I2DS: Interpretable Intrusion Detection System Using Autoencoder and Additive Tree

Xu, Wenfeng; Fan, Yongxian; Li, Changyong

doi:10.1155/2021/5564354

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…Paper [25] used a characterlevel convolutional neural network (CLCNN) with exceptionally massive world-wide pooling to segment HTTP request elements and recognize them as benign or malicious requests. They evaluated the structure of the http dataset CSIC 2010 dataset and achieved an accuracy of 98.8 in 10x cross-validation, with a typical processing time per request of 2.35 ms In paper [26], they have proposed a clever interruption location framework model dependent on model based interpretability, called Interpretable Intrusion Detection System (I2DS). First and foremost, typical and assault tests are recruited via AutoEncoder (AE) with preparing tests to feature the ordinary and assault highlights, so the classifier has an exquisite impact.…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

Recent Studies and A Review about Malware detection and classification by using Artificial Intelligence Techniques

Abdelhafez,

A. El-Sawy,

Sakr

2023

Benha Journal of Applied Sciences

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Due to the harmful and unsafe broad utilization of Malware emergency as a result of various sorts of malware, perilous programs, and scripts that are accessible on the tremendous virtual world known as the Web. This study centers on learning around most later different sorts of malware and strategies to induce freed of them by finding them and kicking them out of the framework, which isn't simple since these little pieces of script or code can be found all over within the client framework. In this paper, we highlight malware collection, conglomeration, and dispersal challenges in client framework environment and show a comprehensive dialog on the later ponders that utilized different AI strategies to meet particular destinations of most malware location frameworks, from 2017 to 2022. We compare and differentiate diverse calculations based on optimization criteria, recreation, genuine sending, malware sorts, and execution parameters. We conclude with conceivable future inquire about headings. This would direct the peruser towards an understanding of up-to-date applications of ML methods concerning malware acknowledgment, accumulation, and spread challenges. At that point, we offer a common assessment and comparison of diverse ML strategies used, which is able be a direct for the investigate community in recognizing the foremost adjusted strategies and the benefits of utilizing different AI and machine learning strategies for tackling the challenges related to getting freed of these destructive malware. At long last, we conclude the paper by expressing the open issues of investigate and unused conceivable outcomes for future ponders.

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Section: Intrusion Detection Systemmentioning

confidence: 99%

Recent Studies and A Review about Malware detection and classification by using Artificial Intelligence Techniques

Abdelhafez,

A. El-Sawy,

Sakr

2023

Benha Journal of Applied Sciences

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“…Xu et al 28 demonstrated a model for IDS using the Interpretable IDS (I 2 DS) technique based on interpretability. Initially, the normal and attack instances were combined and reconstructed using auto encoder (AE), and it was considered a matrix of linear transformation.…”

Section: Related Workmentioning

confidence: 99%

Effective intrusion detection system for IoT using optimized capsule auto encoder model

Kumar¹,

Durairaj²,

Ali³

et al. 2022

Concurrency and Computation

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Intrusion Detection Systems (IDS) play a major part in protecting security threats and networks from attacks. Due to the rapid development of the internet of things (IoT), more cyber‐attacks are attacking these devices. Various security challenges still occur on IoT devices since most of them have limited security mechanisms. Hence, this paper introduces a combination of linear and non‐linear space transformation models for IDS. Independent component analysis (ICA) is employed for linear transformation to obtain an orthogonal space, and a dual‐phase distance metric learning method (D‐DML) is utilized to obtain an optimal distance metric. The Gaussian radial basis function (GRBF) model is employed for non‐linear transformation. Then these features of linear and non‐linear models are integrated and classified by capsule auto encoder with a hybrid kernel function (HKCAE) which classifies normal and malicious attacks. Guidance of the capuchin search algorithm (CSA) is employed to optimize the HKCAE parameters during prediction attack prediction. The performance of the implemented approach is compared with the other approaches with some measures like precision, accuracy, sensitivity, F‐score, and specificity benchamrked through UNSW‐15 and BoT‐IoT datasets. The accuracy of the developed scheme is 0.9973 and 0.999 on UNSW‐15 and BoT‐IoT datasets, respectively.

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“…Also considered were data from GPRS, NSL-KDD, and UNSW-NB15. is classifier is put up against others like Multilayer Perceptrons [28], NBTrees [29], a Random Tree ensemble [30], and Nave Bayes [31]. Study indicated that random forest-based IDSs beat other classifiers in terms of performance.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

Ugendhar

Illuri²,

Vulapula

et al. 2022

Mathematical Problems in Engineering

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Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technology advances, making it difficult to detect an incursion. Detecting and mitigating these threats is a significant issue for standard analytic methods. Furthermore, the attackers use complex processes to remain undetected for an extended period. The changing nature and many cyberattacks require a quick, adaptable, and scalable defense system. For the most part, traditional machine learning-based intrusion detection relies on only one algorithm to identify intrusions, which has a low detection rate and cannot handle large amounts of data. To enhance the performance of intrusion detection systems, a new deep multilayer classification approach is developed. This approach comprises five modules: preprocessing, autoencoding, database, classification, and feedback. The classification module uses an autoencoder to decrease the number of dimensions in a reconstruction feature. Our method was tested against a benchmark dataset, NSL-KDD. Compared to other state-of-the-art intrusion detection systems, our methodology has a 96.7% accuracy.

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I2DS: Interpretable Intrusion Detection System Using Autoencoder and Additive Tree

Cited by 8 publications

References 27 publications

Recent Studies and A Review about Malware detection and classification by using Artificial Intelligence Techniques

Recent Studies and A Review about Malware detection and classification by using Artificial Intelligence Techniques

Effective intrusion detection system for IoT using optimized capsule auto encoder model

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

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