Recommendations for DDOS Attack-Based Intrusion Detection System Through Data Analysis

Pande, Sagar; Kamparia, Aditya; Gupta, Deepak

doi:10.1007/978-981-16-3346-1_73

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The number of classes in the data set was 5, the number of attacks in the training data set was 21, and the number of attacks in the testing data set was 37 [4,20]; this represents that the number of additional novel attacks in the testing data set was 16 [16,28]. There were about 41 attributes for each record of the data set; the comprehensive report of the attributes are mentioned in [13,15].…”

Section: Data Set Discussionmentioning

confidence: 99%

Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems

Pande

Khamparia²

2023

csci

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The research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particularly, the research which are widely utilizing machine learning concepts, and it is proven that these concepts were effective with IDSs, particularly, deep neural network-based models enhanced the rate of detections of IDSs. At the same instance, the models are turning out to be very highly complex, users are unable to track down the explanations for the decisions made which indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, the article deals with the proposed model that able to explain the obtained predictions. The proposed framework is a combination of a conventional intrusion detection system with the aid of a deep neural network and interpretability of the model predictions. The proposed model utilizes Shapley Additive Explanations (SHAP) that mixes with the local explainability as well as the global explainability for the enhancement of interpretations in the case of intrusion detection systems. The proposed model was implemented using the popular dataset, NSL-KDD, and the performance of the framework evaluated using accuracy, precision, recall, and F1-score. The accuracy of the framework is achieved by about 99.99%. The proposed framework able to identify the top 4 features using local explainability and the top 20 features using global explainability.

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Section: Data Set Discussionmentioning

confidence: 99%

Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems

Pande

Khamparia²

2023

csci

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“…Other issues include the fact that most anomaly-based IDS lack high-quality datasets for analysis and that the error rate automatically rises when issues like redundancy arise. The most recent publicly accessible dataset, labelled flow data CIDDS-01 (Yin et al, 2017) (Ibrahim et al, 2013), is presented by Pande, Kamparia, and Gupta (2022). (Ibrahim et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing

Rawat¹,

Chakrawarti²,

Vyas

et al. 2023

International Journal of Information Security and Privacy

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IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSL-KDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.

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“…All these models achieve very high accuracies and low false-positive rates, which are within the acceptable threshold, thus qualifying them as potential options for realworld implementations. Other strategies such as ensemble learning [51], dataset analysis based recommendations [31], feature extraction [20] and feature selection [2] (like stacked feature selection) [38] have also been successfully utilized to improve the performance of the models.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Continual Learning Models for Intrusion Detection System

Prasath

Sethi²,

Mohanty

et al. 2022

IEEE Access

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Deep Learning based Intrusion Detection Systems (IDSs) have received significant attention from the research community for their capability to handle modern-day security systems in large-scale networks. Despite their considerable improvement in performance over machine learning-based techniques and conventional statistical models, deep neural networks (DNN) suffer from catastrophic forgetting: the model forgets previously learned information when trained on newer data points. This vulnerability is specifically exaggerated in large scale systems due to the frequent changes in network architecture and behaviours, which leads to changes in data distribution and the introduction of zero-day attacks; this phenomenon is termed as covariate shift. Due to these constant changes in the data distribution, the DNN models will not be able to consistently perform at high accuracy and low false positive rate (FPR) rates without regular updates. However, before we update the DNN models, it is essential to understand the magnitude and nature of the drift in the data distribution. In this paper, to analyze the drift in data distribution, we propose an eight-stage statistics and machine learning guided implementation framework that objectively studies and quantifies the changes. Further, to handle the changes in data distribution, most IDS solutions collect the network packets and store them to retrain the DNN models periodically, but when the network's size and complexity increase, those tasks become expensive. To efficiently solve this problem, we explore the potential of continual learning models to incrementally learn new data patterns while also retaining their previous knowledge. We perform an experimental and analytical study of advanced intrusion detection systems using three major continual learning approaches: learning without forgetting, experience replay, and dark experience replay on the NSL-KDD and the CICIDS 2017 dataset. Through extensive experimentation, we show that our continual learning models achieve improved accuracy and lower FPR rates when compared to the state-of-the-art works while also being able to incrementally learn newer data patterns. Finally, we highlight the drawbacks of traditional statistical and non-gradient based machine learning approaches in handling the covariate shift problem.

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Recommendations for DDOS Attack-Based Intrusion Detection System Through Data Analysis

Cited by 5 publications

References 16 publications

Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems

Explainable Deep Neural Network Based Analysis on Intrusion Detection Systems

Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing

Analysis of Continual Learning Models for Intrusion Detection System

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