MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System

Yao, Haipeng; Fu, Dejun; Zhang, Peiying; Li, Maozhen; Liu, Yunjie

doi:10.1109/jiot.2018.2873125

Cited by 104 publications

(45 citation statements)

References 21 publications

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“…Their results show that the feature dimension can be reduced to half of the original feature set with the help of the genetic algorithm and high accuracy can be achieved. Yao et al [20] adopt machine learning techniques to propose a new intrusion detection framework to overcome the imbalance of different kinds of data in network traffic and the nonidentical distribution between the training set and the test set. Ren et al [21] adopt isolation forest, genetic algorithm, and RF to design a new intrusion detection system which mainly consists of data sampling and feature selection.…”

Section: Related Workmentioning

confidence: 99%

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A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

2020

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Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples. This poses new threats to the security-critical applications in cybersecurity. Currently, there is still a short of study on adversarial examples in the domain of cybersecurity. In this paper, we propose a new method known as the brute-force attack method to better evaluate the robustness of the machine learning classifiers in cybersecurity against adversarial examples. The proposed method, which works in a black-box way and covers some shortages of the existing adversarial attack methods based on generative adversarial networks, is simple to implement and only needs the output of the target classifiers to generate adversarial examples. To have a comprehensive evaluation of the attack performance of the proposed method, we use our method to generate adversarial examples against the common machine learning based security systems in cybersecurity including host intrusion detection systems, Android malware detection systems, and network intrusion detection systems. We compare the attack performance of the proposed method against these security systems with that of state-of-the-art adversarial attack methods based on generative adversarial networks. The preliminary experimental results show that the proposed method, which is more efficient in computation and outperforms the state-of-the-art attack methods based on generative adversarial networks, can be used to evaluate the robustness of various machine learning based systems in cybersecurity against adversarial examples. INDEX TERMS Adversarial examples, machine learning, deep learning, intrusion detection, malware detection, neural networks, black-box method.

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Section: Related Workmentioning

confidence: 99%

“…The details of the ADFA-LD dataset are shown in Table 1. The NSL-KDD dataset is a benchmark dataset for network intrusion detection, which is widely adopted to evaluate the performance of NIDSs [2], [16], [20]. The previous work [9], [10], [15] mostly adopts the NSL-KDD dataset to verify the attack effect of their methods.…”

Section: Datasetsmentioning

confidence: 99%

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

2020

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“…Moreover, suggested three innovative components related to communication on the outbound network. Haipeng Yao [4] introduces a multilevel model for IDS called multilevel semi-supervised ML (MSML). A notion of "pure cluster" is implemented in the module and implemented a semi-supervised hierarchical kmeans algorithm.…”

Section: Existing Work R Vinayakumarmentioning

confidence: 99%

“…The loss function is same as autoencoder as given in equation 4. e(x,y,W)= + ||W (4) λ is the regularization parameter for the regularization term.…”

Section: B Convolutional Autoencodermentioning

confidence: 99%

An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

T¹,

Saravanan²

2020

IJACSA

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The necessity for safety of information in a network has inflated due to the impressive growth of web applications. Several methods of intrusion detection are used to detect irregularities which depend on precision, detection frequency, other parameters and are anticipated to familiarize to vigorously varying risk scenes. To accomplish consistent abnormalities detection in a network many machine learning algorithms have been formulated by researchers. A technique based on unsupervised machine learning that use two separate machine learning algorithms to identify anomalies in a network viz convolutional autoencoder and softmax classifier is proposed. These profound models were skilled as well as evaluated on NSLKDD test data sets on the NSLKDD training dataset. Using well-known classification metrics such as accuracy, precision and recall, these machine learning models were assessed. The developed intrusion detection system model experimental findings showed promising outcomes in anomaly detection systems for real-world implementation and is compared with the prevailing definitive machine learning techniques. This strategy increases the detection of network intrusion and offers a renewed intrusion detection study method.

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“…The reasons are the massive workload on the IoT network, the cost of the communication infrastructure, the required energy for data transmission, and, more generally, reliability, latency, and privacy concerns [10]. The new trend of IoT devices is to be "smart" to make decisions on their own, without streaming all the raw data to the cloud [11]. The edge computing paradigm is pushing the data processing to the edge of the IoT (comprising gateways and embedded end-devices) close to the sensors where the data is collected [9].…”

Section: Introductionmentioning

confidence: 99%

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Wang

Magno

Cavigelli

et al. 2020

IEEE Internet Things J.

130

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The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic and firmware implementations. This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-Vbased Parallel Ultra-Low-Power (PULP) platform. The toolkit takes multi-layer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This paper also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multi-sensor bracelet. Experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22x and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

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MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System

Cited by 104 publications

References 21 publications

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

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