An Extensive Survey on Intrusion Detection- Past, Present, Future

Nagaraja, Arun; Kumar, T.S. Pradeep

doi:10.1145/3234698.3234743

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…He further presented a comprehensive comparative study on iCloud, Dropbox and Google Drive and how they go about securing their various cloud infrastructure. Similarly, [23] [24] [25] and [26] all carried out extensive surveys on IDS deployment in cloud-based environment. However, all these authors failed to address the use of deception-based techniques for intrusion detection in cloud-based platforms.…”

Section: Related Workmentioning

confidence: 99%

“…[28] highlighted some key technology-and business-related challenges in designing and deploying honey pot systems on cloud computing. Likewise, [26] presents an extensive survey on intrusion detection, including methods used for obtain feature selection, computation of high dimensional data, and choice of learning algorithm. [29] stresses the importance of web-based applications and extensively outlines the possible use scenarios of deception techniques which can be incorporated into application-layer traffic of web applications for detecting various web-based application attacks.…”

Section: Related Workmentioning

confidence: 99%

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Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

Oluoha¹,

Yange²,

Okereke³

et al. 2021

JIS

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Cyber criminals have become a formidable treat in today's world. This present reality has placed cloud computing platforms under constant treats of cyber-attacks at all levels, with an ever-evolving treat landscape. It has been observed that the number of threats faced in cloud computing is rising exponentially mainly due to its widespread adoption, rapid expansion and a vast attack surface. One of the front-line tools employed in defense against cyber-attacks is the Intrusion Detection Systems (IDSs). In recent times, an increasing number of researchers and cyber security practitioners alike have advocated the use of deception-based techniques in IDS and other cyber security defenses as against the use of traditional methods. This paper presents an extensive overview of the deception technology environment, as well as a review of current trends and implementation models in deception-based Intrusion Detection Systems. Issues mitigating the implementation of deception based cyber security defenses are also investigated.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

Oluoha¹,

Yange²,

Okereke³

et al. 2021

JIS

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“…An accuracy of 99.7% is reported on NSL-KDD dataset. A recent survey on intrusion detection techniques discussed various issues in designing an efficient intrusion system and some of the state of art contributions [18], [26].…”

Section: Background and Motivationmentioning

confidence: 99%

Similarity Based Feature Transformation for Network Anomaly Detection

Nagaraja

Boregowda²,

Khatatneh

et al. 2020

IEEE Access

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The fundamental objective behind any network intrusion detection system is to automate the detection process whenever intrusions occur in the network. The problem of the network anomaly detection is to determine, if the network incoming traffic is legitimate (or) anomalous. Automated detection systems designed to identify incoming anomalous traffic patterns usually apply widely used machine learning techniques. However, irrespective of any system model which is developed to identify anomalous traffic, all these models requires comparing anomalous and normal traffic patterns. Such comparisons implicitly depend on the ability of the underlying machine learning model to gauge the similarity between a known legitimate observation and the target. The efficiency of any network anomalous detection system depends on the use of distance (or) similarity measures and how they are actually applied. A novel distance function which can be applied to determine the similarity between two conditional feature pattern vectors is an important contribution of present research. Feature dimensionality is another important issue for any machine learning algorithm. In the present work, feature reduction is achieved using the proposed feature transformation technique. However, our approach for feature transformation uses the proposed gaussian distance function to achieve dimensionality reduction to represent the original input dataset in the new transformation space. We have also proposed new computation expressions for determining equivalent deviation and threshold in gaussian space. Experiments are performed on KDD and NSL-KDD datasets by considering widely applied classifier algorithms in various state-of-art research contributions. For performance validation of machine learning models, k-fold cross validation is applied by setting k to 10 through considering evaluation parameters such as accuracy, precision and recall. Experiment results have proved that our approach for anomaly detection that applies the proposed feature transformation technique proved comparatively better to detection methods CANN, GARUDA, and UTTAMA addressed in the recent research literature.

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“…Each of the four categories to anomaly-based detection are evaluated based on the computational complexity among approaches of that type, the most significant network attacks and what the output is in each technique. Nagaraja and Kumar [119] summarized various studies over the span of six years and presented their techniques, year published, identification and the dataset used. Their conclusion was that the main research problem pertained to reducing high dimensional data and that many of the intrusion attacks were SQL-based.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods

Chou,

Jiang

2020

Preprint

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Data-driven methods have been widely used in network intrusion detection (NID) systems. However, there are currently a number of challenges derived from how the datasets are being collected. Most attack classes in network intrusion datasets are considered the minority compared to normal traffic and many datasets are collected through virtual machines or other simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting models such as random forests or support vector machines to unrepresentative "sandbox" datasets. This survey presents a carefully designed taxonomy highlighting eight main challenges and solutions and explores common datasets from 1999 to 2020. Trends are analyzed on the distribution of challenges addressed for the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for larger amount of network intrusion data, and creating labeled datasets collected in real-world networks. CCS Concepts: • General and reference → Surveys and overviews; • Networks → Network security; • Security and privacy → Intrusion/anomaly detection and malware mitigation; • Computing methodologies → Machine learning.

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An Extensive Survey on Intrusion Detection- Past, Present, Future

Cited by 11 publications

References 46 publications

Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

Similarity Based Feature Transformation for Network Anomaly Detection

Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods

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