A dependable hybrid machine learning model for network intrusion detection

Talukder, Md. Alamin; Hasan, Khondokar Fida; Islam, Md. Manowarul; Uddin, Md. Ashraf; Akhter, Arnisha; Yousuf, Mohammad Abu; Alharbi, Fares; Moni, Mohammad Ali

doi:10.1016/j.jisa.2022.103405

Cited by 79 publications

(42 citation statements)

References 44 publications

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“…The CIC-Malmem-2022 dataset has garnered considerable attention in recent literature. In [25], the authors employ oversampling and XGBoost as preprocessing techniques to combat class imbalance in the dataset, and after evaluating multiple algorithms, their experiments saw that Random Forest and Multilayer Perceptron(MLP) surpass the performance of other models, achieving an accuracy rate of almost 100%, although simply for detection. Although the study is commendable, it lacks the evaluation of individual attack classification.…”

Section: Detecting the Presence Of An Omm Attack (Binary Classification)mentioning

confidence: 99%

Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications

Shafin

Karmakar

Mareels

2023

Sensors

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Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware.

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Section: Detecting the Presence Of An Omm Attack (Binary Classification)mentioning

confidence: 99%

Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications

Shafin

Karmakar

Mareels

2023

Sensors

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“…Random Forest (RF) is enhanced in [23] to address data imbalance issues in IDS. Boosting algorithms, including XGBoost, are utilized in [24] to improve detection rates through hybrid ML and deep learning models. Support Vector Machine (SVM) separates data points with a clear gap is optimized using Genetic Algorithm (GA) in [25], and the Orthogonal Atomic Orbital Search algorithm is applied to optimize the logistic regression model in [26] for ID.…”

Section: Related Workmentioning

confidence: 99%

ANWO-MPL:An Adaptive Nonlinear Multi-layer Perceptron Cyber Intrusion Detection Framework

El-ghaish,

Miqrish,

Elmogy

et al. 2023

Preprint

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The critical need for robust defense against cyber-attacks has driven thedevelopment of Cyber Intrusion Detection Systems (CIDSs). AlthoughCIDSs have proven good capabilities of swiftly and accurately iden-tifying malicious activities, there are still some challenges have to betackled. The exponential growth of the search space is one of thesechallenges making finding an optimal solution computationally infeasi-ble for large datasets. Furthermore, the weight space while searchingfor optimal weight is highly nonlinear. Motivated by the observedcharacteristics, complexities, and challenges in the field, this paperpresents an innovative (CIDS) named ANWO-MLP (Adaptive NonlinearWhale Optimization Multi-layer Perceptron). A novel feature selectionmethod called ANWO-FS (Adaptive Nonlinear Whale Optimization -Feature Selection) is employed in the proposed CIDS to identify themost predictive features enabling robust MLP training even in thehighly nonlinear weight spaces. The insider threat detection process is improved by investigating vital aspects of CIDS, including data pro-cessing, initiation, and output handling. We adopt ANWOA (previouslyproposed by us) to mitigate local stagnation, enable rapid conver-gence, optimize control parameters, and handle multiple objectives byinitializing the weight vector in the ANWO-MLP training with mini-mal mean square error. Experiments were conducted on various highlyimbalanced datasets to verify the robustness, stability, and efficiencyof our proposed ANWO-MLP . By employing diverse metrics, superiordetection rates are demonstrated compared to existing approaches.

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“…Within the realm of prediction-oriented challenges, a pantheon of data mining and machine learning algorithms and techniques awaits exploration [20, 22, 17, 16, 5, 10]. In this study, we harness the power of five distinct machine-learning algorithms and data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Cardiovascular Disease Detection: Leveraging Data Mining and Machine Learning

Hossain,

Talukder,

Mahmud

2024

Preprint

Self Cite

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Cardiovascular disease (CVD) is a significant global health concern, requiring early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns and predicting CVD risk factors like blood pressure (BP), cholesterol levels, and genetic predispositions. Our research aims to predict CVD presence using ML algorithms, leveraging the Heart Disease UCI dataset with 14 attributes and 303 instances. Extensive feature engineering enhanced model performance. We developed five models using Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest Classifier, refining them with hyperparameter tuning. Results show substantial accuracy improvements post-tuning and feature engineering. 'Logistic Regression' achieved the highest accuracy at 93.44%, closely followed by 'Support Vector Machine' at 91.80%. Our findings emphasize the potential of ML in early CVD prediction, underlining its value in healthcare and proactive risk management. ML's utilization for CVD risk assessment promises personalized healthcare, benefiting both patients and healthcare providers. This research showcases the practicality and effectiveness of ML-based CVD risk assessment, enabling early intervention, improving patient outcomes, and optimizing healthcare resource allocation.

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A dependable hybrid machine learning model for network intrusion detection

Cited by 79 publications

References 44 publications

Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications

Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications

ANWO-MPL:An Adaptive Nonlinear Multi-layer Perceptron Cyber Intrusion Detection Framework

Advancements in Cardiovascular Disease Detection: Leveraging Data Mining and Machine Learning

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