Decision tree pairwise metric learning against adversarial attacks

Appiah, Benjamin; Qin, Zhiguang; Ayidzoe, Mighty Abra; Kanpogninge, Ansuura JohnBosco Aristotle

doi:10.1016/j.cose.2021.102268

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Other minor differences exist between these four main DT algorithms such as, how to deal with missing value, variable selection, capacity to handle a huge number of classes in variables, and pruning methods [28][29][30]. DT has been used in phishing detection [31] and Adversarial detection [32]. 3.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

Shahin

Chen

Bouzary

et al. 2022

Lecture Notes in Mechanical Engineering

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The term “the Industrial Internet of Things” has become increasingly more pervasive in the context of manufacturing as digitization has become a business priority for many manufacturers. IIoT refers to a network of interconnected industrial devices, resulting in systems that can monitor, collect, exchange, analyze, and deliver valuable data and new insights. These insights can then help drive smarter, and faster business decisions for manufacturers. However, these benefits have come at the cost of creating a new attack vector for the malicious agents that aim at stealing manufacturing trade secrets, blueprints, or designs. As a result, cybersecurity concerns have become more relevant across the field of manufacturing. One of the main tracks of research in this field deals with developing effective cyber-security mechanisms and frameworks that can identify, classify, and detect malicious attacks in industrial IoT devices. In this paper, we have developed and implemented a classification and detection framework for addressing cyber-security concerns in industrial IoT which takes advantage of various machine learning algorithms. The results prove the satisfactory performance and robustness of the approach in classifying and detecting the attacks.

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Section: Decision Tree (Dt)mentioning

confidence: 99%

Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

Shahin

Chen

Bouzary

et al. 2022

Lecture Notes in Mechanical Engineering

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“…Studies related to data analysis in time series have been carried out for a long time [38,39]. ML-based applications have become more popular due to their high performance on data inference, outperforming even classical statistical models [40].…”

Section: Generation Forecasting Modulementioning

confidence: 99%

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

et al. 2021

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With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples.

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“…Adversarial attacks have been investigated in the area of image, audios, texts and recently in Windows executable files classification exercise and a number of successful attacks examples in image, audios and texts have been generated to cause misclassification [1][2][3][4][5][6][7]. The principal reason for the success in image, audios and texts is that their feature-space is comparatively fixed, an image or text can be formatted as a three-dimensional array of pixels with each pixel value as a three-dimensional RGB (red, green, blue) vector value ranged from 0 to 255, thus, is feasible to find an exact function that is differentiable, therefore, a feature-space attack built on gradients can instantly apply on text or images to create adversarial attack examples.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Programming-based Adversarial Windows Payload Generator

Kingful,

Ahene,

Appiah

et al. 2023

Preprint

Self Cite

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This work presents a behavior preserving Adversarial payload framework against static Windows malware scanners.The framework uses Dynamic Programming to decide on the sequence of static code transformation actions to transform a Windows payload to its adversarial state. In an empirical evaluation with Windows payloads from Metasploit Framework in a black-box settings, static machine learning based and majority of commercial antivirus scanners can still be evaded by these transformations. The potency of these generated Adversarial payload capable of breaching commercial antivirus on users’ devices was demonstrated. The experimental results show a generated Adversarial Backdoor Trojan evade static and also evade its offline dynamic detector and establish a backdoor on the users’ device.

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Decision tree pairwise metric learning against adversarial attacks

Cited by 5 publications

References 9 publications

Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

Dynamic Programming-based Adversarial Windows Payload Generator

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