SlideShare uma empresa Scribd logo
1 de 26
MALICIOUS TRAFFIC
Presented by Ishraq Fataftah
Agenda
   Introduction.
   What is Malicious traffic.
   Malicious traffic types.
   Malicious traffic detection and prevention.
   Conclusion.
Introduction
   As the internet become more
    mature, management of its resources to
    provide guaranteed services is crucial.
   The success of the Internet has increased its
    vulnerability to misuse and performance
    problems.
Introduction
   It has been frequently abused by people
    mostly with hostile intentions.
   We have been under various kinds of attacks
    such as viruses, worms and commonly a
    bunch of spam mails every day.
Introduction
Malicious Traffic
   It is hard to detect and distinguish malicious
    packet and legitimate packets in the traffic.
   The behavior of Internet traffic is very far from
    being regular.
   Presents large variations in its throughput at
    all scales.
Malicious Traffic
   Any traffic anomalies that occur from hardware
    or software failures to internet packets with
    maliciously modified options.
   Generated from what is called botnets.
Malicious Traffic: Botnets
Malicious Traffic
   Monitoring the flow of packets.
   Malicious traffic usually exhausts the legitimate
    resources by sending a lot of traffic.
   Monitoring traffic targeting unused addresses
    in the network.
Malicious Traffic Types
   Scanners.
   Worms.
   Malicious Spam.
   Backscatters.
   DOS, DDOS.
Scanners
 Single source.
 Strikes the same port on many machines.

 Different ports on the same machine.

 Generates

a lot of flows.
Worms
   Self-replicating virus that does not alter files
    but resides in active memory and duplicates
    itself.
   CodeRed worm infected 395,000 computers
    and resulted in approximately $2.6 billion in
    damage.
   Results in an increase in service
    activity, especially if service is law traffic.
Worms
MyTob Worm, 2005
                              Copies itself as %System%msnmsgs.exe
                              Adds the value: “MSN” = “msnmsgs.exe” to
              IRC Server       registry:
                               HKEY_LOCAL_MACHINESoftwareMicrosoftWindowsCurrentVersionRun
                               HKEY_LOCAL_MACHINESoftwareMicrosoftWindowsCurrentVersion
                               RunServices
                               HKEY_CURRENT_USERSOFTWAREMicrosoftWindowsCurrentVersionRun
                               HKEY_CURRENT_USERSoftwareMicrosoftOLE
                               HKEY_CURRENT_USERSYSTEMCurrentControlSetControlLsa


                              W32.Mytob@mm runs every time Windows starts




  User Zone                    Server Zone
Malicious Spam
   Spamming is flooding the network with a huge
    amount of unsolicited email messages to force
    people to receive them.
   Contains malware or links to malicious sites.
Backscatter
   Email bounces for emails that a person didn’t
    send.
   Spammer is spoofing the Reply-to field in
    email.
   When sent to email server, it is bounces to the
    reply-to address rather than the sender.
   Used to overcome spam filters and in DOS
    attacks.
DOS, DDOS
   Generate a huge amount of adverse traffic to a
    target server to make it unavailable.
   Attempt to exhaust the resources of the victim.
   They are difficult to detect and prevent.
   DDOS attacks are simultaneously launched
    from several sources destined to the same
    target.
DOS, DDOS
Malicious traffic Detection and
Prevention
   Anomaly detection techniques.
   Signature-scan techniques.
   Intrusion detection and prevention systems.
   QoS metrics.
   Tools such as Snort.
   Network filters such as ACLs.
   Honeypots.
Anomaly detection techniques
   Differentiates between normal and malicious
    traffic by:
     Studying the normal behavior of users, resources.
     Create patterns for these activities.

     Any behavior that deviates from this pattern is
      considered malicious.
Signature-scan techniques
   Uses a database that store signatures.
   Passive scan for network traffic, any patterns
    match these stored signatures are considered
    malicious traffic.
   Effective for known attacks.
Intrusion detection and prevention
systems
   Software or hardware that is designed to
    detect and prevent any malicious attack or
    activity on the network.
   Monitor the network traffic.
   Analyze any suspicious event.
   Log these events and report them to the
    network administrator for actions.
QoS metrics
   Studying the behavior of the network traffic
    under normal and malicious attacks.
   Extracting parameters from network traffic.
Snort
   Open source tool that is used in intrusion
    detection systems.
   Real time analysis on the network traffic.
   Intrusion detection system to monitor the
    traffic, analyzes it and inform the network
    administrator for suspicious activities.
ACLs
   Installed in routers and used to match packet
    headers against a pre-defined list of rules and
    takes pre-defined actions on any matching
    packets.
Honeypots
“a security resource whose value lies in being
  probed, attacked or compromised”

   Any attempt to interact with honeypots incurs a
    malicious activity or attack.
Conclusion
   Malicious traffic is any traffic anomalies occurs
    from failure in traffic packets that is
    intentionally modified for malicious acts.
   By studying malicious attacks we can obtain
    better understanding of malicious traffic and
    how to detect and prevent these attacks.
   An increase in the awareness toward the
    importance of security will help in mitigation
    against internet misuse.

Mais conteúdo relacionado

Mais procurados

Malware detection-using-machine-learning
Malware detection-using-machine-learningMalware detection-using-machine-learning
Malware detection-using-machine-learningSecurity Bootcamp
 
Deep learning approach for network intrusion detection system
Deep learning approach for network intrusion detection systemDeep learning approach for network intrusion detection system
Deep learning approach for network intrusion detection systemAvinash Kumar
 
Block Ciphers Modes of Operation
Block Ciphers Modes of OperationBlock Ciphers Modes of Operation
Block Ciphers Modes of OperationRoman Oliynykov
 
Five Major Types of Intrusion Detection System (IDS)
Five Major Types of Intrusion Detection System (IDS)Five Major Types of Intrusion Detection System (IDS)
Five Major Types of Intrusion Detection System (IDS)david rom
 
Intrusion detection system ppt
Intrusion detection system pptIntrusion detection system ppt
Intrusion detection system pptSheetal Verma
 
Protection in general purpose operating system
Protection in general purpose operating systemProtection in general purpose operating system
Protection in general purpose operating systemG Prachi
 
Security Information and Event Management (SIEM)
Security Information and Event Management (SIEM)Security Information and Event Management (SIEM)
Security Information and Event Management (SIEM)hardik soni
 
Intrusion Detection Systems and Intrusion Prevention Systems
Intrusion Detection Systems  and Intrusion Prevention Systems Intrusion Detection Systems  and Intrusion Prevention Systems
Intrusion Detection Systems and Intrusion Prevention Systems Cleverence Kombe
 
Intrusion detection
Intrusion detectionIntrusion detection
Intrusion detectionCAS
 
Security models
Security models Security models
Security models LJ PROJECTS
 
Network security (vulnerabilities, threats, and attacks)
Network security (vulnerabilities, threats, and attacks)Network security (vulnerabilities, threats, and attacks)
Network security (vulnerabilities, threats, and attacks)Fabiha Shahzad
 
Digital Forensics
Digital ForensicsDigital Forensics
Digital ForensicsOldsun
 
Information Security Principles - Access Control
Information Security  Principles -  Access ControlInformation Security  Principles -  Access Control
Information Security Principles - Access Controlidingolay
 
Segmenting your Network for Security - The Good, the Bad and the Ugly
Segmenting your Network for Security - The Good, the Bad and the UglySegmenting your Network for Security - The Good, the Bad and the Ugly
Segmenting your Network for Security - The Good, the Bad and the UglyAlgoSec
 

Mais procurados (20)

Types of attacks
Types of attacksTypes of attacks
Types of attacks
 
Malware detection-using-machine-learning
Malware detection-using-machine-learningMalware detection-using-machine-learning
Malware detection-using-machine-learning
 
IoT Security
IoT SecurityIoT Security
IoT Security
 
Deep learning approach for network intrusion detection system
Deep learning approach for network intrusion detection systemDeep learning approach for network intrusion detection system
Deep learning approach for network intrusion detection system
 
Block Ciphers Modes of Operation
Block Ciphers Modes of OperationBlock Ciphers Modes of Operation
Block Ciphers Modes of Operation
 
Five Major Types of Intrusion Detection System (IDS)
Five Major Types of Intrusion Detection System (IDS)Five Major Types of Intrusion Detection System (IDS)
Five Major Types of Intrusion Detection System (IDS)
 
Intrusion detection system ppt
Intrusion detection system pptIntrusion detection system ppt
Intrusion detection system ppt
 
Key management
Key managementKey management
Key management
 
Protection in general purpose operating system
Protection in general purpose operating systemProtection in general purpose operating system
Protection in general purpose operating system
 
Firewall ppt
Firewall pptFirewall ppt
Firewall ppt
 
Security Information and Event Management (SIEM)
Security Information and Event Management (SIEM)Security Information and Event Management (SIEM)
Security Information and Event Management (SIEM)
 
Intrusion Detection Systems and Intrusion Prevention Systems
Intrusion Detection Systems  and Intrusion Prevention Systems Intrusion Detection Systems  and Intrusion Prevention Systems
Intrusion Detection Systems and Intrusion Prevention Systems
 
Intrusion detection
Intrusion detectionIntrusion detection
Intrusion detection
 
Firewalls
FirewallsFirewalls
Firewalls
 
Security models
Security models Security models
Security models
 
Network security (vulnerabilities, threats, and attacks)
Network security (vulnerabilities, threats, and attacks)Network security (vulnerabilities, threats, and attacks)
Network security (vulnerabilities, threats, and attacks)
 
Digital Forensics
Digital ForensicsDigital Forensics
Digital Forensics
 
Network forensic
Network forensicNetwork forensic
Network forensic
 
Information Security Principles - Access Control
Information Security  Principles -  Access ControlInformation Security  Principles -  Access Control
Information Security Principles - Access Control
 
Segmenting your Network for Security - The Good, the Bad and the Ugly
Segmenting your Network for Security - The Good, the Bad and the UglySegmenting your Network for Security - The Good, the Bad and the Ugly
Segmenting your Network for Security - The Good, the Bad and the Ugly
 

Destaque

Towards scalable locationaware
Towards scalable locationawareTowards scalable locationaware
Towards scalable locationawareIshraq Al Fataftah
 
Network Intrusion Detection System Using Snort
Network Intrusion Detection System Using SnortNetwork Intrusion Detection System Using Snort
Network Intrusion Detection System Using SnortDisha Bedi
 
Intrusion detection system
Intrusion detection systemIntrusion detection system
Intrusion detection systemAkhil Kumar
 
Intrusion Detection System(IDS)
Intrusion Detection System(IDS)Intrusion Detection System(IDS)
Intrusion Detection System(IDS)shraddha_b
 
Intrusion detection and prevention system
Intrusion detection and prevention systemIntrusion detection and prevention system
Intrusion detection and prevention systemNikhil Raj
 
Introduction to Snort Rule Writing
Introduction to Snort Rule WritingIntroduction to Snort Rule Writing
Introduction to Snort Rule WritingCisco DevNet
 
Intrusion detection system
Intrusion detection system Intrusion detection system
Intrusion detection system gaurav koriya
 
Hacking & its types
Hacking & its typesHacking & its types
Hacking & its typesSai Sakoji
 

Destaque (11)

Towards scalable locationaware
Towards scalable locationawareTowards scalable locationaware
Towards scalable locationaware
 
Optimizing spatial database
Optimizing spatial databaseOptimizing spatial database
Optimizing spatial database
 
Password based cryptography
Password based cryptographyPassword based cryptography
Password based cryptography
 
Network Intrusion Detection System Using Snort
Network Intrusion Detection System Using SnortNetwork Intrusion Detection System Using Snort
Network Intrusion Detection System Using Snort
 
Snort IDS/IPS Basics
Snort IDS/IPS BasicsSnort IDS/IPS Basics
Snort IDS/IPS Basics
 
Intrusion detection system
Intrusion detection systemIntrusion detection system
Intrusion detection system
 
Intrusion Detection System(IDS)
Intrusion Detection System(IDS)Intrusion Detection System(IDS)
Intrusion Detection System(IDS)
 
Intrusion detection and prevention system
Intrusion detection and prevention systemIntrusion detection and prevention system
Intrusion detection and prevention system
 
Introduction to Snort Rule Writing
Introduction to Snort Rule WritingIntroduction to Snort Rule Writing
Introduction to Snort Rule Writing
 
Intrusion detection system
Intrusion detection system Intrusion detection system
Intrusion detection system
 
Hacking & its types
Hacking & its typesHacking & its types
Hacking & its types
 

Semelhante a Malicious traffic

Information security & EthicalHacking
Information security & EthicalHackingInformation security & EthicalHacking
Information security & EthicalHackingAve Nawsh
 
Computing safety
Computing safetyComputing safety
Computing safetyBrulius
 
Threat Hunting Playbook.pdf
Threat Hunting Playbook.pdfThreat Hunting Playbook.pdf
Threat Hunting Playbook.pdflaibaarsyila
 
CyberSecurity presentation for basic knowledge about this topic
CyberSecurity presentation for basic knowledge about this topicCyberSecurity presentation for basic knowledge about this topic
CyberSecurity presentation for basic knowledge about this topicpiyushkamble6
 
Type of Malware and its different analysis and its types !
Type of Malware and its different analysis and its types  !Type of Malware and its different analysis and its types  !
Type of Malware and its different analysis and its types !Mohammed Jaseem Tp
 
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...Editor IJCATR
 
An email worm vaccine architecture
An email worm vaccine architectureAn email worm vaccine architecture
An email worm vaccine architectureUltraUploader
 
Recipient Activated Malware Diffusion
Recipient Activated Malware DiffusionRecipient Activated Malware Diffusion
Recipient Activated Malware DiffusionBruce Fowler
 
Trojan horse and salami attack
Trojan horse and salami attackTrojan horse and salami attack
Trojan horse and salami attackguestc8c7c02bb
 
A review botnet detection and suppression in clouds
A review botnet detection and suppression in cloudsA review botnet detection and suppression in clouds
A review botnet detection and suppression in cloudsAlexander Decker
 
5 worms and other malware
5   worms and other malware5   worms and other malware
5 worms and other malwaredrewz lin
 
L N Yadav Cyber SECURITY.ppt
L N Yadav Cyber SECURITY.pptL N Yadav Cyber SECURITY.ppt
L N Yadav Cyber SECURITY.pptlowlesh1
 
L N Yadav Cyber SECURITY2.ppt
L N Yadav Cyber SECURITY2.pptL N Yadav Cyber SECURITY2.ppt
L N Yadav Cyber SECURITY2.pptlowlesh1
 
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGY
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGYA REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGY
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGYijasa
 
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516Yasser Mohammed
 
Chapter 2 konsep dasar keamanan
Chapter 2 konsep dasar keamananChapter 2 konsep dasar keamanan
Chapter 2 konsep dasar keamanannewbie2019
 
Types of attack -Part3 (Malware Part -2)
Types of attack -Part3 (Malware Part -2)Types of attack -Part3 (Malware Part -2)
Types of attack -Part3 (Malware Part -2)SHUBHA CHATURVEDI
 
computer virus ppt.pptx
computer virus ppt.pptxcomputer virus ppt.pptx
computer virus ppt.pptxAbiniyavk
 

Semelhante a Malicious traffic (20)

Security threats
Security threatsSecurity threats
Security threats
 
Information security & EthicalHacking
Information security & EthicalHackingInformation security & EthicalHacking
Information security & EthicalHacking
 
Computing safety
Computing safetyComputing safety
Computing safety
 
Threat Hunting Playbook.pdf
Threat Hunting Playbook.pdfThreat Hunting Playbook.pdf
Threat Hunting Playbook.pdf
 
CyberSecurity presentation for basic knowledge about this topic
CyberSecurity presentation for basic knowledge about this topicCyberSecurity presentation for basic knowledge about this topic
CyberSecurity presentation for basic knowledge about this topic
 
Type of Malware and its different analysis and its types !
Type of Malware and its different analysis and its types  !Type of Malware and its different analysis and its types  !
Type of Malware and its different analysis and its types !
 
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...
Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Bo...
 
An email worm vaccine architecture
An email worm vaccine architectureAn email worm vaccine architecture
An email worm vaccine architecture
 
Recipient Activated Malware Diffusion
Recipient Activated Malware DiffusionRecipient Activated Malware Diffusion
Recipient Activated Malware Diffusion
 
Modern Malware and Threats
Modern Malware and ThreatsModern Malware and Threats
Modern Malware and Threats
 
Trojan horse and salami attack
Trojan horse and salami attackTrojan horse and salami attack
Trojan horse and salami attack
 
A review botnet detection and suppression in clouds
A review botnet detection and suppression in cloudsA review botnet detection and suppression in clouds
A review botnet detection and suppression in clouds
 
5 worms and other malware
5   worms and other malware5   worms and other malware
5 worms and other malware
 
L N Yadav Cyber SECURITY.ppt
L N Yadav Cyber SECURITY.pptL N Yadav Cyber SECURITY.ppt
L N Yadav Cyber SECURITY.ppt
 
L N Yadav Cyber SECURITY2.ppt
L N Yadav Cyber SECURITY2.pptL N Yadav Cyber SECURITY2.ppt
L N Yadav Cyber SECURITY2.ppt
 
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGY
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGYA REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGY
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGY
 
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516
 
Chapter 2 konsep dasar keamanan
Chapter 2 konsep dasar keamananChapter 2 konsep dasar keamanan
Chapter 2 konsep dasar keamanan
 
Types of attack -Part3 (Malware Part -2)
Types of attack -Part3 (Malware Part -2)Types of attack -Part3 (Malware Part -2)
Types of attack -Part3 (Malware Part -2)
 
computer virus ppt.pptx
computer virus ppt.pptxcomputer virus ppt.pptx
computer virus ppt.pptx
 

Mais de Ishraq Al Fataftah

Mais de Ishraq Al Fataftah (6)

Edge detection
Edge detectionEdge detection
Edge detection
 
Peer to-peer mobile payments
Peer to-peer mobile paymentsPeer to-peer mobile payments
Peer to-peer mobile payments
 
Publish subscribe model overview
Publish subscribe model overviewPublish subscribe model overview
Publish subscribe model overview
 
Requirement engineering evaluation
Requirement engineering evaluationRequirement engineering evaluation
Requirement engineering evaluation
 
Packet sniffing in switched LANs
Packet sniffing in switched LANsPacket sniffing in switched LANs
Packet sniffing in switched LANs
 
Presentation skills
Presentation skillsPresentation skills
Presentation skills
 

Último

QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Alkin Tezuysal
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi González
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observabilityitnewsafrica
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Farhan Tariq
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesManik S Magar
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 

Último (20)

QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 

Malicious traffic

  • 2. Agenda  Introduction.  What is Malicious traffic.  Malicious traffic types.  Malicious traffic detection and prevention.  Conclusion.
  • 3. Introduction  As the internet become more mature, management of its resources to provide guaranteed services is crucial.  The success of the Internet has increased its vulnerability to misuse and performance problems.
  • 4. Introduction  It has been frequently abused by people mostly with hostile intentions.  We have been under various kinds of attacks such as viruses, worms and commonly a bunch of spam mails every day.
  • 6. Malicious Traffic  It is hard to detect and distinguish malicious packet and legitimate packets in the traffic.  The behavior of Internet traffic is very far from being regular.  Presents large variations in its throughput at all scales.
  • 7. Malicious Traffic  Any traffic anomalies that occur from hardware or software failures to internet packets with maliciously modified options.  Generated from what is called botnets.
  • 9. Malicious Traffic  Monitoring the flow of packets.  Malicious traffic usually exhausts the legitimate resources by sending a lot of traffic.  Monitoring traffic targeting unused addresses in the network.
  • 10. Malicious Traffic Types  Scanners.  Worms.  Malicious Spam.  Backscatters.  DOS, DDOS.
  • 11. Scanners  Single source.  Strikes the same port on many machines.  Different ports on the same machine.  Generates a lot of flows.
  • 12. Worms  Self-replicating virus that does not alter files but resides in active memory and duplicates itself.  CodeRed worm infected 395,000 computers and resulted in approximately $2.6 billion in damage.  Results in an increase in service activity, especially if service is law traffic.
  • 13. Worms MyTob Worm, 2005  Copies itself as %System%msnmsgs.exe  Adds the value: “MSN” = “msnmsgs.exe” to IRC Server registry: HKEY_LOCAL_MACHINESoftwareMicrosoftWindowsCurrentVersionRun HKEY_LOCAL_MACHINESoftwareMicrosoftWindowsCurrentVersion RunServices HKEY_CURRENT_USERSOFTWAREMicrosoftWindowsCurrentVersionRun HKEY_CURRENT_USERSoftwareMicrosoftOLE HKEY_CURRENT_USERSYSTEMCurrentControlSetControlLsa  W32.Mytob@mm runs every time Windows starts User Zone Server Zone
  • 14. Malicious Spam  Spamming is flooding the network with a huge amount of unsolicited email messages to force people to receive them.  Contains malware or links to malicious sites.
  • 15. Backscatter  Email bounces for emails that a person didn’t send.  Spammer is spoofing the Reply-to field in email.  When sent to email server, it is bounces to the reply-to address rather than the sender.  Used to overcome spam filters and in DOS attacks.
  • 16. DOS, DDOS  Generate a huge amount of adverse traffic to a target server to make it unavailable.  Attempt to exhaust the resources of the victim.  They are difficult to detect and prevent.  DDOS attacks are simultaneously launched from several sources destined to the same target.
  • 18. Malicious traffic Detection and Prevention  Anomaly detection techniques.  Signature-scan techniques.  Intrusion detection and prevention systems.  QoS metrics.  Tools such as Snort.  Network filters such as ACLs.  Honeypots.
  • 19. Anomaly detection techniques  Differentiates between normal and malicious traffic by:  Studying the normal behavior of users, resources.  Create patterns for these activities.  Any behavior that deviates from this pattern is considered malicious.
  • 20. Signature-scan techniques  Uses a database that store signatures.  Passive scan for network traffic, any patterns match these stored signatures are considered malicious traffic.  Effective for known attacks.
  • 21. Intrusion detection and prevention systems  Software or hardware that is designed to detect and prevent any malicious attack or activity on the network.  Monitor the network traffic.  Analyze any suspicious event.  Log these events and report them to the network administrator for actions.
  • 22. QoS metrics  Studying the behavior of the network traffic under normal and malicious attacks.  Extracting parameters from network traffic.
  • 23. Snort  Open source tool that is used in intrusion detection systems.  Real time analysis on the network traffic.  Intrusion detection system to monitor the traffic, analyzes it and inform the network administrator for suspicious activities.
  • 24. ACLs  Installed in routers and used to match packet headers against a pre-defined list of rules and takes pre-defined actions on any matching packets.
  • 25. Honeypots “a security resource whose value lies in being probed, attacked or compromised”  Any attempt to interact with honeypots incurs a malicious activity or attack.
  • 26. Conclusion  Malicious traffic is any traffic anomalies occurs from failure in traffic packets that is intentionally modified for malicious acts.  By studying malicious attacks we can obtain better understanding of malicious traffic and how to detect and prevent these attacks.  An increase in the awareness toward the importance of security will help in mitigation against internet misuse.

Notas do Editor

  1. threats may range from simple to severe functional and financial damage to the network infrastructure. Adding the legal perspective, these threats should be clearly and carefully identified, analyzed and managed.
  2. data is encapsulated in packets.
  3. Most flows are roughly symmetric at the packet levelWhenever a packet is sent, a packet is received within some reasonable interval (round trip time)This can me measured (and enforced) at the edge router inexpensively
  4. these botnets launch malicious traffic that attacks network hosts and internet service provider (ISPS).
  5. Malicious traffic can be detected by monitoring the network traffic using packet monitoring tools and studying any up normal or suspected behavior in the network. By monitoring the flow of packets, maliciously changed packets can be identified and infected computers can be determined based on its signature. In addition, malicious traffic usually exhausts the legitimate resources by sending a lot of traffic to halt its functionality. Another measurement can be by monitoring traffic targeting unused addresses in the network [3]. Unused addresses should expect a very limited load of traffic not mentioning that no device should be connected to it.
  6. Among all attacks, the denial-of-service (DoS) attack is one ofthe attacks rather difficult to detect and prevent since they exploitregular services, and overwhelm such services with tremendousmalicious traffic.
  7. Anomaly-detection first establishes a normal behavior pattern forusers, programs or resources in the system, and then looks for deviationfrom this behavior.signature-scan techniques passively monitor traffic seen on a network and detect an attack when patterns within the packet match predefined signatures in a database.They are a resource that has no authorized activity, they do not have any production value. Theoreticlly, a honeypot should see no traffic because it has no legitimate activity. This means any interaction with a honeypot is most likely unauthorized or malicious activity. Any connection attempts to a honeypot are most likely a probe, attack, or compromise. Snort’s open source network-based intrusion detection system (NIDS) has the ability to perform real-time traffic analysis and packet logging on Internet Protocol (IP) networks. Snort can be configured in three main modes: sniffer, packet logger, and network intrusion detection. Snort can be configured in three main modes: sniffer, packet logger, and network intrusion detection. the program will monitor network traffic and analyze it against a ruleset defined by the user. The program will then perform a specific action based on what has been identified