HIDS: A host based intrusion detection system for cloud computing environment

Deshpande, Prachi; Sharma, S. C.; Peddoju, Sateesh K.; Junaid, S.

doi:10.1007/s13198-014-0277-7

Cited by 107 publications

(61 citation statements)

References 9 publications

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“…Such abnormalities are recorded as logs in the IDS servers to prevent similar attacks on the network perimeter. The host IDS, on the contrary, is deployed on each individual host and the incoming and outgoing network traffic is monitored for abnormalities [3][4][5]. The existing data is compared with the previous data log and any discrepancies detected are reported to the administrator.…”

Section: Introductionmentioning

confidence: 99%

“…These decisions require security expert personnel capable of the interpretation of system outputs and computing of crucial functions. Hence experienced security experts are required for the remediation, detection and management of such threats [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Covariance: Covariance configuration is very much the same as variance configuration as shown in Equation (4).…”

Section: Introductionmentioning

confidence: 99%

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A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

et al. 2020

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The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

et al. 2020

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“…Anomaly based [22] Specification based [23] Monitoring Method Network based [24] Host based [25] Behavior Pattern Passive and active IDS [26] …”

Section: Detection Methodsmentioning

confidence: 99%

Security threats in cloud computing

Deshpande¹,

Sharma²,

Kumar

2015

International Conference on Computing, Communication &Amp; Automation

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“…The anomaly-based method identify attacker by comparing the action performed by the recent user to the standard user. In signature-based approach, the ongoing data traffic is monitored by different activities and compared with the rule set [Deshpande et al, (2018); Chiba et al (2019)]. In this research article, we presented an IDS system by using the advantages of nature inspired algorithm with ML approach.…”

Section: Ddos Attacksmentioning

confidence: 99%

A Hybrid Approach for Cyber Security: Improved Intrusion Detection System Using Ann-SVM

Kajal¹,

Nandal²

2020

INDJCSE

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The instances of cyber attacks are increasing as of exponential hike in online processing amid COVID-19 pandemic scenario that significantly compromising huge number of confidential data as well. With the rising security demands, the detection of cyber-attacks has emerged as a promising field that offers wider scope for the application developers. In the similar scenario, authors have proposed an improved detection system that could effectively detect attacks as DDoS, malware etc. Initially, Genetic Algorithm is implemented for the feature section and reducing the size of data thereby reducing the computational work load of classifiers. In second phase, Discrete Wavelet Transform (DWT) with Artificial Bee colony (ABC) is used to divides the data into four categories and also filters out the irrelevant features. In later stages, Artificial Neural Network is employed whose results are refined by Support Vector Machine (SVM). The novelty of the work lies in the precise detection of the malicious behaviour of the nodes. This ANN-SVM hybrid approach enhanced the classification efficiency of the proposed system in identifying the cyber-attacks. The simulation study over 1000 rounds reflects the performance of the proposed system in terms of higher precision, recall and f-measure in comparison to the existing works dedicated to deal with DDoS attacks. Also, the proposed work demonstrated better outcomes in terms of average True Positive rate and False Positive rate.

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HIDS: A host based intrusion detection system for cloud computing environment

Cited by 107 publications

References 9 publications

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Security threats in cloud computing

A Hybrid Approach for Cyber Security: Improved Intrusion Detection System Using Ann-SVM

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