scholarly journals Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2098
Author(s):  
Mumraiz Khan Kasi ◽  
Sarah Abu Ghazalah ◽  
Raja Naeem Akram ◽  
Damien Sauveron
Keyword(s):  
Reinforcement Learning ◽  
Network Performance ◽  
Security Attacks ◽  
Placement Problem ◽  
High Data ◽  
Security Issues ◽  
Network Latency ◽  
Multi Agent ◽  
Server Placement ◽  
Action Policy

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

scholarly journals Computer Network Simulation of Firewall and VoIP Performance Monitoring

2018 ◽  
Vol 14 (09) ◽  
pp. 4
Author(s):  
Ammar O. Barznji ◽  
Tarik A. Rashid ◽  
Nawzad K. Al-Salihi
Keyword(s):  
Network Performance ◽  
Performance Monitoring ◽  
Computer Network ◽  
Data Transfer ◽  
Global Changes ◽  
Academic Administration ◽  
High Data ◽  
Security Issues ◽  
Simulated Network ◽  
The University

<p class="0abstract">Fast growing in communication technology has influenced global changes and challenges appear in the field of network security issues. Security solutions must be efficient and operate in a way to deal with threats, reject and stop the network intruders and Trojans. The simulated network of Salahaddin university new campus is planned to build on an area of (3000X3000) meter square. The network consists of many main and secondary devices. The network is mainly consisted of one core switch that provides a very high data transfer through connecting all the collected positions by a variety of cable medias to the entire network switches which are installed in each college location. The department of academic administration (presidency) of the university design is similar to the network designed in each college. The mentioned switch obtains the services from a router that isolates the network from the cloud which supports the services of internet to the network. The firewall is connected to the switch that connects the main server and cloud together. This paper focuses on undertaking a simulation to analyze and examine the performance of the whole network when two scenarios are implemented, the first is if firewall devices is used, the second is when firewall is not used, since the project of building Salahaddin University new campus is at the initial stage, therefore, the researchers think that, it is very important to figure out the drawbacks and deadlocks of using firewall upon each branch of the network and overall network performance before the submitting the final networks design that going to be implemented and installed, because this will indicate many differences on the construction, for example, the network panels ways, the cable collecting locations, network channels and many other device fixing things depending on the media types in addition of the demand of future expansion capabilities. The results show that the using or adding of firewall device to the university campus computer network, will improve the overall network performance though increasing the data stream on many network sections and sectors, also better results are obtained.</p>

Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yang Zhao ◽  
Jian-Ming Hu ◽  
Ming-Yang Gao ◽  
Zuo Zhang
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multi Agent

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group

scholarly journals Multi-Agent Deep Reinforcement Learning Based Cooperative Edge Caching for Ultra-Dense Next-Generation Networks

2020 ◽  
pp. 1-1
Author(s):  
Shuangwu Chen ◽  
Zhen Yao ◽  
Xiaofeng Jiang ◽  
Jian Yang ◽  
Lajos Hanzo
Keyword(s):  
Reinforcement Learning ◽  
Next Generation Networks ◽  
Next Generation ◽  
Multi Agent ◽  
Edge Caching

Multi-Agent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Xiaoyu Zhu ◽  
Yueyi Luo ◽  
Anfeng Liu ◽  
Md Zakirul Alam Bhuiyan ◽  
Shaobo Zhang
Keyword(s):  
Reinforcement Learning ◽  
Computation Offloading ◽  
Multi Agent

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

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