Midterm Score Distribution

1. Midterm Score Distribution You should worryif you are belowthis point

2. More Announcements • Your projected and optimistically projected grades should be in the grade center soon • Projected: • Your current weighted score /30 * 100 • Optimistic: • (Your current weighted score+70)/100 • Just for your feedback • Quiz 1 is posted • Do it before your lab slot but after this week’s lab lecture • Open book open notes, unlimited time • You will do the same version again after your lab – to be posted soon. Better score counts.

3. Resource Limitations • Don’t allow an individual attack machine to use many of a target’s resources • Requires: • Authentication, or • Making the sender do special work (puzzles) • Authentication schemes are often expensive for the receiver • Existing legitimate senders largely not set up to handle doing special work • Can still be overcome with a large enough army of zombies

4. Hiding From the Attacker • Make it hard for anyone but legitimate clients to deliver messages at all • E.g., keep your machine’s identity obscure • A possible solution for some potential targets • But not for others, like public web servers • To the extent that approach relies on secrecy, it’s fragile • Some such approaches don’t require secrecy

5. Resource Multiplication As attacker demands more resources, supply them Essentially, never allow resources to be depleted Not always possible, usually expensive Not clear that defender can keep ahead of the attacker But still a good step against limited attacks More advanced versions might use Akamai-like techniques

6. Trace and Stop Attacks • Figure out which machines attacks come from • Go to those machines (or near them) and stop the attacks • Tracing is trivial if IP source addresses aren’t spoofed • Tracing may be possible even if they are spoofed • May not have ability/authority to do anything once you’ve found the attack machines • Not too helpful if attacker has a vast supply of machines

7. Filtering Attack Streams • The basis for most defensive approaches • Addresses the core of the problem by limiting the amount of work presented to target • Key question is: • What do you drop? • Good solutions drop all (and only) attack traffic • Less good solutions drop some (or all) of everything

8. Filtering Vs. Rate Limiting • Filtering drops packets with particular characteristics • If you get the characteristics right, you do little collateral damage • At odds with the desire to drop all attack traffic • Rate limiting drops packets on basis of amount of traffic • Can thus assure target is not overwhelmed • But may drop some good traffic

9. Where Do You Filter? In multiple places? In the network core? Near the source? Near the target?

10. Filtering Location Choices • Near target • Near source • In core

11. Filtering Location Choices • Near target • Easier to detect attack • Sees everything • May be hard to prevent collateral damage • May be hard to handle attack volume • Near source • In core

12. Filtering Location Choices • Near target • Near source • May be hard to detect attack • Doesn’t see everything • Easier to prevent collateral damage • Easier to handle attack volume • In core

13. Filtering Location Choices • Near target • Near source • In core • Easier to handle attack volume • Sees everything (with sufficient deployment) • May be hard to prevent collateral damage • May be hard to detect attack

14. How Do You Detect Attacks? • Have database of attack signatures • Detect anomalous behavior • By measuring some parameters for a long time and setting a baseline • Detecting when their values are abnormally high • By defining which behavior must be obeyed starting from some protocol specification

15. How Do You Filter? • Devise filters that encompass most of anomalous traffic • Drop everything but give priority to legitimate-looking traffic • It has some parameter values • It has certain behavior

16. DDoS Defense Challenges • Need for a distributed response • Economic and social factors • Lack of detailed attack information • Lack of defense system benchmarks • Difficulty of large-scale testing • Moving target

17. TCP SYN Flood • Attacker sends lots of TCP SYN packets • Victim sends an ack, allocates space in memory • Attacker never replies • Goal is to fill up memory before entries time out and get deleted • Usually spoofed traffic • Otherwise patterns may be used for filtering • OS at the attacker or spoofed address may send RST and free up memory

18. TCP SYN Cookies • Effective defense against TCP SYN flood • Victim encodes connection information and time in ACK number • Must be hard to craft values that get encoded into the same ACK number – use crypto for encoding • Memory is only reserved when final ACK comes • Only the server must change • But TCP options are not supported • And lost SYN ACKs are not repeated

19. Small-Packet Floods • Overwhelm routers • Create a lot of pps • Exhaust CPU • Most routers can’t handle full bandwidth’s load of small packets • No real solution, must filter packets somehow to reduce router load

20. Shrew Attack • Periodically slam the victim with short, high-volume pulses • Lead to congestion drops on client’s TCP traffic • TCP backs off • If loss is large back off to 1 MSS per RTT • Attacker slams again after a few RTTs • Solution requires TCP protocol changes • Tough to implement since clients must be changed

21. Flash-Crowd Attack • Generate legitimate application traffic to the victim • E.g., DNS requests, Web requests • Usually not spoofed • If enough bots are used no client appears too aggressive • Really hard to filter since both traffic and client behavior seem identical between attackers and legitimate users

22. Reflector Attack • Generate service requests to public servers spoofing the victim’s IP • Servers reply back to the victim overwhelming it • Usually done for UDP and ICMP traffic (TCP SYN flood would only overwhelm CPU if huge number of packets is generated) • Often takes advantage of amplification effect – some service requests lead to huge replies; this lets attacker amplify his attack

23. Sample Research Defenses • Pushback • Traceback • SOS • Proof-of-work systems

24. Pushback1 1”Controlling high bandwidth aggregates in the network,” Mahajan, Bellovin, Floyd, Paxson, Shenker, ACM CCR, July 2002 • Goal: Preferentially drop attack traffic to relieve congestion • Local ACC: Enable core routers to respond to congestion locally by: • Profiling traffic dropped by RED • Identifying high-bandwidth aggregates • Preferentially dropping aggregate traffic to enforce desired bandwidth limit • Pushback: A router identifies the upstream neighbors that forward the aggregate traffic to it, requests that they deploy rate-limit

25. Can it Work? • Even a few core routers are able to control high-volume attacks • Separation of traffic aggregates improves current situation • Only traffic for the victim is dropped • Drops affect a portion containing the attack traffic • Likely to successfully control the attack, relieving congestion in the Internet • Will inflict collateral damage on legitimate traffic

26. Advantages and Limitations • Routers can handle high traffic volumes • Deployment at a few core routers can affectmany traffic flows, due to core topology • Simple operation, no overhead for routers • Pushback minimizes collateral damage by placing response close to the sources • Pushback only works in contiguous deployment • Collateral damage is inflicted by response, whenever attack is not clearly separable • Requires modification of existing core routers

27. Traceback1 1“Practical network support for IP Traceback,” Savage, Wetherall, Karlin, Anderson, ACM SIGCOMM 2000 • Goal: locate the agent machines • Each packet header may carry a mark, containing: • EdgeID(IP addresses of the routers) specifying an edge it has traversed • The distance from the edge • Routers mark packets probabilistically • If a router detects half-marked packet (containing only one IP address) it will complete the mark • Victim under attack reconstructs the path from the marked packets

28. Traceback and IP Spoofing • Tracebackdoes nothing to stop DDoS attacks • It only identifies attackers’ true locations • Comes to a vicinity of attacker • If IP spoofing were not possible in the Internet, traceback would not be necessary • There areother approaches to filter out spoofed traffic

29. Can it Work? • Incrementally deployable, a few disjoint routers can provide beneficial information • Moderate router overhead (packet modification) • A few thousand packets are needed even for long path reconstruction • Does not work well for highly distributed attacks • Path reassembly is computationally demanding, and is not 100% accurate: • Path information cannot be used for legal purposes • Routers close to the sources can efficiently block attack traffic, minimizing collateral damage

30. Advantages and Limitations • Incrementally deployable • Effective for non-distributed attacks and for highly overlapping attack paths • Facilitates locating routers close to the sources • Packet marking incurs overhead at routers, must be performed at slow path • Path reassembly is complex and prone to errors • Reassembly of distributed attack paths is prohibitively expensive

31. SOS1 1“ SOS: Secure Overlay Services,” Keromytis, Misra, Rubensteain, ACM SIGCOMM 2002 • Goal: route only “verified user” traffic to the server, drop everything else • Clients use overlay network to reach the server • Clients are authenticated at the overlay entrance, their packets are routed to proxies • Small set of proxies are “approved” to reach the server, all other traffic is heavily filtered out

32. SOS • User first contacts nodes that can check its legitimacy and let him access the overlay – access points • An overlay node uses Chord overlay routing protocol to send user’s packets to a beacon • Beacon sends packets to a secret servlet • Secret servlets tunnel packets to the firewall • Firewall only lets through packets with an IP of a secret servlet • Secret servlet’s identity has to be hidden, because their source address is a passport for the realm beyond the firewall • Beacons are nodes that know the identity of secret servlets • If a node fails, other nodes can take its role

33. Can It Work? • SOS successfully protects communication with a private server: • Access points can distinguish legitimate from attack communications • Overlay protects traffic flow • Firewall drops attack packets • Redundancy in the overlay and secrecy of the path to the target provide security against DoS attacks on SOS

34. Advantages And Limitations • Ensures communication of “verified user” with the victim • Resilient to overlay node failure • Resilient to DoS on the defense system • Does not work for public service • Traffic routed through the overlay travels on suboptimal path • Brute force attack on links leading to the firewall still possible

35. Client Puzzles1 1“Client puzzles: A cryptographic countermeasure against connection depletion attacks,” Juels, Brainard, NDSS 1999 • Goal: defend against connection depletion attacks • When under attack: • Server distributes small cryptographic puzzles to clients requesting service • Clients spend resources to solve the puzzles • Correct solution, submitted on time, leads to state allocation and connection establishment • Non-validated connection packets are dropped • Puzzle generation is stateless • Client cannot reuse puzzle solutions • Attacker cannot make use of intercepted packets

36. Can It Work? • Client puzzles guarantee that each client has spent a certain amount of resources • Server determines the difficulty of the puzzle according to its resource consumption • Effectively server controls its resource consumption • Protocol is safe against replay or interception attacks • Other flooding attacks will still work

37. Advantages And Limitations • Forces the attacker to spend resources, protects server resources from depletion • Attacker can only generate a certain number of successful connections from one agent machine • Low overhead on server • Requires client modification • Will not work against highly distributed attacks • Will not work against bandwidth consumption attacks (Defense By Offense paper changes this)