Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Petri Net ◽  
Cyber Security ◽  
Large Scale ◽  
Security System ◽  
Divide And Conquer ◽  
Defense System ◽  
Divide And Conquer Algorithm

This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.

scholarly journals Botnet Defense System: Concept, Design, and Basic Strategy

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 516
Author(s):  
Shingo Yamaguchi
Keyword(s):  
Internet Of Things ◽  
Petri Net ◽  
Cyber Security ◽  
Command And Control ◽  
Adaptive Strategy ◽  
Security System ◽  
Concept Design ◽  
Defense System ◽  
Basic Strategy ◽  
And Control

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.

2A2-G06 A solution of Large-scale TSP by use of LCO with Divide and Conquer Algorithm

2010 ◽  
Vol 2010 (0) ◽  
pp. _2A2-G06_1-_2A2-G06_4
Author(s):  
Kazunori ISHIKAWA ◽  
Ikuo SUZUKI ◽  
Masahito YAMAMOTO ◽  
Masashi FURUKAWA
Keyword(s):  
Large Scale ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm

scholarly journals An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1732
Author(s):  
Sun-Ho Choi ◽  
Yoonkyung Jang ◽  
Hyowon Seo ◽  
Bum Il Hong ◽  
Intae Ryoo
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Sensor Nodes ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm ◽  
Computational Speed ◽  
Large Scale Networks ◽  
Gateway Deployment ◽  
The Cost ◽  
The Given

In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments.

Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security

2019 ◽  
pp. 139-153 ◽  
Author(s):  
Vardan Mkrttchian ◽  
Leyla Ayvarovna Gamidullaeva ◽  
Sergey Kanarev
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Petri Net ◽  
Cyber Security ◽  
Theoretical Basis ◽  
Network Processor ◽  
Security Applications ◽  
The Future ◽  
Universal Network ◽  
Special Case

The literature review of known sources forming the theoretical basis of calculations on Sleptsova networks and on the basis of authors' developments in machine learning with avatar-based management established the basis for the future solutions to hyper-computations to support cyber security applications. The chapter established that the petri net performed exponentially slower and is a special case of the Sleptsov network. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors conclude that machine learning with avatar-based management at the platform of the Sleptsov net-processor is the future solution for cyber security applications in Russia.

scholarly journals A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaodong Liu ◽  
Tong Li ◽  
Runzi Zhang ◽  
Di Wu ◽  
Yongheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Large Scale ◽  
High Rate ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
False Alarms ◽  
Security Issues

In recent years, there have been numerous cyber security issues that have caused considerable damage to the society. The development of efficient and reliable Intrusion Detection Systems (IDSs) is an effective countermeasure against the growing cyber threats. In modern high-bandwidth, large-scale network environments, traditional IDSs suffer from a high rate of missed and false alarms. Researchers have introduced machine learning techniques into intrusion detection with good results. However, due to the scarcity of attack data, such methods’ training sets are usually unbalanced, affecting the analysis performance. In this paper, we survey and analyze the design principles and shortcomings of existing oversampling methods. Based on the findings, we take the perspective of imbalance and high dimensionality of datasets in the field of intrusion detection and propose an oversampling technique based on Generative Adversarial Networks (GAN) and feature selection. Specifically, we model the complex high-dimensional distribution of attacks based on Gradient Penalty Wasserstein GAN (WGAN-GP) to generate additional attack samples. We then select a subset of features representing the entire dataset based on analysis of variance, ultimately generating a rebalanced low-dimensional dataset for machine learning training. To evaluate the effectiveness of our proposal, we conducted experiments based on the NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets. The experimental results show that our method can effectively improve the detection performance of machine learning models and outperform the baselines.

scholarly journals A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization

2016 ◽  
Vol 42 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Yi Mei ◽  
Mohammad Nabi Omidvar ◽  
Xiaodong Li ◽  
Xin Yao
Keyword(s):  
Large Scale ◽  
Black Box ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm
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