Transcript CS5361 - ShareCourse

CS536100 網路安全
Network Security
黃能富 教授 (Professor Nen-Fu Huang)
清大資工系, E-mail: [email protected]
助教: 紀成璞, 清大資工系碩士班 [email protected]
0.1
Network Security Outline
 Introduction
 Layer 2 network security
Switch security
 VLAN security
 Layer 3 network security
 Router security,
 Access Control List(ACL)
 Layer 4 network security
 Firewall,
 Flow classification technologies,
 Layer 7 network security
 Deep Packet Inspection (DPI) technologies,
 Intrusion Detection System (IDS), Snort
 Intrusion Prevention System (IPS),
 Anti-virus,

0.2
Network Security Outline
 Application identification technologies,

Protocols headers, packet contents

Encrypted packets
 Machine learning based traffic/Application identification

Machine learning tools

Attributes selection
 Botnet detection and prevention technologies,

IRC, p2p, http, honey-pot
 Cloud security

Virtual machine isolation, security cloud data center, Network
Virtualization
0.3
課程進行方式
 翻轉式教學, 自主學習, 線上預習, 練習, 複習
 課程內容: www.sharecourse.net (每周開放內容)
 請修課同學上網註冊, 選課
 DoS/DDoS 網路攻擊與防禦演練
 Botnet 佈建與經營演練
 期末計畫 (Term project), Malware Snort rules generation and
evaluation.
(針對特定 惡意程式產生其 Snort rule, 並驗證之)
 論文選讀報告 (Paper study and presentation)

每人一篇論文, 口頭 + 書面 報告
 網路安全專家專題演講 (4-5 場次)

惡意程式分析

APT 攻擊受法分析

Botnet 分析與追蹤技術
0.4
Cloud + SRAGP + SDN (Snort Rule Generation)
Rule Generation VMs
Malware
Samples
> 200,000
Malwares
Malware
Dispatcher
Rules
Snort Rules
Automatic
Generation
Platform
Snort Rules
DB
Bot
Security Rules
Server
SDN
Controller
Botnet Master
DoS
Attack
SDN Switches
(Pica8)
0.5
Cloud + SDN + Security (Attack Blocking)
Rule Generation VMs
Malware
Samples
> 200,000
Malwares
Rules
Snort Rules
Automatic
Generation
Platform
Botnet Master
Snort Rules
DB
Malware
Dispatcher
Bot
Security Rules
Server
DoS
Attack
Rules
Rules
SDN Switches
(Pica8)
SDN
Controller
0.6
Network Security 成績考核方式
 成績考核(Evaluation)

線上期中考 (Min-term test) 30%

線上期末考 (Final test) 30%

DoS/DDoS 網路攻擊與防禦演練 10%

Botnet 佈建與經營演練 10%

惡意程式分析期末計畫 (Term project) 10%,

網路安全相關論文報告 (Paper study and
presentation) 10% (同儕評分)

平時成績 5% (課堂與專題演講出席等)
0.7