A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

Alsudani, Mustafa Qahtan; Reflish, Salah H. Abbdal; Moorthy, Kohbalan; Adnan, Myasar Mundher

doi:10.1016/j.matpr.2021.07.015

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…The cuckoo search algorithm improves the LSTM model's classification ability as it identifies the most appropriate features within the wireframe. Alsudani et al [17] state that types of IDSs known as hybrid models are more effective than IDSs derived from a single algorithm, but they also have flaws. To overcome the drawbacks of conventional hybrid IDS models, the present research proposes developing a novel TLBO-ELM hybrid IDS model That integrates the Firefly Algorithm and Fast Learning Network.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Hassan,

Najim,

Al-Sharhanee

et al. 2024

Preprint

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Integrating machine learning (ML) into intrusion detection systems (IDS) is considered an important topic for preventing the spread of cyber threats. However, when it comes to machine learning techniques, IDSs face challenges in accurately identifying various types of attacks within the complex structures of a network. This study addresses the lack of research on combining metaheuristic optimization techniques with unsupervised machine learning algorithms in IDS design. The proposed model uses the cuckoo search metaheuristic and the K-means method to improve IDS precision. Here, the cuckoo search algorithm is used to increase the efficiency of feature selection. Meanwhile, the k-means clustering methodology is used to discretize the data and reduce its dimensionality by using two clusters, C1 and C2. The proposed model, developed carefully, includes data preprocessing (handling missing values), data transformation (label encoding), and data normalization. A stochastic operator assesses the impact of the K-means operator. The model is evaluated using an accessible intrusion dataset and compared with other state-of-the-art models. From the research conclusions, the presented model also demonstrates better results compared to the rest, especially when it reaches accuracy (99. 79%), precision (99. 78%), recall (99. 51%), and the F1-score (99).

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Section: Related Workmentioning

confidence: 99%

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Hassan,

Najim,

Al-Sharhanee

et al. 2024

Preprint

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“…This section provides a literature review of current approaches to cloud intrusion detection and classification [21][22][23]. There is an examination of the pros and cons of each method according to their own operational characteristics, important features, and functional nature.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Ramachandran,

Albathan,

Hussain

et al. 2023

Systems

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Developing a simple and efficient attack detection system for ensuring the security of cloud systems against cyberthreats is a crucial and demanding process in the present time. In traditional work, various machine-learning-based detection methodologies have been developed for securing the cloud network. However, those methodologies face the complications of overfitting, complex system design, difficulty understanding, and higher time consumption. Hence, the proposed work contributes to the design and development of an effective security model for detecting cyberthreats from cloud systems. The proposed framework encompasses the modules of preprocessing and normalization, feature extraction, optimization, and prediction. An improved principal component analysis (IPCA) model is used to extract the relevant features from the normalized dataset. Then, a hybrid grasshopper–crow search optimization (GSCSO) is employed to choose the relevant features for training and testing operations. Finally, an isolated heuristic neural network (IHNN) algorithm is used to predict whether the data flow is normal or intrusive. Popular and publicly available datasets such as NSL-KDD, BoT-IoT, KDD Cup’99, and CICIDS 2017 are used for implementing the detection system. For validation, the different performance indicators, such as detection accuracy (AC) and F1-score, are measured and compared with the proposed GSCSO-IHNN system. On average, the GSCO-IHNN system achieved 99.5% ACC and 0.999 F1 scores on these datasets. The results of the performance study show that the GSCSO-IHNN method outperforms the other security models. Ultimately, this research strives to contribute to the ongoing efforts to fortify the security of cloud systems, making them resilient against cyber threats more simply and efficiently.

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A novel intrusion detection framework for optimizing IoT security

Qaddos,

Yaseen,

Al-Shamayleh

et al. 2024

Sci Rep

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The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model’s applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

Cited by 4 publications

References 20 publications

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

A novel intrusion detection framework for optimizing IoT security

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