Big data analysis and distributed deep learning for next-generation intrusion detection system optimization

Jallad, Khloud Al; Aljnidi, Mohamad; Desouki, Mohammad Said

doi:10.1186/s40537-019-0248-6

Cited by 39 publications

(18 citation statements)

References 34 publications

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“…We have done many experiments [26] that we care about collective and contextual attacks. Where we chose using a deep network with type of recurrent so we can handle sequences of actions or events.…”

Section: Methodsmentioning

confidence: 99%

Anomaly detection optimization using big data and deep learning to reduce false-positive

2020

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Signature-based Intrusion detection systems are not suitable anymore to be used in nowadays network environment. Because signature-based models are not able to detect new threats and unknown attacks. Due to technology improvement, the number of attacks is increasing exponentially. Statistics show that attacks number increases with a rate of 100% each year causing huge money loss, about tens of millions of dollars for ransomware attacks only. This high number of millions of new threats that are developed every day, reduces the effectiveness of signature-based IDS because it is not a practical solution to update the signatures databases every few minutes. Anomaly-based IDS can be a better alternative of signature-based IDS because it is more suitable for nowadays Abstract Anomaly-based Intrusion Detection System (IDS) has been a hot research topic because of its ability to detect new threats rather than only memorized signatures threats of signature-based IDS. Especially after the availability of advanced technologies that increase the number of hacking tools and increase the risk impact of an attack. The problem of any anomaly-based model is its high false-positive rate. The high false-positive rate is the reason why anomaly IDS is not commonly applied in practice. Because anomaly-based models classify an unseen pattern as a threat where it may be normal but not included in the training dataset. This type of problem is called overfitting where the model is not able to generalize. Optimizing Anomaly-based models by having a big training dataset that includes all possible normal cases may be an optimal solution but could not be applied in practice. Although we can increase the number of training samples to include much more normal cases, still we need a model that has more ability to generalize. In this research paper, we propose applying deep model instead of traditional models because it has more ability to generalize. Thus, we will obtain less false-positive by using big data and deep model. We made a comparison between machine learning and deep learning algorithms in the optimization of anomaly-based IDS by decreasing the false-positive rate. We did an experiment on the NSL-KDD benchmark and compared our results with one of the best used classifiers in traditional learning in IDS optimization. The experiment shows 10% lower false-positive by using deep learning instead of traditional learning.

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Section: Methodsmentioning

confidence: 99%

Anomaly detection optimization using big data and deep learning to reduce false-positive

2020

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“…This subsection studies the IDS schemes [82][83][84], which apply RNN to detect intrusions. For instance, for detecting attacks within the IoT network, Roy et al [85] presented a deep learning-based approach using an LSTM RNN.…”

Section: Recurrent Neural Network-based Schemesmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems 

Lánský

Mohammadi

Mohammed

et al. 2021

Preprint

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The ever-increasing complication and severity of the computer networks' security attacks have inspired security researchers to apply various machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques that recently have been widely used by intrusion detection systems (IDS) to secure computer networks and hosts' performance. This survey article focuses on the signature-based IDS using deep learning techniques and puts forward an in-depth survey and classification of these schemes. For this purpose, it first presents the essential background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods applied in each of them. It describes how deep learning networks are utilized in the misuse detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

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“…In this paper [19] the language-processing concepts, distributed-deep-learning concepts, Big-data concepts, ow-analysis of anomaly-identi cation concepts and the contextual-analysis concepts are integrated to generate the model. Further to this, the framework de nes the network-abstract behaviour obtained by the millions count of packets in the context.…”

Section: Effective and Accurate Big-data Processing Based Operational Parametersmentioning

confidence: 99%

Survey of Deep-learning Techniques in Big-Data analytics

Goswami¹,

Kumar²

2021

Preprint

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The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

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Big data analysis and distributed deep learning for next-generation intrusion detection system optimization

Cited by 39 publications

References 34 publications

Anomaly detection optimization using big data and deep learning to reduce false-positive

Anomaly detection optimization using big data and deep learning to reduce false-positive

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems

Survey of Deep-learning Techniques in Big-Data analytics

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Big data analysis and distributed deep learning for next-generation intrusion detection system optimization

Cited by 39 publications

References 34 publications

Anomaly detection optimization using big data and deep learning to reduce false-positive

Anomaly detection optimization using big data and deep learning to reduce false-positive

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems&nbsp;

Survey of Deep-learning Techniques in Big-Data analytics

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A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems