Intrusion Detection via MLP Neural Network Using an Arduino Embedded System

Florencio, Felipe; Moreno, Edward David; Macedo, Hendrik; Salgueiro, Ricardo Jose Paiva De Britto; Nascimento, Filipe B. do; Santos, Flávio Arthur O.

doi:10.1109/sbesc.2018.00036

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…According to the taxonomy presented in our earlier paper by Sarker et al [115], deep learning techniques can be broadly categorized into three types: supervised or discriminative learning, e.g., CNN; unsupervised or generative learning, e.g., Auto-encoder; and hybrid learning combining both with other applicable techniques, can be used to address today's cybersecurity issues. For instance, an intrusion detection model based on the NSL-KDD dataset [116], malware analysis [117], and detecting malicious botnet traffic [118] are all constructed using the MLP network. A CNN-based deep learning model can be used to detect intrusions such as denial-of-service (DoS) attacks [119], malware detection [120], and android malware detection [121].…”

Section: Deep Learning In Cybersecuritymentioning

confidence: 99%

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker

2022

Ann. Data. Sci.

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Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today’s cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

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Section: Deep Learning In Cybersecuritymentioning

confidence: 99%

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker

2022

Ann. Data. Sci.

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“…The performance metrics used in this study are accuracy, precision, recall, and f1-Score, based on the research from [7], [8], [9], [10], [11], and [12]. Accuracy is the percentage of samples that are correctly classified above the total number of samples.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Model measurements are consisting of accuracy, precision, coverage, and f1-score, and performance measurements are consisting of mean, standard deviation, variance, and confidence interval. The results are the value of Test 1 and Test 2 such as accuracy (97.14% (Test 1) and 59.02% (Test 2)), precision (98.6% (Test 1) and 96.1% (Test 2)), coverage (95.2% (Test 1) and 52.1% (Test 2)), f1-score (96.9% (Test 1) and 67.6% (Test 2)), mean (5716.04 μs), standard deviation (66.38 μs), variance (4406.86 μs), and confidence interval (5711.88 μs; 5720.20 μs) [8].…”

Section: Introductionmentioning

confidence: 99%

Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System

Satyanegara

Ramli

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Man in the Middle (MitM) is one of the attack techniques conducted for eavesdropping on data transitions or conversations between users in some systems secretly. It has a sizeable impact because it could make the attackers will do another attack, such as website or system deface or phishing. Deep Learning could be able to predict various data well. Hence, in this study, we would like to present the approach to detect MitM attacks and process its data, by implementing hybrid deep learning methods. We used 2 (two) combinations of the Deep Learning methods, which are CNN-MLP and CNN-LSTM. We also used various Feature Scaling methods before building the model and will determine the better hybrid deep learning methods for detecting MitM attack, as well as the feature selection methods that could generate the highest accuracy. Kitsune Network Attack Dataset (ARP MitM Ettercap) is the dataset used in this study. The results prove that CNN-MLP has better results than CNN-LSTM on average, which has the accuracy rate respectively at 99.74%, 99.67%, and 99.57%, and using Standard Scaler has the highest accuracy (99.74%) among other scenarios.

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“…The neural network was made of 26 input neurons, 9 hidden layers, and 2 output neurons. The paper does not specify were the training phase had occurred [105].…”

Section: Resource-scarce Mcusmentioning

confidence: 99%

Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

2019

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The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.

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Intrusion Detection via MLP Neural Network Using an Arduino Embedded System

Cited by 22 publications

References 14 publications

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System

Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

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