Intrusion Detection System Using Hybrid Convolutional Neural Network

Samha, Amani K.; Malik, Nidhi; Sharma, Deepak Kumar; Kavitha, S.; Dutta, Papiya

doi:10.1007/s11036-023-02223-6

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Positive outcomes from validating the NSL-KDD Test+ dataset demonstrate that the MultiTree algorithm attains 84.2% accuracy and 85.2% adaptive voting. Compared to earlier studies, the ensemble model increases detection accuracy [18,19]. This illustrates how detection results are impacted by data quality.…”

Section: Literature Reviewmentioning

confidence: 65%

“…Binary features (7,12,14,20,21,22) describe attributes with two states, while categorical features (2, 3, 4, 42) reflect qualitative variables with distinct categories. Discrete features (8,9,15,(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)43) are unique numeric variables, but continuous features (1,5,6,10,11,13,16,17,18,19) can take any real value within a range. The dataset's 'attack' label has 40 labels, categorizing attacks as revised, U2R, DoS, R2L, and probing.…”

Section: Datasetmentioning

confidence: 99%

“…In contrast, NIDS is specifically designed to oversee and manage an entire network, offering extensive understanding of all the data traversing the network [16,17]. The decision-making process in NIDS is based on both packet information and contents, which allows for a more extensive perspective that facilitates the detection of widespread threats [18,19]. However, NIDS cannot monitor the internal processes of the individual endpoints it protects.…”

Section: Introductionmentioning

confidence: 99%

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CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

Alrayes,

Zakariah,

Amin

et al. 2024

CMC

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Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highlyadaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label's "attack" and "level." It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy. Furthermore, maintaining operating efficiency while improving detection accuracy is the primary goal of this work. Moreover, typical NIDS examines both risky and typical behavior using a variety of techniques. On the NSL-KDD dataset, our CNN-based approach achieves an astounding 99.728% accuracy rate when paired with channel attention. Compared to previous approaches such as ensemble learning, CNN, RBM (Boltzmann machine), ANN, hybrid auto-encoders with CNN, MCNN, and ANN, and adaptive algorithms, our solution significantly improves intrusion detection performance. Moreover, the results highlight the effectiveness of our suggested method in improving intrusion detection precision, signifying a noteworthy advancement in this field. Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

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Section: Literature Reviewmentioning

confidence: 65%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

Alrayes,

Zakariah,

Amin

et al. 2024

CMC

View full text Add to dashboard Cite

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“…This is the primary reason for the rapid increase in cyberattacks against IoT devices [7]. Proactive network security defenses are required to protect essential assets and data because IoT attack vectors have the potential to result in successful security breaches [8].…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Chalichalamala,

Govindan,

Kasarapu

2023

Sensors

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The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM.

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