A Detection of Intrusions Based on Deep Learning

Kamalakkannan, D.; Menaga, D.; Shobana, S.; Sagar, K. V. Daya; Rajagopal, R.; Tiwari, Mohit

doi:10.1080/01969722.2023.2175134

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Table 11 shows the comparison of binary and multiclass classification using the different datasets. Also, the XGBoost-DT got a test accuracy score of 90.85% compared to 81.42% for the GA-LR-DT with 20 UNSW-NB15 features, which is different from the outcomes in [17], where the GA-LR-DT was used. Moreover, the results obtained from this research outperform those obtained from previous research in which the sigmoid PIO selected 14 optimal features from the UNSW-NB15 and utilized the validation dataset to attain an accuracy score of 91.30%.…”

Section: Discussionmentioning

confidence: 62%

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Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

2024

IJATEE

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Hackers have evolved at a faster rate in terms of their capabilities because of precise technological developments like networks and communication systems. These crooks are constantly looking for new ways to jeopardize the security of computer networks (CN). Intrusion detection systems (IDS) consequently automatically become important parts of a CN. An IDS is a piece of software or hardware that tracks a company's network for potential threats. Meanwhile, an IDS is capable of reacting to and reporting any fraudulent activity. Node-based or host-based IDS (HIDS) [1, 2] Network based IDS (NIDS) or distributed-based IDS (DIDS), and hybrid-based IDS (HYIDS) are the three basic categories into which IDSs fall based on the unique IDS operation concept, this classification was created. *Author for correspondenceThe primary fundamental design goals of IDS are a decrease in false positive (FP) alerts and an increase in detection accuracy. Subsequently, when designing and implementing any IDS, that perspective must be taken into consideration [3]. In currently decades, machine learning (ML)-based IDS have taken over as the industry standard. According to ML systems may now potentially learn from the past and improve. Broadly speaking, the two ML philosophies of supervised ML and unsupervised ML can be separated. Models are learned using labeled data in supervised ML [4]. Unsupervised ML uses unstructured data to train models.The multiclass and binary classification objectives are carefully taken into consideration when using supervised ML techniques in this study. The classification process occurs whenever a supervised ML technique is asked to identify a specific quality. The training data for the techniques in this configuration frequently has large sizes and high-

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Section: Discussionmentioning

confidence: 62%

“…The experimental approaches make use of the NSL-KDD dataset. The set of features was given the filter, and 14 features were chosen [16,17]. The author also considered both the multi and binary classification setup options, which included each of the five NSL-KDD attack types.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

2024

IJATEE

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“…Using the highly reputable Bot-IoT dataset, the proposed model surpassed the highest accuracy, with a 99.8% ratio. Kamalakkannan et al [45] developed an improved CNN + LSTM model that learns spatial and temporal data characteristics, demonstrating 98% accuracy and a 98.14% average detection rate on the NSL-KDD dataset. www.ijacsa.thesai.org Shivhare et al [46] proposed a CNN + LSTM + SVM model to tackle multiclass tasks on the CICIDS 2017 dataset, achieving an accuracy of 97.29%.…”

Section: Hybrid Solutionsmentioning

confidence: 99%

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Chen,

You,

Shiue

2024

IJACSA

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The growth of the internet has advanced information-sharing capabilities and vastly increased the importance of global network security. However, because new and inconspicuous abnormal behaviors are nearly impossible to detect in massive network access environments, modern intrusion detection systems have identified a high rate of false-positive (FP) and false-negative (FN) attacks. To overcome this, this paper proposes a hybrid deep learning model that significantly mitigates the disadvantages of consistently imbalanced sample attack data. First, it resolves imbalanced data using random undersampling and synthetic minority oversampling techniques. Then, convolutional neural networks (CNNs) extract local and spatial features, and a transformer encoder extracts global and temporal features. The novelty of this combination increases recognition accuracy at the algorithm level, which is crucial to reducing FPs and FNs. The model was subjected to multiclassification testing on the NSL-KDD and CICIDS2017 benchmark datasets, and the results show that our model has higher classification accuracy and lower FP rates than state-ofthe-art intrusion detection models. Moreover, it significantly improves the detection rate of low-frequency attacks.

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A Detection of Intrusions Based on Deep Learning

Cited by 2 publications

References 18 publications

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

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