Intrusion Detection and Attack Classification Leveraging Machine Learning Technique

Shaukat, Shahtaj; Ali, Asjad; Batool, Amreen; Alqahtani, Fehaid; Khan, Jan Sher; Ali, Arshad; Ahmad, Jawad

doi:10.1109/iit50501.2020.9299093

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…A comparative evaluation of the proposed approach was carried out with intrusion detection systems (IDS) and intrusion prevention systems (IPS), which were based on signature [ 50 ], statistical [ 51 ], and machine learning methods [ 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

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A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods

Kotenko

Saenko

Lauta

et al. 2022

Sensors

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The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating its self-similarity, detecting and classifying cyberattacks in anomalies, and taking effective protection measures using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Fractal analysis, mathematical statistics, and neural networks with long short-term memory are used as tools in the development of this protection system. The issues of software implementation of the proposed system and the formation of a data set containing network packets of a smart grid system are considered. The experimental results obtained using the generated data set demonstrated and confirmed the high efficiency of the proposed proactive smart grid protection system in detecting cyberattacks in real or near real-time, as well as in predicting the impact of cyberattacks and developing efficient measures to counter them.

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Section: Discussionmentioning

confidence: 99%

“…Their effectiveness depends on the classification and clustering models they use. In the works [ 52 , 53 ] considered in Table 4 , the SVM, Gaussian Naive Bayes, and Decision Tree models were used. In these methods, it is necessary to train models on control samples.…”

Section: Discussionmentioning

confidence: 99%

A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods

Kotenko

Saenko

Lauta

et al. 2022

Sensors

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show abstract

“…The precision of a model is measured by the number of items that are actually relevant to the model. Accordingly, we can write it as [44]:…”

Section: E Precisionmentioning

confidence: 99%

“…F1-score calculates the combined view of both recall and precision, which makes it a strong metric. Mathematically, it is equivalent to [44], [45]:…”

Section: G F1-scorementioning

confidence: 99%

Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging

Siddiqa,

Tang,

et al. 2024

IEEE Access

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Neonatal sleep staging is crucial for understanding infant brain development and assessing neurological health. This study explores the optimal electrode configuration to reduce technical complexities and potential risks of causing skin irritation to neonates during data collection. A Multi-Branch Convolutional Neural Network (CNN) is used to categorize neonatal sleep states based on single-channel Electroencephalography (EEG) data. The proposed model was trained and tested on 16803 30-second segments from 64 infants, all of whom were at post-menstrual age between 36 and 43 weeks at the Children's hospital of Fudan University. A total of 74 extracted time and frequency domain linear and nonlinear features are applied to improve the performance of a Multi-Branch CNN-based classification model. Additionally, using principal component analysis (PCA), feature selection and feature importance are also applied to identify the most important features. Notably, the F3 channel outperforms other single-channels and has accuracy and kappa values 74.27±0.80% and 0.61, respectively. Furthermore, a combination of four left-side electrodes yields slightly better classification accuracy (75.36±0.57%) compared to the four right-side electrodes (74.76±1.10%), with corresponding kappa values of 0.63 and 0.62, respectively. In addition to providing insights into optimal electrode configuration using single-channel and multi-channel EEG data, the results highlight the critical role played by specific EEG channels in sleep stage classification. This research has the potential to enhance neonate care and monitor sleep more effectively, enabling early detection of sleep-related abnormalities such as sleep disorders. Furthermore, this research effectively captures information from a single-channel, reducing computing load while maintaining commendable performance. Additionally, integrating time and frequency domain linear and non-linear features into neonatal sleep staging can enhance accuracy and provide a deeper insight into the complex dynamics and irregularities of newborn's sleep patterns.

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“…Precision: Precision is defined as the ratio of the total number of correct positive predictive values to the total number of positive predictive values [45]. Additionally, it provides a value that is between 0 and 1.…”

mentioning

confidence: 99%

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Almufareh,

Tariq,

Humayun

et al. 2023

Healthcare

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Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale.

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Intrusion Detection and Attack Classification Leveraging Machine Learning Technique

Cited by 12 publications

References 23 publications

A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods

A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods

Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

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