Intrusion Detection

CS 161 Fall 2022 - Lecture 23

Computer Science 161

Last Time: Denial of Service

• Availability: Making sure users are able to use a service
• DoS attacks availability of services
• Application-level DoS: Attacks the high-level applications
• Algorithmic complexity attacks: Attack using inputs that cause the worst-case runtime of an algorithm
• Defense: Identification, isolation, and quotas
• Defense: Proof of work
• Network-level DoS: Attacks the network of a service
• Typically floods either the network bandwidth or the packet processing capacity
• Distributed DoS: Use multiple computers to flood a network at the same time
• Amplified DoS: Use an amplifier to turn a small input into a large output, spoofing packets so the reply goes to the victim
• Defense: Packet filtering
• All DoS attacks can be defended against by overprovisioning

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Computer Science 161

Last Time: SYN Cookies

• SYN flooding: A type of DoS that causes a server to allocate state for unfinished TCP connections, upon receiving a SYN packet
• SYN cookies: Instead of allocating state when receiving a SYN, send the state back to the client in the sequence number
• The client returns the state back to the server, which it only then allocates state for

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Computer Science 161

Last Time: Firewalls

• Firewalls: Defend many devices by defending the network
• Security policies dictate how traffic on the network is handled
• Packet filters: Choose to either forward or drop packets
• Stateless packet filters: Choose depending on the packet only
• Stateful packet filters: Choose depending on the packet and the history of the connection
• Attackers can subvert packet filters by splitting key payloads or exploiting the TTL
• Proxy firewalls: Create a connection with both sides instead of forwarding packets

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Computer Science 161

Today: Intrusion Detection

• Path traversal attacks
• Types of detectors
• Network intrusion detection system (NIDS)
• Host-based intrusion detection system (HIDS)
• Detection accuracy
• False positives and false negatives
• Base rate fallacy
• Combining detectors

• Styles of detection
• Signature-based detection
• Specification-based detection
• Anomaly-based detection
• Behavioral detection
• Other intrusion detection strategies
• Vulnerability scanning
• Honeypots
• Forensics
• Intrusion prevention systems

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Today: Intrusion Detection

• We’ve talked about many ways to prevent attacks
• However, some not all methods are perfect: attacks will slip through our defenses
• Recall: “Detect if you can’t prevent”
• How can we detect network attacks when they happen?

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Path Traversal Attacks

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Top 25 Most Dangerous Software Weaknesses (2020)

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Unix File Paths

• A file path points to a file or a directory (folder) on a Unix system
• File paths have special characters
• / (slash): Separates directories
• . (one period): Shorthand for the current directory
• .. (two periods): Shorthand for the parent directory

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Unix File Paths

/home/public/evanbot.jpg

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home

public

private

evanbot.jpg

codabot.jpg

passwords.txt

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Unix File Paths

./codabot.jpg (Assume we're currently in public)

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home

public

private

evanbot.jpg

codabot.jpg

passwords.txt

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Unix File Paths

/home/public/../private/passwords.txt

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home

public

private

evanbot.jpg

codabot.jpg

passwords.txt

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Path Traversal Intuition

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home

private

evanbot.jpg

codabot.jpg

passwords.txt

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Path Traversal Intuition

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home

public

private

evanbot.jpg

codabot.jpg

passwords.txt

Backend Filesystem

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Path Traversal Attacks

• Path traversal attack: Accessing unauthorized files on a remote server by exploiting Unix file path semantics
• Often makes use of ../ to enter other directories
• Vulnerability: User input is interpreted as a file path by the Unix file system
• Defense: Check that user input is not interpreted as a file path

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Types of Detectors

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Types of Detectors

• Three types of detectors
• Network Intrusion Detection System (NIDS)
• Host-based Instruction Detection System (HIDS)
• Logging
• The main difference is where the detector is deployed

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Structure of a Network

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Server

Employee Computer

Employee Computer

Border Router

End hosts in the local network send packets to the Internet by sending it to the border router for forwarding

Internet

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Network Intrusion Detection System (NIDS)

• Network intrusion detection system (NIDS): A detector installed on the network, between the local network and the rest of the Internet
• Monitors network traffic to detect attacks

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Server

Employee Computer

Employee Computer

Border Router

Internet

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Network Intrusion Detection System (NIDS)

• Operation:
• NIDS has a table of all active connections and maintains state for each connection
• If the NIDS sees a packet not associated with any known connection, create a new entry in the table
• Example: A connection that started before the NIDS started running
• NIDS can be used for more sophisticated network monitoring: not only detect attacks, but analyze and understand all the network traffic

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NIDS: Benefits

• Cheap: A single detector can cover a lot of systems
• Easy to scale: As the network gets larger, add computing power to the NIDS
• Linear scaling: Investing twice as much money gives twice as much bandwidth
• Simple management: Easy to install and manage a single detector
• End systems are unaffected
• Doesn’t consume any resources on end systems
• Useful for adding security on an existing system
• Smaller trusted computing base (TCB)
• Only the detector needs to be trusted

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NIDS: Drawbacks

• Inconsistent or ambiguous interpretation between the detector and the end host
• How does the NIDS monitor encrypted traffic?

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Drawback: Inconsistent Interpretation

• What should the NIDS do if it sees this packet?
• This looks like a path traversal attack… Maybe it should alert
• What if the packet’s TTL expires before it reaches any end host?
• Problem: What the NIDS sees doesn’t exactly match what arrives at the end system

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NIDS

../etc/passwd

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Drawback: Inconsistent Interpretation

• What should the NIDS do if it sees this packet?
• This doesn’t look like a path traversal attack...maybe it shouldn’t alert
• This input is using URL percent encoding. If you decode it, you get ../etc/passwd!
• Problem: Inputs are interpreted differently between the NIDS and the end system

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NIDS

%2e%2e%2f%2e%2e%2f

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Drawback: Inconsistent Interpretation

• What should the NIDS do if it sees this packet?
• What file on the file system does this file path refer to? It’s hard for the NIDS to know
• Problem: Information needed to interpret correctly is missing

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NIDS

..///.///..////

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Evasion Attacks

• Problem: Imperfect observability
• What the NIDS sees doesn’t match what the end system sees
• Example: The packet’s time-to-live (TTL) might expire before reaching the end host
• Problem: Incomplete analysis (double parsing)
• Inconsistency: Inputs are interpreted and parsed differently between the NIDS and the end system
• Ambiguity: Information needed to interpret correctly is missing
• Evasion attack: Exploit inconsistency and ambiguity to provide malicious inputs that are not detected by the NIDS

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Evasion Attacks: Defenses

• Make sure that the NIDS and the end host are using the same interpretations
• This can be very challenging
• How do we detect the URL-encoded attack %2e%2e%2f%2e%2e%2f?�Now the NIDS has to parse URL encodings!
• How do we detect a more complicated path traversal attack ..///.///..////?�Now the NIDS has to parse Unix file paths!
• Impose a canonical (“normalized”) form for all inputs
• Example: Force all URLs to expand all URL encodings or not expand all URL encodings
• Analyze all possible interpretations instead of assuming one
• Flag potential evasions so they can be investigated further

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Drawback: Encrypted Traffic

• Recall: TLS is end-to-end secure, so a NIDS can’t read any encrypted traffic
• One possible solution: Give the NIDS access to all the network’s private keys
• Now the NIDS can decrypt messages to inspect them for attacks
• Problem: Users have to share their private key with someone else

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Recall: Structure of a Network

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Server

Employee Computer

Employee Computer

Border Router

End hosts in the local network send packets to the Internet by sending it to the border router for forwarding

Internet

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Host-Based Intrusion Detection System (HIDS)

• Host-based intrusion detection system (HIDS): A detector installed on each end system

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Server

Employee Computer

Employee Computer

Border Router

HIDS: put detectors here

Internet

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Host-Based Intrusion Detection System (HIDS)

• Benefits
• Fewer problems with inconsistencies or ambiguities: The HIDS is on the end host, so it will interpret packets exactly the same as the end host!
• Works for encrypted messages
• Can protect against non-network threats too (e.g. malicious user inside the network)
• Performance scales better than NIDS: one NIDS is more vulnerable to being overwhelmed than many HIDS
• Drawbacks
• Expensive: Need to install one detector for every end host
• Evasion attacks are still possible (consider Unix file name parsing)

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Logging

• Logging: Analyze log files generated by end systems
• Example: Each night, run a script on the log files to analyze them for attacks
• Benefits
• Cheap: Modern web servers often already have built-in logging systems
• Fewer problems with inconsistencies or ambiguities: The logging system works on the end host, so it will interpret packets exactly the same as the end host!
• Drawbacks
• Unlike NIDS and HIDS, there is no real-time detection: attacks are only detected after the attack has happened
• Some evasion attacks are still possible (again, consider Unix file name parsing)
• The attacker could change the logs to erase evidence of the attack

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Detection Accuracy

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Detection Errors

• Two main types of detector errors
• False positive: Detector alerts when there is no attack
• False negative: Detector fails to alert when there is an attack
• Detector accuracy is often assessed in terms of the rates at which these errors occur
• False positive rate (FPR): The probability the detector alerts, given there is no attack
• False negative rate (FNR): The probability the detector does not alert, given there is an attack

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Perfect Detectors

• Can we build a detector with a false positive rate of 0%? How about a detector with a false negative rate of 0%?
• Recall false positive rate: The probability the detector alerts, given there is no attack
• Recall false negative rate: The probability the detector does not alert, given there is an attack

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void detector_with_no_false_positives(char *input) {

printf("Nope, not an attack!");

}

void detector_with_no_false_negatives(char *input) {

printf("Yep, it's an attack!");

}

Computer Science 161

Detection Tradeoffs

• The art of a good detector is achieving an effective balance between false positives and false negatives
• The quality of the detector depends on the system you’re using it on
• What is the rate of attacks on your system?
• How much does a false positive cost in your system?
• How much does a false negative cost in your system?
• Example of cost analysis: Fire alarms
• Which is better: a very low false positive rate or a very low false negative rate?
• Cost of a false positive: The fire department needs to inspect the building
• Cost of a false negative: The building burns down
• In this situation, false negatives are much more expensive!
• We want a detector with a low false negative rate

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Detection Tradeoffs

• Example of changing the base rate of attacks
• Consider a detector with a 0.1% false positive rate (for every 1,000 non-attacks, there is one mistaken alert)
• Scenario #1: Our server receives 1,000 non-attacks and 5 attacks per day
• Expected number of false positives per day: 1,000 × 0.1% = 1
• Scenario #2: Our server receives 10,000,000 non-attacks and 5 attacks per day
• Expected number of false positives per day: 10,000,000 × 0.1% = 10,000
• Possibly expensive if the false positives cost money to investigate
• Example: Maybe a human has to manually examine 10,000 requests per day
• Nothing changed about the detector: Only our environment changed
• Takeaway: Accurate detection is very challenging if the base rate of attacks is low!

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Detection Tradeoffs

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Not false positives

False positives

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Base Rate Fallacy

• Consider the detector from before: 0.1% false positive rate
• Assume a 0% false negative rate: Every attack is detected
• Scenario from before: Our server receives 10,000,000 non-attacks and 5 attacks per day
• Expected number of false positives per day: 10,000,000 × 0.1% = 10,000
• You see the detector alert. What is the probability this is really an attack?
• Of the 10,005 detections, 5 are real attacks, and 10,000 are false positives
• There is an approximately 0.05% probability that the detector found a real attack
• Base rate fallacy: Even though the detector alerted, it’s still highly unlikely that you found an attack, because of the high false positive rate
• Takeaway: Detecting is hard when the base rate of attacks is low

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Combining Detectors

• Can you combine two independent detectors to create a better detector?
• Parallel composition
• Alert if either detector alerts
• Intuition: The combination generates more alerts
• Reduces false negative rate
• Increases false positive rate
• Series composition
• Alert only if both detectors alert
• Intuition: The combination generates fewer alerts
• Reduces false positive rate
• Increases false negative rate
• There is no free lunch: reducing one rate usually increases the other

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Styles of Detection

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Styles of Detection

• So far we’ve talked about types of detectors: what the detector is scanning
• Now we’ll talk about styles of detection: how the detector scans data to find attacks
• Four main styles of detection
• Signature-based detection
• Specification-based detection
• Anomaly-based detection
• Behavioral detection

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Signature-based Detection

• Signature-based detection: Flag any activity that matches the structure of a known attack
• Signature-based detection is blacklisting: Keep a list of patterns that are not allowed, and alert if we see something on the list
• Signatures can be at different network layers
• Example: TCP/IP header fields
• Example: URLs
• Example: Payload of the HTTP request

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Signature-based Detection: Examples

• Example: Path traversal attacks
• We know that ../ is often part of a path traversal attack
• Strategy: Alert if any request contains ../
• Example: Buffer overflows
• We know that buffer overflows usually contain shellcode
• Strategy: Keep a list of common shellcodes and alert if any request contains shellcode

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Signature-based Detection: Tradeoffs

• Benefits
• Conceptually simple
• Very good at detecting known attacks
• Easy to share signatures and build up shared libraries of attacks
• Drawbacks
• Won’t catch new attacks without a known signature
• Might not catch variants of known attacks if the variant doesn’t match the signature
• The attacker can modify their attack to avoid matching a signature
• Simpler versions only look at raw bytes, without parsing them in context
• May miss variants
• May generate lots of false positives

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Specification-based Detection

• Specification-based detection: Specify allowed behavior and flag any behavior that isn’t allowed behavior
• Specification-based detection is whitelisting: Keep a list of allowed patterns, and alert if we see something that is not on the list

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Specification-based Detection: Examples

• Example: Path traversal attacks
• We have a folder where all filenames are alphanumeric (a-z, A-Z, 0-9)
• We specify that only alphanumeric characters are allowed as input
• Strategy: Alert if any request contains something other than alphanumeric characters
• If an attacker tries a path traversal attack (../), the detector will flag it
• Example: Buffer overflows
• Consider a program that asks for the user’s age as input
• We know that ages are numerical, so we specify that only numbers are allowed
• Strategy: Flag input that isn’t numerical
• If an attacker tries to input shellcode (not numbers), the detector will flag it

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Specification-based Detection: Tradeoffs

• Benefits
• Can detect new attacks we’ve never seen before
• If we properly specify all allowed behavior, can have low false positive rate
• Drawbacks
• Takes a lot of time and effort to manually specify all allowed behavior
• May need to update specifications as things change

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Anomaly-based Detection

• Idea: Attacks look unusual
• Anomaly-based detection: Develop a model of what normal activity looks like. Alert on any activity that deviates from normal activity.
• Example: Analyze historical logs to develop the model
• Similar to specification-based detection, but learn a model of normal behavior instead of manually specifying normal behavior

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Anomaly-based Detection: Examples

• Example: Path traversal attacks
• Analyze characters in requests and learn that .. only appears in attacks
• Strategy: Alert if any request contains ..
• Example: Buffer overflows
• Study user inputs to a C program
• Learn that user input usually contains characters that can be typed on a keyboard
• Strategy: Alert if the input contains characters that can’t be typed on a keyboard
• If an attacker inputs shellcode (can’t be typed on a keyboard), the detector will alert

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Anomaly-based Detection: Tradeoffs

• Benefits
• Can detect attacks we haven’t seen before
• Drawbacks
• Can fail to detect known attacks
• Can fail to detect new attacks if they don’t look unusual to our model
• What if our model is trained on bad data (e.g. data with a lot of attacks)?
• The false positive rate might be high (lots of non-attacks look unusual)
• If we try to reduce false positives by only flagging the most unusual inputs, the false negative rate might be high (we miss slightly unusual attacks)
• Great subject for academic research papers, but not used in practice

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Behavioral Detection

• Behavioral detection: Look for evidence of compromise
• Unlike the other three styles, we are not scanning the input: We’re looking at the actions triggered by the input
• Instead of looking for the exploit, we’re looking for the result of the exploit
• Behaviors can themselves be analyzed using blacklists (signature-based), whitelists (specification-based), or normal behavior (anomaly-based)

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Behavioral Detection: Examples

• Example: Path traversal attacks
• Strategy: See if any unexpected files are being accessed (e.g. the passwords file)
• Example: Buffer overflows
• Strategy: See if the program calls unexpected functions
• Consider a C program that never calls the exec function: if the program starts running exec, there is probably an attack in progress!

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Behavioral Detection: Tradeoffs

• Benefits
• Can detect attacks we haven’t seen before
• Can have low false positive rates if we’re looking for behavior that rarely occurs in normal programs (e.g. in the exec example, there are probably no false positives!)
• Can be cheap to implement (e.g. existing tools to monitor system calls for a program)
• Drawbacks
• Legitimate processes could perform the behavior as well (e.g. accessing a password file)
• Only detects attacks after they’ve already happened
• Only detects successful attacks (maybe we want to detect failed attacks as well)
• The attacker can modify their attack to avoid triggering some behavior

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Other Intrusion Detection Strategies

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Vulnerability Scanning

• Idea: Instead of detecting attacks, launch attacks on your own system first, and add defenses against any attacks that worked
• Vulnerability scanning: Use a tool that probes your own system with a wide range of attacks (and fix any successful attacks)
• Widely used in practice today
• Often used to complement an intrusion detection system

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Vulnerability Scanning: Tradeoffs

• Benefits
• Accuracy: If your scanning tool is good, it will find real vulnerabilities
• Proactive: Prevents attacks before they happen
• Intelligence: If your intrusion detection system alerts on an attack you know you already fixed, you can safely ignore the alert
• Drawbacks
• Can take a lot of work
• Not helpful for systems you can't modify
• Dangerous for disruptive attacks (you might not know which attacks are dangerous before you run the scanning tool)

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Honeypots

• Honeypot: a sacrificial system with no real purpose
• No legitimate systems ever access the honeypot
• If anyone accesses the honeypot, they must be an intruder
• False positives: Legitimate systems mistakenly accessing the honeypot
• Similar idea as stack canaries

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Honeypots: Examples

• Example: Hospitals
• Employees should not read patient records
• The hospital enters a honeypot record with a celebrity name
• Catch any staff member who reads the honeypot record
• Example: Unsecured Bitcoin wallet
• Leave an unsecured Bitcoin wallet on your system with a small amount of money in it
• If the money is stolen, you know that someone has attacked your system!
• Example: Spamtrap
• Create a fake email address that is never used for legitimate emails
• If email gets sent to the address, it's probably spam!

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Honeypots: Tradeoffs

• Benefits
• Can detect attacks we haven’t seen before
• Can analyze the attacker's actions
• Who is the attacker?
• What are they doing to the system?
• Can distract the attacker from legitimate targets
• Drawbacks
• Can be difficult to trick the attacker into accessing the honeypot
• Building a convincing honeypot might take a lot of work
• These drawbacks matter less if the honeypot is aimed at automated attacks (e.g. the spam detection honeypot)

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Forensics

• Forensics: Analyzing what happened after a successful attack
• Important complement to detecting attacks
• Tools needed
• Detailed logs of system activity
• Tools for analyzing and understanding logs

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Blocking: Intrusion Prevention Systems

• Idea: If we can detect attacks, can we also block them?
• Intrusion prevention system (IPS): An intrusion detection system that also blocks attacks
• Commonly used today
• Drawbacks
• Not possible for retrospective analysis (e.g. logging)
• Difficult for a detector that passively monitors traffic (e.g. an on-path NIDS)
• Dynamically change firewall rules to block attacks?
• Forge a RST packet to stop an attack?
• Need to race against the attacker's malicious packets
• False positives are expensive
• Blocking a non-attack might affect legitimate users

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Building the Perfect IPS?

Takeaway: You must always have tradeoffs between false positive and false negative rates

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Attacks on Intrusion Detection Systems (IDS)

• The IDS is a system with limited resources, so it is vulnerable to DoS attacks!
• DoS attack: Exhaust the IDS’s memory
• IDS needs to track all ongoing activity
• Attacker generates lots of activity to consume all the IDS's memory
• Example: Spoof TCP SYN packets to force the IDS to keep track of too many connections
• DoS attack: Exhaust the IDS’s processing power
• Example: If the IDS uses a hash table to keep track of connections, create hash collisions to trigger worst-case complexity (algorithmic complexity attack)
• The IDS analyzes outside input, so it is vulnerable to code injection attacks!
• Attacker supplies malicious input to exploit the IDS

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Inside A Modern IDS

• Employ defense in depth
• To cover all devices, use a modern NIDS:
• Single entry point with a simple packet filter
• Simple but effective filters can handle 1,000 Gbps
• Parallel processing using multiple NIDS nodes
• A single server rack slot can handle 1–5 Gbps, and scales linearly
• In-depth detection techniques
• Protocol analysis
• Signature analysis on content and behavior
• Shadow execution (execute unknown content found on the network)
• Extensive logging
• Automatic updates

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Internet

Network

Packet Filter

NIDS unit

NIDS unit

NIDS unit

NIDS unit

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Inside A Modern IDS

• Cover individual devices using a HIDS on each device
• Antivirus software is a kind of HIDS used by many corporations!
• Block access to blacklisted sites (e.g. malware sites)
• Detection techniques
• Protocol analysis
• Signature analysis on networking traffic
• Signature analysis on memory and filesystem
• Query a cloud database to see if a payload has been seen by other devices running the same HIDS
• Sandboxed execution (execute a payload in a safe, inescapable environment)
• Analyze the behavior of the program while in the sandbox

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Path Traversal Attacks: Summary

• Path traversal attack: Accessing unauthorized files on a remote server by exploiting Unix file path semantics
• Often makes use of ../ to enter other directories
• Vulnerability: User input is interpreted as a file path by the Unix file system
• Defense: Check that user input is not interpreted as a file path

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Types of Detectors: Summary

• Network Intrusion Detection System (NIDS): Installed on the network
• Benefits: Cheap, easy to scale, simple management, end systems unaffected, small TCB
• Drawbacks: Inconsistent interpretation (leads to evasion attacks), encrypted traffic
• Host-based Intrusion Detection System (HIDS): Installed on the end host
• Benefits: Fewer inconsistencies, works with encrypted traffic, works inside the network, performance can scale
• Drawbacks: Expensive, evasion attacks still possible
• Logging: Analyze logs generated by servers
• Benefits: Cheap, fewer inconsistencies
• Drawbacks: Only detects attacks after they happen, evasion attacks still possible, attacker could change the logs

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Detection Accuracy: Summary

• Two main types of detector errors
• False positive: Detector alerts when there is no attack
• False negative: Detector fails to alert when there is an attack
• Detector accuracy
• False positive rate (FPR): The probability the detector alerts, given there is no attack
• False negative rate (FNR): The probability the detector does not alert, given there is an attack
• Designing a good detector involves considering tradeoffs
• What is the rate of attacks on your system?
• How much does a false positive cost in your system?
• How much does a false negative cost in your system?
• Accurate detection is very challenging if the base rate of attacks is low
• Detectors can be combined
• Parallel: Fewer false negatives, more false positives
• Series: Fewer false positives, more false negatives

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Styles of Detection: Summary

• Signature-based
• Flag any activity that matches the structure of a known attack (blacklisting)
• Good at detecting known attacks, but bad at detecting unknown attacks
• Specification-based
• Specify allowed behavior and flag any behavior that isn’t allowed behavior (whitelisting)
• Can detect unknown attacks, but requires work to manually write specifications
• Anomaly-based
• Develop a model of what normal activity looks like. Alert on any activity that deviates from normal activity.
• Mostly seen in research papers, not in practice
• Behavioral
• Look for evidence of compromise
• Can cheaply detect new attacks with few false positives, but only detects after the attack

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Other Intrusion Detection Strategies: Summary

• Vulnerability scanning: Use a tool that probes your own system with a wide range of attacks (and fix any successful attacks)
• Can accurately prevent attacks before they happen, but can be expensive
• Honeypot: a sacrificial system with no real purpose
• Can detect attackers and analyze their actions, but may take work to trick the attacker into using the honeypot
• Forensics: Analyzing what happened after a successful attack
• Intrusion Prevention System (IPS): An intrusion detection system that also blocks attacks

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