Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

Rosenberg, Ihai; Shabtai, Asaf; Elovici, Yuval; Rokach, Lior

doi:10.48550/arxiv.2007.02407

Cited by 5 publications

(5 citation statements)

References 96 publications

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“…For example, malicious devices may attempt to mimic the traffic of a legitimate device in order to connect to the network. Fortunately, it is very difficult to do this while preserving the intended malicious functionality [102]. As discussed in [15], the rogue device must be able to generate similar requests to the manufacturer's servers and get similar responses, which is difficult to achieve if device authentication is required.…”

Section: B Reducing the Cost Of Feature Extractionmentioning

confidence: 99%

A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis

et al. 2022

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Smart home IoT devices lack proper security, raising safety and privacy concerns. One-sizefits-all network administration is ineffective because of the diverse QoS requirements of IoT devices. Device classification can improve IoT administration and security. It identifies vulnerable and rogue items and automates network administration by device type or function. Considering this, a promising research topic focusing on Machine Learning (ML)-based traffic analysis has emerged in order to demystify hidden patterns in IoT traffic and enable automatic device classification. This study analyzes these approaches to understand their potential and limitations. It starts by describing a generic workflow for IoT device classification. It then looks at the methods and solutions for each stage of the workflow. This mainly consists of i) an analysis of IoT traffic data acquisition methodologies and scenarios, as well as a classification of public datasets, ii) a literature evaluation of IoT traffic feature extraction, categorizing and comparing popular features, as well as describing open-source feature extraction tools, and iii) a comparison of ML approaches for IoT device classification and how they have been evaluated. The findings of the analysis are presented in taxonomies with statistics showing literature trends. This study also explores and suggests undiscovered or understudied research directions. 15 16 and network management policies based on the type of 48 device, each automatically classified device can be assigned 49 to a class with predetermined policies. 50 Note that the term device classification is often confused 51 with many similar tasks, namely i) traffic classification, 52 ii) intrusion detection, iii) device identification, and iv) device 53 fingerprinting. Traffic classification is a broad research field 54 that involves classifying network traffic based on various 55 parameters [5] (see Fig. 1). For instance, traffic can be classi-56 fied as either legitimate or malicious based on attack patterns: 57 this is called intrusion detection. It can also be classified by 58 the device that generates the traffic (device classification). 59 The devices can be categorized into groups of similar devices, 60 such as devices for energy management or devices for health 61 monitoring, or according to their function, such as cameras, 62 hubs, home assistants, etc. Device identification classifies 63 devices more finely according to their model or constructor, 64 such as D-link camera, Nest camera, Alexa home assistant, 65 or Google home mini assistant, etc. Device fingerprinting is 66 the finest level of device classification. It gives each device 67 instance (e.g., camera A and camera B are two instances of 68 the Nest Camera) a distinct fingerprint that is ''impossible to 69 forge and independent of environmental changes and mobil-70 ity'' [6]. In this study, we focus on device classification as 71 a specific case of traffic classification, broader than device 72 identification and device fingerprinting.73 A simple ...

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Section: B Reducing the Cost Of Feature Extractionmentioning

confidence: 99%

A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis

et al. 2022

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“…However, since ML have been proven to be very instrumental in the progress of CPS, a number of researchers are beginning to explore the field of AML with focus on CPS. Rosenberg et al [107] in their study of adversarial learning in cybersecurity presented CPS and industrial control systems as a case study. Cai et al [108] studied an advanced emergency braking system for self-driving cars that operates by using DNN to estimate the proximity to an obstacle.…”

Section: Adversarial Machine Learning (Aml) and Cpsmentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

146

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Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

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“…Proposal [21] presents an overview of different adversarial attacks, including FGSM. However, unlike our motivation, they use the MNIST and CIFAR10 datasets, not a custom dataset.…”

Section: Related Workmentioning

confidence: 99%

Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives

Arawinkumaar¹,

Pal²,

Jadidi³

2021

Preprint

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Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of Things (IoT) applications and services, and wireless communications. However, at the same time, several vulnerabilities and adversarial attacks make it challenging for a safe and secure smart healthcare system from a security point of view. Machine learning has been used widely to develop suitable models to predict and mitigate attacks. Still, the attacks could trick the machine learning models and misclassify outputs generated by the model. As a result, it leads to incorrect decisions, for example, false disease detection and wrong treatment plans for patients. In this paper, we address the type of adversarial attacks and their impact on smart healthcare systems. We propose a model to examine how adversarial attacks impact machine learning classifiers. To test the model, we use a medical image dataset. Our model can classify medical images with high accuracy. We then attacked the model with a Fast Gradient Method Sign attack (FGSM) to cause the model to predict the images and misclassify them inaccurately. Using transfer learning, we train a VGG-19 model with the medical dataset and later implement the FGSM to the Convolutional Neural Network (CNN) to examine the significant impact it causes on the performance and accuracy of the machine learning model. Our results demonstrate that the adversarial attack misclassifies the images, causing the model's accuracy rate to drop from 88% to 11%.

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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

Cited by 5 publications

References 96 publications

A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis

A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives

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