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Search Results (5)

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Keywords = white-hat botnet

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20 pages, 1843 KiB  
Article
Implementation of White-Hat Worms Using Mirai Source Code and Its Optimization through Parameter Tuning
by Yudai Yamamoto, Aoi Fukushima and Shingo Yamaguchi
Future Internet 2024, 16(9), 336; https://doi.org/10.3390/fi16090336 - 13 Sep 2024
Cited by 1 | Viewed by 2425
Abstract
Mirai, an IoT malware that emerged in 2016, has been used for large-scale DDoS attacks. The Mirai source code is publicly available and continues to be a threat with a variety of variants still in existence. In this paper, we propose an implementation [...] Read more.
Mirai, an IoT malware that emerged in 2016, has been used for large-scale DDoS attacks. The Mirai source code is publicly available and continues to be a threat with a variety of variants still in existence. In this paper, we propose an implementation system for malicious and white-hat worms created using the Mirai source code, as well as a general and detailed implementation method for white-hat worms that is not limited to the Mirai source code. The white-hat worms have the function of a secondary infection, in which the white-hat worm disinfects the malicious worm by infecting devices already infected by the malicious worm, and two parameters, the values of which can be changed to modify the rate at which the white-hat worms can spread their infection. The values of the parameters of the best white-hat worm for disinfection of the malicious botnet and the impact of the value of each parameter on the disinfection of the malicious botnet were analyzed in detail. The analysis revealed that for a white-hat worm to disinfect a malicious botnet, it must be able to infect at least 80% of all devices and maintain that situation for at least 300 s. Then, by tuning and optimizing the values of the white-hat worm’s parameters, we were able to successfully eliminate the malicious botnet, demonstrating the effectiveness of the white-hat botnet’s function of eliminating the malicious botnet. Full article
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18 pages, 8783 KiB  
Article
Diffusion of White-Hat Botnet Using Lifespan with Controllable Ripple Effect for Malware Removal in IoT Networks
by Mohd Anuaruddin Bin Ahmadon and Shingo Yamaguchi
Sensors 2023, 23(2), 1018; https://doi.org/10.3390/s23021018 - 16 Jan 2023
Cited by 2 | Viewed by 2129
Abstract
Self-propagating malware has been infecting thousands of IoT devices and causing security breaches worldwide. Mitigating and cleaning self-propagating malware is important but challenging because they propagate unpredictably. White-hat botnets have been used to combat self-propagating malware with the concept of fight fire-with-fire. However, [...] Read more.
Self-propagating malware has been infecting thousands of IoT devices and causing security breaches worldwide. Mitigating and cleaning self-propagating malware is important but challenging because they propagate unpredictably. White-hat botnets have been used to combat self-propagating malware with the concept of fight fire-with-fire. However, white-hat botnets can also overpopulate and consume the resource of IoT devices. Later, lifespan was introduced as a self-destruct measure to restrain white-hat botnets’ overpopulation, but unable to change based on real-time situations. This paper proposes a method for diffusing white-hat botnets by controlling lifespan. The main contribution of this paper is that the method uses a dynamic lifespan that increases and decreases based on the congregation’s situation of the self-propagating malware and white-hat botnets. The method tackles the problem of overpopulation of white-hat botnets since they can self-propagate by controlling the ripple effect that widens the white-hat botnet’s diffusion area but suppresses the number of white-hat botnets to achieve a ’zero-botnet’ situation. The effectiveness in reducing the overpopulation rate was confirmed. The experiment result showed that the ripple effect could reduce the number of white-hat botnets in the network by around 80%, depending on different control parameters. Full article
(This article belongs to the Section Internet of Things)
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22 pages, 12835 KiB  
Article
Botnet Defense System: Observability, Controllability, and Basic Command and Control Strategy
by Shingo Yamaguchi
Sensors 2022, 22(23), 9423; https://doi.org/10.3390/s22239423 - 2 Dec 2022
Cited by 5 | Viewed by 2822
Abstract
This paper deals with the observability, controllability, and command and control strategy in the Botnet Defense System (BDS) that disinfects malicious botnets with white-hat botnets. The BDS defends an IoT system built over the Internet. The Internet is characterized by openness, but not [...] Read more.
This paper deals with the observability, controllability, and command and control strategy in the Botnet Defense System (BDS) that disinfects malicious botnets with white-hat botnets. The BDS defends an IoT system built over the Internet. The Internet is characterized by openness, but not all nodes are observable and controllable. We incorporated the concept of observability and controllability into the BDS design and theoretically clarified that the BDS can enhance its observability and controllability by utilizing its white-hat botnets. In addition, we proposed a Withdrawal strategy as a basic strategy to command and control white-hat botnets. Then, we modeled the BDS, adopted the Withdrawal strategy with agent-oriented Petri net PN2 and confirmed the effect through the simulation of the model. The result shows that even if considering observability and controllability, the BDS wiped out the malicious bots and reduced the white-hat bots to less than 1% as long as the white-hat worms were sufficiently infectious. Full article
(This article belongs to the Section Internet of Things)
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16 pages, 727 KiB  
Article
Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System
by Xiangnan Pan and Shingo Yamaguchi
Sensors 2022, 22(13), 4666; https://doi.org/10.3390/s22134666 - 21 Jun 2022
Cited by 9 | Viewed by 2534
Abstract
Malicious botnets such as Mirai are a major threat to IoT networks regarding cyber security. The Botnet Defense System (BDS) is a network security system based on the concept of “fight fire with fire”, and it uses white-hat botnets to fight against malicious [...] Read more.
Malicious botnets such as Mirai are a major threat to IoT networks regarding cyber security. The Botnet Defense System (BDS) is a network security system based on the concept of “fight fire with fire”, and it uses white-hat botnets to fight against malicious botnets. However, the existing white-hat Worm Launcher of the BDS decides the number of white-hat worms, but it does not consider the white-hat worms’ placement. This paper proposes a novel machine learning (ML)-based white-hat Worm Launcher for tactical response by zoning in the BDS. The concept of zoning is introduced to grasp the malicious botnet spread with bias over the IoT network. This enables the Launcher to divide the network into zones and make tactical responses for each zone. Three tactics for tactical responses for each zone are also proposed. Then, the BDS with the Launcher is modeled by using agent-oriented Petri nets, and the effect of the proposed Launcher is evaluated. The result shows that the proposed Launcher can reduce the number of infected IoT devices by about 30%. Full article
(This article belongs to the Special Issue Lightweight Security Integrity and Confidentiality for Internet of Things (IoT))
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15 pages, 585 KiB  
Article
Botnet Defense System: Concept, Design, and Basic Strategy
by Shingo Yamaguchi
Information 2020, 11(11), 516; https://doi.org/10.3390/info11110516 - 4 Nov 2020
Cited by 29 | Viewed by 5424
Abstract
This paper proposes a new kind of cyber-security system, named Botnet Defense System (BDS), which defends an Internet of Things (IoT) system against malicious botnets. The concept of BDS is “Fight fire with fire”. The distinguishing feature is that it uses white-hat botnets [...] Read more.
This paper proposes a new kind of cyber-security system, named Botnet Defense System (BDS), which defends an Internet of Things (IoT) system against malicious botnets. The concept of BDS is “Fight fire with fire”. The distinguishing feature is that it uses white-hat botnets to fight malicious botnets. A BDS consists of four components: Monitor, Strategy Planner, Launcher, and Command and Control (C&C) server. The Monitor component watches over a target IoT system. If the component detects a malicious botnet, the Strategy Planner component makes a strategy against the botnet. Based on the planned strategy, the Launcher component sends white-hat worms into the IoT system and constructs a white-hat botnet. The C&C server component commands and controls the white-hat botnet to exterminate the malicious botnet. Strategy studies are essential to produce intended results. We proposed three basic strategies to launch white-hat worms: All-Out, Few-Elite, and Environment-Adaptive. We evaluated BDS and the proposed strategies through the simulation of agent-oriented Petri net model representing the battle between Mirai botnets and the white-hat botnets. This result shows that the Environment-Adaptive strategy is the best and reduced the number of needed white-hat worms to 38.5% almost without changing the extermination rate for Mirai bots. Full article
(This article belongs to the Special Issue Security and Privacy in the Internet of Things)
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