A Novel Intrusion Detection System Based on Neural Networks

Andreatos, Antonios; Moussas, Vassilios C.

doi:10.1051/matecconf/201929203017

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The combination of the CICIDS2017 dataset and a store-and-forward dataset extracted from an experimental network can provide various advantages and mitigate specific constraints when investigating attacks associated with the manipulation of "Flow Duration" and "Forward Packets". In particular, the focus would be on acknowledging the CICIDS2017 dataset as a publicly accessible benchmark dataset that holds significant prominence in the realm of network intrusion detection research [17]. A limited number of scenarios or features can be derived from the dataset as it encompasses a diverse range of attack scenarios and instances of network traffic, as long as the aforementioned features are linked to the store-and-forward dataset derived from this research experimental network.…”

Section: Discussionmentioning

confidence: 99%

“…The intrusion detection system (IDS) is the primary area of research for detecting attacks. Machine learning approaches are predominantly utilized for achieving successful detection [17].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the integration of rule-based and machine learning models in hybrid techniques shows potential in attaining a wellrounded detection capability. It is crucial to note that the aforementioned studies [16][17][18][19][20][21][22][23][24][25][26][27][28][29] continue to prioritize the study of intrusion detection systems (IDSs). However, there has been limited discussion regarding the practical implementation of at-a-point detection in various applications.…”

Section: Related Workmentioning

confidence: 99%

“…The CICIDS2017 dataset (see Figure 1) is extensively employed in the field of cybersecurity for the purposes of intrusion detection and network traffic analysis research [17]. The creation and release of the aforementioned project occurred in 2017 under the auspices of the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick, located in Canada [18].…”

Section: Cicids2017 Dataset Generationmentioning

confidence: 99%

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Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Munshi

2023

Applied Sciences

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The detection technique for IP packet header modifications associated with store-and-forward operation pertains to a methodology or mechanism utilized for the identification and detection of alterations made to packet headers within a network setting that utilizes a store-and-forward operation. The problem that led to employing this technique lies with the fact that previous research studies expected intrusion detection systems (IDSs) to perform everything associated with inspecting the entire network transmission session for detecting any modification. However, in the store-and-forward process, upon arrival at a network node such as a router or switch, a packet is temporarily stored prior to being transmitted to its intended destination. Throughout the duration of storage, IDS operation tasks would not be able to store that packet; however, it is possible that certain adjustments or modifications could be implemented to the packet headers that IDS does not recognize. For this reason, this current research uses a combination of a convolutional neural network and long short-term memory to predict the detection of any modifications associated with the store-and-forward process. The combination of CNN and LSTM suggests a significant improvement in the model’s performance with an increase in the number of packets within each flow: on average, 99% detection performance was achieved. This implies that when comprehending the ideal pattern, the model exhibits accurate predictions for modifications in cases where the transmission abruptly increases. This study has made a significant contribution to the identification of IP packet header modifications that are linked to the store-and-forward operation.

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Section: Discussionmentioning

confidence: 99%

“…The intrusion detection system (IDS) is the primary area of research for detecting attacks. Machine learning approaches are predominantly utilized for achieving successful detection [17].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Cicids2017 Dataset Generationmentioning

confidence: 99%

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Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Munshi

2023

Applied Sciences

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“…Additional tuning on the proposed model is required for improving performance. In the same way, the author in [45] proposes the intrusion detection model which incorporates the neural network. The proposed system is implemented in MATLAB.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Malware Attacks using Artiﬁcial Neural Network

Rana,

Minhaj Ahmad Khan

2023

VAWKUM trans. comput. sci.

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Malware attacks are increasing rapidly as the technology continues to become prevalent. These attacks have become extremely difficult to detect as they continuously change their mechanism for exploitation of vulnerabilities in software. The conventional approaches to malware detection become ineffective due to a large number of varying patterns and sequences, thereby requiring artificial intelligence-based approaches for the detection of malware attacks. In this paper, we propose an artificial neural network-based model for malware detection. Our proposed model is generic as it can be applied to multiple datasets. We have compared our model with different machine-learning approaches. The experimentation results show that the proposed model can outperform other well-known approach as it achieves 99.6\% , 98.9\% and 99.9\% accuracy on the Windows API call dataset, Top PE Imports Dataset and Malware Dataset, respectively.

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