C-DIDS: A Cooperative and Distributed Intrusion Detection System in Cloud environment

Ghribi, Saadia; Meddeb-Makhlouf, Amel; Zarai, Faouzi

doi:10.1109/iwcmc.2018.8450478

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…This analysis shows that the proposed ADC-DBScan-LNB technique outperforms the other technique with reduced delay of transmission. In the existing work [55], intrusion detection is performed with the help of radial basis function integrated with BAT algorithm. Figure 7 depicts the confusion matrix of the proposed scheme for the NSL-KDD dataset, where the actual/predicted number of classes are determined with the number of sequences.…”

Section: Figure 4 (A) Features Of Nsl-kdd Dataset Figure 4 (B) Attack...mentioning

confidence: 99%

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

et al. 2022

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Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples.Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. Theresults show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes. INDEX TERMSNetwork IntrusionDetection System (NIDS), Density-based spatial clustering of applications with noise (DBSCAN), Anticipated Distance-based Clustering (ADC), Data Preprocessing, Likelihood Naïve Bayes (LNB), and IDS Datasets.

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Section: Figure 4 (A) Features Of Nsl-kdd Dataset Figure 4 (B) Attack...mentioning

confidence: 99%

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

et al. 2022

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“…Previous research only considered the amount of data analysis components and locations rather than the components of data collection. Some distributed systems of intrusion detection are classified into Distributed Intrusion Detection System (DIDS) [51,52].…”

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confidence: 99%

Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

et al. 2021

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In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In addition, a Sparse Matrix is used to reduce the space by keeping only the weight of the features that appear in the trace, thus ignoring the zero weights. Subsequently, Linear Correlation Coefficient (LCC) is utilized to compensate for any missing N-gram in the test data. In the feature selection stage, the Mutual Information (MI) method and Principle Component Analysis (PCA) are utilized and then compared to reduce the number of input features. Following the feature selection stage, the modeling and performance evaluation of various Machine Learning classifiers are conducted using a Raspberry Pi IoT device. Further analysis of the effect of MLP parameters, such as the number of nodes, number of features, activation, solver, and regularization parameters, is also conducted. From the simulation, it can be seen that different parameters affect the accuracy and lightweight evaluation. By using a single hidden layer and four nodes, the proposed method with MI can achieve 96% accuracy, 97% recall, 96% F1-Measure, 5% False Positive Rate (FPR), highest curve of Receiver Operating Characteristic (ROC), and 96% Area Under the Curve (AUC). It also achieved low CPU time usage of 4.404 (ms) milliseconds and low energy consumption of 8.809 (mj) millijoules.

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Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach

Ogwara

Petrova

Yang

et al. 2021

Advances in Security, Networks, and Internet of Things

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This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile-Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect malicious activities at the UL in the MCCenvironment. Preliminary investigations show that our approach will enhance the security of the UL in the MCC environment. Our future work will include the development and the evaluation of the proposed model across the various mobile platforms in the MCC environment.

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C-DIDS: A Cooperative and Distributed Intrusion Detection System in Cloud environment

Cited by 6 publications

References 10 publications

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach

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