1. IPS
[Intrusion Prevention System]
Vishwanath Badiger
Avaya India

2. Introduction
• Intrusion prevention systems are network
security devices that monitor network and/or
system activities for malicious activity (intrusion)
• Main functions of Intrusion Prevention System
(IPS) are,
– Identify intrusion
– Log information about intrusion
– Attempt to block/stop intrusion and
– Report intrusion
• Intrusion Detection System (IDS) only detect
intrusions

3. Terminology
• False positive – Incorrectly identifying benign
activity as being malicious
• False negative – Failing to identify malicious
activity has occurred
• Many organizations choose to decrease false
negatives at the cost of increasing false
positives. Altering the configuration of an IPS
to improve its detection accuracy is known as
tuning

4. Detection Methods
• Signature based detection
– This method uses preconfigured and
predetermined attack patterns (signatures) to
detect attacks
– Monitors network traffic for matches to signatures
and takes appropriate action if match is found
– Very effective at detecting known threats
– Largely ineffective at detecting unknown threats
and many variants of known threats

5. Detection Methods
• Anomaly based detection
– Creates baseline of average network traffic
– Intermittently samples network traffic to compare
the sample to the set baseline
– If the sample is outside the baseline then
appropriate action will be taken
– Requires much more overhead and processing
capacity than signature based detection
– May generate many false positives

6. Detection Methods
• Stateful Protocol Analysis detection
– Can natively decode application layer network
protocols like HTTP, FTP, etc
– Once protocols are fully decoded, the IPS analysis
engine can evaluate different parts of protocol for
anomalous behavior or exploits against
predetermined profiles of generally accepted
definitions of benign protocol activity for each
protocol state
– very difficult or impossible to develop completely
accurate models of protocols

7. Classifications
• Network based IPS [NIPS]
– Perform packet sniffing and analyze network traffic to
identify and stop suspicious activity
– They are typically deployed inline like a network
firewall
– They receive packets, analyze them, decide whether
they should be permitted, and allow acceptable
packets to pass through
– Most products use a combination of attack signatures
and analysis of network and application protocols
– Some products allow administrators to create and
deploy attack signatures

8. Network based IPS

9. Classifications
• Host based IPS [HIPS]
– HIPS are similar in principle and purpose to network-
based , except that a host-based product monitors the
characteristics of a single host and the events
occurring within that host, such as monitoring
network traffic (only for that host), system logs,
running processes, file access and modification, and
system and application configuration changes
– Host-based IDPSs are most commonly deployed on
critical hosts such as publicly accessible servers and
servers containing sensitive information

10. Host based IPS

11. Classifications
• Network Behavior Analysis [NBA]
– Examines network traffic to identify threats that
generate unusual traffic flows, such as denial of
service (DoS) and distributed denial of service (DDoS)
attacks, certain forms of malware and policy violations
– NBA systems are most often deployed to monitor
flows on an organization’s internal networks and flows
between an organization’s networks and external
networks

12. Classifications
• Wireless IPS [WIPS]
– Monitors wireless network traffic and analyzes its
wireless networking protocols to identify suspicious
activity involving the protocols themselves
– It cannot identify suspicious activity in the application
or higher-layer network protocols (e.g., TCP, UDP) that
the wireless network traffic is transferring

13. Case Study: Snort
• Snort is an open source
network intrusion prevention
and detection system
• It uses a rule-based language
combining signature, protocol
and anomaly inspection
methods
• Snort is the most widely
deployed intrusion detection
and prevention technology
and it has become the de facto
standard technology
worldwide in the industry
• Rules-based detection engine

14. Snort: Typical locations

15. Snort: Components

16. Snort: Components
• Packet Decoder – It takes packets from different types of network
interfaces (Ethernet, SLIP,PPP…), prepare packets for processing
• Preprocessors
– prepare data for detection engine
– detect anomalies in packet headers
– packet defragmentation
– decode HTTP URI
– reassemble TCP streams
• Detection Engine - the most important part, applies rules to packets
• Logging and Alerting System
• Output Modules - process alerts and logs and generate final output

17. Snort Rules
• In a single line
• Rules are created by known intrusion signatures
• Usually place in snort.conf configuration file
rule header rule options

18. Snort Rules
• Snort rules are extremely flexible and are easy to
modify, unlike many commercial NIDS
• Sample rule to detect SubSeven trojan:
alert tcp $EXTERNAL_NET 27374 -> $HOME_NET
any(msg:"BACKDOOR subseven 22"; flags: A+; content:
"|0d0a5b52504c5d3030320d0a|"; reference:arachnids,485;
reference:url,www.hackfix.org/subseven/; sid:103;
classtype:misc-activity; rev:4;)
• Elements before parentheses comprise ‘rule
header’
• Elements in parentheses are ‘rule options’

19. Snort Rules
alert tcp $EXTERNAL_NET 27374 -> $HOME_NET any
(msg:"BACKDOOR subseven 22"; flags: A+; content:
"|0d0a5b52504c5d3030320d0a|"; reference:arachnids,485;
reference:url,www.hackfix.org/subseven/; sid:103;
classtype:misc-activity; rev:4;)
• alert action to take; also log, pass, activate, dynamic
• tcp protocol; also udp, icmp, ip
• $EXTERNAL_NET source address; this is a variable – specific IP is ok
• 27374 source port; also any, negation (!21), range (1:1024)
• -> direction; best not to change this, although <> is allowed
• $HOME_NET destination address; this is also a variable here
• any destination port

20. Snort Rules
alert tcp $EXTERNAL_NET 27374 -> $HOME_NET any
(msg:"BACKDOOR subseven 22"; flags: A+; content:
"|0d0a5b52504c5d3030320d0a|"; reference:arachnids,485;
reference:url,www.hackfix.org/subseven/; sid:103;
classtype:misc-activity; rev:4;)
• msg:”BACKDOOR subseven 22”; message to appear in logs
• flags: A+; tcp flags; many options, like SA, SA+, !R, SF*
• content: “|0d0…0a|”; binary data to check in packet; content
without | (pipe) characters do simple content matches
• reference…; where to go to look for background on this rule
• sid:103; rule identifier
• classtype: misc-activity; rule type; many others
• rev:4; rule revision number
• other rule options possible, like offset, depth,
nocase

21. Detection Engine: Rules
Rule Header Rule Options
Alert tcp 1.1.1.1 any -> 2.2.2.2 any (flags: SF; msg: “SYN-FIN Scan”;)
Alert tcp 1.1.1.1 any -> 2.2.2.2 any (flags: S12; msg: “Queso Scan”;)
Alert tcp 1.1.1.1 any -> 2.2.2.2 any (flags: F; msg: “FIN Scan”;)

22. Detection Engine: Internal
Representation
Rule Node
Alert tcp 1.1.1.1 any -> 2.2.2.2 any
Option Node
(flags: SF; msg: “SYN-FIN Scan”;)
(flags: S12; msg: “Queso Scan”;)
(flags: F; msg: “FIN Scan”;)

23. Detection Engine: Fully Populated
Rule Rule Rule Rule Rule
Node Node Node Node Node
Option Option Option Option Option
Node Node Node Node Node
Option Option Option Option
Node Node Node Node
Option Option
Node Node

24. Detection engine order to scan the
rules
• Snort does not evaluate the rules in the
order that they appear in the Snort rules file.
In default, the order is:
– Alert rules
– Pass rules
– Log rules

25. Challenges with snort
• Misuse detection – avoid known intrusions
– Rules database is larger and larger
– It continues to grow
– snort version 2.3.2, there are 2,600 rules
– Snort spends 80% work time to do string match
• Anomaly detection – identify new attacks
– Probability of detection is low

26. Attempts to improve
• Using hardware to reduce workload : a hybrid
architecture
– software has more flexibility, hardware has
relatively higher throughput
• Better detection algorithm
• Migrating it to multi core platforms to take
better use of underlying hardware

27. Thank You !
Vishwanath Badiger
Avaya India