An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-Based Enhanced Stacking Multiple Binary Classifiers

AlHabshy, AbdAllah A.; Hameed, Bashar Ibrahim; ElDahshan, Kamal A.

doi:10.1109/access.2022.3174482

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…In [34], the proposed ensemble multi binary attack model (EMBAM) is an intrusion detection system (IDS) that offers a unique anomaly based IDS to detect normal behavior and abnormal attacks, for example, threats in a network.…”

Section: Decision Trees Algorithmmentioning

confidence: 99%

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Maksimović,

Nikolić,

Vidojević

et al. 2024

IEEE Access

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This paper describes an Intrusion Detection System (IDS) which uses several existing known IDS algorithms and Triple Modular Redundancy (TMR) algorithm to make decision about eventual existing attack by majority voting in one constructed ensemble model which solves practically the problem of binary classification. Proposed novel model belongs to so called stacking ensemble methods of machine learning algorithms which uses exactly four algorithms from the group of best binary classification algorithms: Decision trees, Naive Bayes, Support Vector Machine, k-nearest neighbors, logistic regression and AdaBoost and is applicable for any similar problem. Using proposed method, we get a more precisely determined threshold than it is case using whatever of in method individual applied algorithm as well as those algorithms that are the state of the art in the field of binary classification. Besides that, one of the main disadvantages of classic TMR used for classification network traffic, which is the problem of bad over-voting was successfully avoided by improving classic TMR with a new algorithm proposed by the authors. Today a denial of service attacks (DoS) and distributed denial of service (DDoS) are one of most present type on Internet and that is why the authors in this paper paid special attention to them and because of that the authors of proposed method chose to use known KAGGLE dataset which contains data of these type of attacks for the examination of quality of proposed IDS method implemented in suitable software. Obtained results showed that IDS software with implemented proposed method worked precise and timely, which means alarms was trigger properly and efficiently with better results of quality of classification in most important measures than the individual included algorithms who are the state of the art in binary classification.

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Section: Decision Trees Algorithmmentioning

confidence: 99%

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Maksimović,

Nikolić,

Vidojević

et al. 2024

IEEE Access

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“…ML sits at the intersection of computer science and statistics and is the core of artificial intelligence and data science. ML is a broad phrase that describes computational algorithms to enhance functionality or produce accurate predictions regarding performance metrics [15]. Experience in this context refers to the learner's prior knowledge, frequently represented through electronically gathered data available for analysis [16].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

“…Due to its relative simplicity and flexibility in handling multiple classification issues, SVM provides balanced predicted performance, even in studies with limited sample sets [22]. The SVM classifier is based on the concept of the most appropriate hyper-planes employed to distinguish among classes [15,23]. Due to the goals of SVM, the decision boundary might have to be very close to one specific class to correctly label all data points in the training set [23].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells燙lassification

Fathy¹,

Yaseen²,

Abou-Kreisha³

et al. 2023

Computers, Materials &Amp; Continua

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Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border Collie Optimization (BBCO) is introduced as an updated version of Border Collie Optimization (BCO) for feature reduction based on maximizing classification accuracy. The proposed computer aid diagnosis tool merges transfer deep learning ResNet101, BBCO feature reduction, and Support Vector Machine (SVM) classifier to form a hybrid model ResNet101-BBCO-SVM an accurate and fast model for classifying WBCs. As a result, the ResNet101-BBCO-SVM scores the best accuracy at 99.21%, compared to recent studies in the benchmark. The model showed that the addition of the BBCO algorithm increased the detection accuracy, and at the same time, decreased the classification time consumption. The effectiveness of the ResNet101-BBCO-SVM model has been demonstrated and beaten in reasonable ratios in recent literary studies and end-to-end transfer learning of pre-trained models.

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“…In a study conducted in 2022, Alhabshy et al, [6] introduced a specialized anomaly-based intrusion detection system (IDS) called the Ensemble Multi Binary Attack Model (EMBAM). This system enables users to distinguish between normal behavior and abnormal attacks.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhanced Blockchain Based Security and Attack Detection Using Transformer In IOT-Cloud Network

Ingle¹,

Ingle²

2023

ARASET

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The IoT (Internet of Things) encompasses numerous networks and connected devices. One of the primary concerns surrounding IoT, according to researchers and security experts, is the potential risks to privacy and cybersecurity. Deep learning offers significant capabilities for self-adjustment, self-organization, and generalization. Recognizing this, advanced deep learning algorithms are employed in this research to address the privacy and security issues plaguing the IoT landscape. To address these concerns, a novel model called BC-Trans Network is proposed, leveraging the strengths of both Blockchain technology and a transformer component. The transformer plays a vital role in identifying abnormal data, enabling the system to take proactive measures against potential threats. In addition Hash-2 is introduced for the verification of IoT users, adding an extra layer of security to the authentication process. The Blockchain model is utilized to securely store user passwords and details, ensuring a robust and tamper-proof authentication mechanism. To validate the proposed model, a publicly available dataset CSE-CIC-IDS2018 is employed. Pre-processing techniques, including feature selection using the chi-square method, are applied to refine the dataset. The transformer module then classifies the data as normal or abnormal, allowing for accurate identification of potential security breaches. To further safeguard the data and protect the privacy of users, a Fully Homomorphic Encryption (FHE) method is employed. This advanced encryption enables the encryption of categorized normal data, ensuring its confidentiality even during transmission and storage. The study's findings support IoT-cloud server security and privacy by demonstrating the effectiveness of the suggested paradigm in identifying and thwarting network threats. With detection times of 225.3 seconds, an accuracy of 99.25%, a precision of 99.53%, a recall of 99.32%, and an F1 score of 99.59%, the proposed system exhibits impressive performance. Furthermore, as the output numbers increase, the system's metrics improve, suggesting its scalability and flexibility.

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An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-Based Enhanced Stacking Multiple Binary Classifiers

Cited by 13 publications

References 35 publications

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells燙lassification

An Enhanced Blockchain Based Security and Attack Detection Using Transformer In IOT-Cloud Network

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