A bio-inspired hybrid deep learning model for network intrusion detection

Moizuddin, Md.; Jose, M. Victor

doi:10.1016/j.knosys.2021.107894

Cited by 33 publications

(16 citation statements)

References 40 publications

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“…In this experiment, the main attack detection approaches that are compared are hyperparameter optimization (random search (RS) [ 8 ]; annealing; the tree Parzen estimator (TPE)); machine learning (KNN and random forest (RF)) plus feature selection (information gain-based feature selection (IGFS) and correlation-based feature selection (CFS)) with RS-KNN-CFS, Anneal-KNN-CFS, TPE-KNN-CFS, RS-KNN-IGFS, Anneal-KNN-IGFS, TPE-KNN-IGFS, RS-RF-CFS, Anneal-RF-CFS, TPE-RF-CFS, RS-RF-IGFS, Anneal-RF-IGFS, TPE-RF-IGFS [ 9 , 49 , 50 ] and GMGWO-ECAE-machine learning [ 14 ]; BRA [ 15 ]; and the ADDC proposed in this article. The specific performance indicators of each attack detection approach are analyzed in Sections 5.3.1 to 5.3.3.…”

Section: Analysis Of the Experimental Resultsmentioning

confidence: 99%

“…Through comparative analysis of experiments, we found that previous detection methods generally performed poorly in terms of these two indices. For example, the latest method proposed this year is the generalized mean gray wolf optimization algorithm (GMGWO) [ 14 ]. Its overall detection precision is good on the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset.…”

Section: Introductionmentioning

confidence: 99%

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Towards Intelligent Attack Detection Using DNA Computing

Zeng

Zhao

Meng

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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In recent years, frequent network attacks have seriously threatened the interests and security of mankind. To address this threat, many detection methods have been studied, and some of them have achieved good results. However, with the development of network interconnection technology, massive network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulties in the collection of samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of the existing detection methods, but the existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of the detection method in terms of overall accuracy, feature subset optimization and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

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Section: Analysis Of the Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Intelligent Attack Detection Using DNA Computing

Zeng

Zhao

Meng

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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“…Moizuddin and Victor [24] presented a bioinspired HDL (hybrid DL) model for identifying network intrusion detection. This IDS model was framed as a two-stage model which includes feature selection with a bio-inspired algorithm and DL based attack classification.…”

Section: Related Workmentioning

confidence: 99%

“…The performance metrics such as accuracy, precision, recall, and f1-score are evaluated for the proposed method with some existing methods. The proposed method is compared with different existing baselines such as HDL [24], HCRN [25], DCNN [26], KNN (K-nearest neighbour) [28], multi-scale convolutional neural network (MSCNN) [29], MLP [30], LSTM [31], Conv-LSTM (convolution longshort term memory) [32], and DNN (deep neural network) [33]. Fig.…”

Section: Performance Analysis On the Unsw-nb15 Datasetmentioning

confidence: 99%

OptiBiNet_GRU: Robust Network Intrusion Detection System Using Optimum Bi-Directional Gated Recurrent Unit

2023

IJIES

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Security is considered one of the major challenges in the network due to the increase in network services. Many systems linked to the network play a major role in business and other applications that provide network services. Therefore, it is necessary to identify effective ways to secure the system. One of the significant information security technologies is the intrusion detection system (IDS) that utilizes various deep learning (DL) and machine learning (ML) algorithms to identify network issues. As a result, a novel DL technique is used to classify and detect attacks in the network. To perform this approach, pre-processing is initially done for the collected data using Z-score normalization (ZsN). Then, feature selection is performed using the hybrid pattern search whale optimization algorithm (HPS-WOA). Followed by, network attacks are detected and classified using the optimum bi-directional long shortterm memory with the gated recurrent unit (OptiBiNet_GRU). Finally, the hyper parameters such as loss and weight of the proposed DL model are optimized using the pelican optimization algorithm (POA). The effectiveness of the proposed method is validated using two datasets on various performance metrics with some existing baselines. The results show that the proposed method outperforms other methods in terms of accuracy of 99%, precision of 95%, f1score of 97%, specificity of 99%, and recall of 98% for UNSW-NB15 dataset, whereas accuracy of 99%, precision of 95%, f1-score by 99%, specificity of 99%, and recall of 99% for CICIDS 2017 dataset, respectively.

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“…In the literature, majority of the researchers have worked on binary classification of intrusion traffic ( Gu & Lu, 2021 ; Zhao et al, 2021 ; Rashid et al, 2022 ; Dora & Lakshmi, 2022 ) while some also worked on multi-class classification using deep learning techniques ( Chen, Fu & Zheng, 2022 ; Zhang et al, 2022 ; Abdel-Basset et al, 2021 ; Moizuddin & Jose, 2022 ). However, deep learning requires computational resources and time to train the models ( Gu & Lu, 2021 ).…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Abbas,

Hina,

Sajjad

et al. 2023

PeerJ Computer Science

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Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.

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A bio-inspired hybrid deep learning model for network intrusion detection

Cited by 33 publications

References 40 publications

Towards Intelligent Attack Detection Using DNA Computing

Towards Intelligent Attack Detection Using DNA Computing

OptiBiNet_GRU: Robust Network Intrusion Detection System Using Optimum Bi-Directional Gated Recurrent Unit

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

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