 99%

Section: Literature Reviewsmentioning

confidence: 99%

Section: Literature Reviewsmentioning

confidence: 99%

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Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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IDDLE: A Novel Deep Learning–Based Approach for Intrusion Detection Problem Using Feature Extraction

Göktepe,

Uzun

2024

Security and Privacy

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The internet has revolutionized access to information, enabling users to retrieve vast amounts of data effortlessly, transcending geographical and temporal barriers. This unprecedented connectivity has advanced research, education, and communication, but it has also created vulnerabilities that cybercriminals exploit to steal confidential information, interfere with processes, and carry out extensive attacks. Consequently, robust security measures are essential to safeguard the integrity and confidentiality of online data. Intrusion detection systems (IDS) are crucial in defending against such threats, employing both signature‐based and anomaly‐based models. Even though signature‐based IDS are highly effective at countering recognized threats, they struggle with novel, “zero‐day” attacks. Conversely, anomaly‐based detection systems can identify unknown threats but often generate high false positive rates. A hybrid IDS, combining elements of both approaches, offers a more comprehensive defense. This study presents a new intrusion detection model utilizing deep learning, evaluated on the KDD'99 and NSL‐KDD datasets. The proposed model (IDDLE—intrusion detection deep learning engine) incorporates advanced preprocessing techniques, including normalization, feature extraction, and categorical encoding. Empirical findings demonstrate that the proposed model outperforms existing state‐of‐the‐art approaches. This study underscores the capability of deep learning to enhance it in improving IDS performance, emphasizing the importance of continuous innovation and collaboration among security researchers, developers, and users to stay ahead of evolving cyberthreats. The findings underscore the significance of advanced feature extraction and hybrid detection strategies in developing robust intrusion detection systems.

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WiP: Distributed Intrusion Detection System for TCP/IP-Based Connections in Industrial Environments Using Self-organizing Maps

Kharitonov

Zimmermann

2021

Lecture Notes in Computer Science

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Performance Comparison for Intrusion Detection System Using Neural Network With KDD Dataset

Cited by 32 publications

References 12 publications

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

IDDLE: A Novel Deep Learning–Based Approach for Intrusion Detection Problem Using Feature Extraction

WiP: Distributed Intrusion Detection System for TCP/IP-Based Connections in Industrial Environments Using Self-organizing Maps

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