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White-Hat Worm to Fight Malware and Its Evaluation by Agent-Oriented Petri Nets †

Sensors (Basel, Switzerland)

MDPI

IoT, cybersecurity, malware, DDoS, bot, botnet, Petri net

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Abstract

A new kind of malware called Mirai is spreading like wildfire. Mirai is characterized by targeting Internet of Things (IoT) devices. Since IoT devices are increasing explosively, it is not realistic to manage their vulnerability by human-wave tactics. This paper proposes a new approach that uses a white-hat worm to fight malware. The white-hat worm is an extension of an IoT worm called Hajime and introduces lifespan and secondary infectivity (the ability to infect a device infected by Mirai). The proposed white-hat worm was expressed as a formal model with agent-oriented Petri nets called PN $2$ . The model enables us to simulate a battle between the white-hat worm and Mirai. The result of the simulation evaluation shows that (i) the lifespan successfully reduces the worm’s remaining if short; (ii) if the worm has low secondary infectivity, its effect depends on the lifespan; and (iii) if the worm has high secondary infectivity, it is effective without depending on the lifespan.

Most cited references21

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DDoS in the IoT: Mirai and Other Botnets

(2017)
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PEABS: A Process for developing Efficient Agent-Based Simulators

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Adversarial Samples on Android Malware Detection Systems for IoT Systems

(2019)
Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.
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Author and article information

Journal
Sensors (Basel)
Sensors (Basel)
sensors
Sensors (Basel, Switzerland)
MDPI
1424-8220
19 January 2020
January 2020
: 20
: 2
Affiliations
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube 755-8611, Japan; shingo@ 123456yamaguchi-u.ac.jp
Author notes
[†]

This paper is an extended version of our paper published in Yamaguchi, S. Modeling and Evaluation of IoT Worm with Lifespan and Secondary Infectivity by Agent-Oriented Petri Net PN $2$ . In Proceeding of the IEEE 6th International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW), Yilan, Taiwan, 20–22 May 2019.

Article
sensors-20-00556
10.3390/s20020556
7014485
31963954

Categories
Article

Biomedical engineering

iot, cybersecurity, malware, ddos, bot, botnet, petri net