A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning

计算机科学 服务拒绝攻击 强化学习 软件定义的网络 可扩展性 入侵检测系统 应用层DDoS攻击 前进飞机 计算机网络 特里诺 计算机安全 人工智能 操作系统 互联网 网络数据包
作者
Noe M. Yungaicela-Naula,Cesar Vargas‐Rosales,Jesús Arturo Pérez-Díaz,Diego Fernando Carrera
出处
期刊:Journal of Network and Computer Applications [Elsevier BV]
卷期号:205: 103444-103444 被引量:53
标识
DOI:10.1016/j.jnca.2022.103444
摘要

Distributed Denial-of-Service (DDoS) attacks are difficult to mitigate with existing defense tools. Fortunately, it has been demonstrated that Software-Defined Networking (SDN) with machine learning (ML) and deep learning (DL) techniques has a high potential to handle these threats effectively. However, although there are many SDN-based solutions for detecting DDoS attacks, only a few contain mitigation strategies. Additionally, most previous studies have focused on solving high-rate DDoS attacks. For the time being, recent slow-rate DDoS threats are hard to detect and mitigate. In this work, we propose a modular, flexible, and scalable SDN-based framework that integrates a DL-based intrusion detection system (IDS) and a deep reinforcement learning (DRL)-based intrusion prevention system (IPS) to address slow-rate DDoS threats. We incorporated scalability features into this framework, such as data-plane-based traffic monitoring and traffic flow sampling. Moreover, we have designed a lightweight DRL-based IPS to provide rapid mitigation responses. Furthermore, to evaluate the framework, we deployed a data center network using Mininet, Open Network Operating System (ONOS) controller, and Apache Web server. Next, we performed extensive experiments varying the number of attackers and the rate of attack connections. The proposed IDS achieved an average detection rate of 98%, with a flow sampling rate of 30%. In addition, IPS timely mitigated slow-rate DDoS with 100% of success for a few attackers. Taken together, these results show that the proposed framework provides effective responses to malicious and legitimate connections.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
魔幻凝云完成签到,获得积分10
刚刚
刚刚
fox完成签到 ,获得积分10
1秒前
量子星尘发布了新的文献求助10
1秒前
3秒前
顾矜应助ins采纳,获得20
3秒前
姚景涛发布了新的文献求助10
4秒前
4秒前
不想学习发布了新的文献求助10
4秒前
wangyup发布了新的文献求助10
5秒前
执着静竹完成签到,获得积分10
6秒前
hetao完成签到,获得积分10
7秒前
完美世界应助忧心的不二采纳,获得10
8秒前
栉风沐雨发布了新的文献求助10
8秒前
11发布了新的文献求助10
9秒前
英俊的铭应助外向芹菜采纳,获得10
10秒前
wanci应助姚景涛采纳,获得10
11秒前
超级炎彬完成签到,获得积分10
12秒前
13秒前
13秒前
wangchaofk发布了新的文献求助20
14秒前
新新完成签到,获得积分10
15秒前
搜集达人应助小超超采纳,获得10
15秒前
付2完成签到,获得积分20
16秒前
55完成签到,获得积分20
16秒前
共享精神应助wangyup采纳,获得10
16秒前
小雨发布了新的文献求助10
17秒前
栉风沐雨完成签到,获得积分10
17秒前
18秒前
魔幻凝云发布了新的文献求助40
19秒前
posh完成签到 ,获得积分10
20秒前
20秒前
20秒前
22秒前
魔幻慕梅完成签到,获得积分10
22秒前
科研通AI2S应助结实的半双采纳,获得10
23秒前
23秒前
ionicliquids发布了新的文献求助10
23秒前
烂漫薯片完成签到,获得积分10
23秒前
SciGPT应助Passerby采纳,获得10
25秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970048
求助须知:如何正确求助?哪些是违规求助? 3514739
关于积分的说明 11175783
捐赠科研通 3250115
什么是DOI,文献DOI怎么找? 1795198
邀请新用户注册赠送积分活动 875630
科研通“疑难数据库(出版商)”最低求助积分说明 804951