An Efficient Filter for Denial-of-Service Bandwidth Attacks

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1 An Efficient Filter for Denial-of-Service Bandwidth Attacks Samuel Abdelsayed, David Glimsholt, Christopher Leckie, Simon Ryan and Samer Shami Department of Electrical and Electronic Engineering ARC Special Research Centre for Ultra-Broadband Information Networks Department of Computer Science and Software Engineering, The University of Melbourne, Parkville 3010 Australia Telstra Research Laboratories, 770 Blackburn Road, Clayton 3186, Australia Abstract In this paper, we present an efficient method for detecting and filtering denial-of-service bandwidth attacks. Our system called TOPS (Tabulated Online Packet Statistics) can monitor a large number of network addresses in a compact, fixed-size structure using several effective heuristics. We demonstrate that TOPS can detect bandwidth attacks in a standard benchmark dataset with a high accuracy and a low false alarm rate. A key benefit of TOPS is that it uses few computational resources and does not slow down during an attack. I. INTRODUCTION Bandwidth attacks are a form of denial-of-service attack that aim to disrupt a network service by consuming large amounts of network or server capacity [2]. These attacks usually involve traffic from a large number sources that use fake source IP addresses (known as spoofing ). The ease with which source addresses can be spoofed makes it extremely difficult to identify and trace the source of malicious traffic. Since bandwidth attacks have caused expensive disruptions to many high-profile Internet sites [4], there is a pressing need to develop cost-effective defences against these attacks. In this paper, we present an efficient filter called TOPS (Tabulated Online Packet Statistics) for defending against bandwidth attacks. Numerous approaches have been proposed for dealing with bandwidth attacks. Ingress filtering [3] tries to stop attacks near their source by checking that outgoing packets from a sub-network have a source address that is consistent with that sub-network. An alternative approach is to trace the source of attack traffic by using packet marking to reconstruct the attack path [9, 10, 12, 13]. Once an attack has been detected, pushback techniques [6] have been proposed to insert packet filters near the sources of the attack. However, a significant issue with all of these approaches is that a considerable amount of network infrastructure will need to support these techniques in order to obtain their full benefit. An alternative approach is to deploy filters that use heuristic rules to analyse local traffic and detect suspicious packets. For example, history-based filtering [11] can be used near a server to discard packets from previously unseen IP addresses during an attack, based on the heuristic that these new sources are likely to have been spoofed. In this paper, we focus on an alternative heuristic introduced by Gil and Poletto [5], which looks for an imbalance between the incoming and outgoing traffic flows to or from each IP address. The intuition behind the flow imbalance heuristic is that the number of incoming packets and outgoing packets to a destination are evenly matched over a period of time under normal conditions. For example, each packet sent in a TCP connection is normally acknowledged. Similarly, the number of packets to and from a source should also be evenly matched. However, when a bandwidth attack occurs, the victim is unable to keep up with all the requested TCP connections. Consequently, the number of incoming packets will greatly exceed the number of outgoing packets. In the case of an attacking source, it is likely that the number of outgoing packets will exceed the number of incoming packets. This imbalance can be used to infer that an attack is occurring, and to identify the victim of the attack. Some protocols such as UDP and ICMP do not require an acknowledgement. However, even these protocols are often used in applications such as ICMP echo requests and real-time interactive UDP services where there is a balanced bi-directional flow. In order to make use of the flow imbalance heuristic, we need an efficient mechanism for monitoring the incoming and outgoing flow rates to a network address (victimoriented monitoring) or from a network address (attackeroriented monitoring). Depending on the monitoring mode and position, there is a potentially huge address space that needs to be monitored. An existing proposal for monitoring flow imbalances is called MULTOPS [5], which uses a four-level 256-ary tree to monitor the IP address space at successive levels of detail. Initially the MULTOPS tree monitors the traffic flow to large blocks of addresses using a single node at the top level. Each entry in the top level node corresponds to a block of addresses that all share the same first 8 bits of their address, e.g., 123.*.*.*. When a flow imbalance is detected in a specific address range, that address range is refined by adding a new node at the next level. This enables MULTOPS to monitor the flow balance within smaller address ranges, e.g., *.*, *.*, etc. At the bottom level of the tree, MULTOPS can monitor the flow for an individual IP address. A key feature of MULTOPS is that it reduces the amount

2 of memory required to monitor the IP address space. This is achieved by expanding only those nodes whose corresponding addresses have a flow imbalance. However, this dynamic memory management can be a computational burden for heavily loaded routers. The MULTOPS data structure can also become the subject of a memory exhaustion attack if a large number of nodes are expanded. In addition, there is a delay introduced when a node is expanded due to the monitoring time required to gather statistics for each new address range. In this paper, we present an efficient method for detecting and filtering denial-of-service bandwidth attacks, based on a novel implementation of the flow imbalance heuristic of Gil and Poletto [5]. Our system is called TOPS (Tabulated Online Packet Statistics). TOPS provides an efficient method for detecting packet flow imbalances by using a fixed set of compact tables to monitor the IP address space. Each table uses a different field in the IP address to map a given IP address to a table entry. Each table entry records the incoming and outgoing packet rate for the corresponding set of IP addresses. If there is an imbalance in the input and output flows for a particular IP address, then the corresponding entries in each table will indicate an anomaly. These abnormal table entries can then be used to identify packets that are part of an attack. The main contributions of this paper are the novel design of the TOPS monitoring scheme for detecting attacks, and an empirical evaluation of the effectiveness of TOPS and the flow imbalance heuristic for detecting attacks. We have found that TOPS can detect a high proportion of attack packets in a standard benchmark dataset with a low false positive rate. In particular, TOPS has several important advantages as a filter for bandwidth attacks: (1) it has a simple, static memory structure that can be efficiently incorporated into network routing hardware; (2) it can analyse a large volume of packets per second; and (3) it does not slow down or consume more resources when an attack is occurring. II. PROBLEM DEFINITION A wide variety of denial-of-service bandwidth attacks have been reported in the literature [7]. A common example is the SYN flood attack, where the attacker sends a large number of TCP SYN packets with a fake source address. These half-open TCP connections consume resources at the victim until they eventually time-out. These half-open connections can prevent legitimate users from connecting to the victim. Other examples include ICMP and UDP floods. In an ICMP flood, an ICMP echo request packet is sent to the broadcast address of a third party network, with the source address set to that of the victim. This causes hosts on the third party network to flood the victim with ICMP echo reply packets. In a UDP flood, a UDP packet is sent to the echo port of a third party, with the source set to the address and echo port of the victim. This causes an endless loop of packets that can overwhelm the victim, especially if several third parties are used. In each of these examples, there are more incoming packets than the victim can handle. In addition, network links and routers near the victim can become congested. Consequently, the victim is unable to reply to all the incoming packets, which causes an imbalance in the packet flow rate between the victim and the source of the traffic. Our aim is to recognise attacks by detecting an imbalance in the packet flow for a network address. Monitoring is usually performed at the router that connects the network to be monitored to the outside world. Let P in (A) and P out (A) denote the incoming and outgoing packet rate respectively for network address A in the monitored network. An imbalance in the packet flow for A is reflected in the traffic ratio R(A) = P in (A)/P out (A). In the victim-oriented mode of detection, we consider that A is the victim of an attack if R(A) > R max, where R max > 1 is the detection threshold. Similarly, in the attacker-oriented mode of detection, we consider that A is the source of an attack if R(A) < R min, where R min < 1 is the detection threshold. Once an attack has been detected, we can drop packets that are associated with the attacker or the victim. Attackeroriented detection checks whether one of the hosts in the monitored network is the source of an attack. We would like to detect and drop attack packets close to the source in order to minimise congestion in the wider network. However, if the attacker uses a large number of spoofed source addresses, then attacker-oriented detection can become less reliable. As a further defence we can use victim-oriented detection to check whether one of the hosts in our monitored network is the subject of an attack, in which case we can drop packets addressed to the victim. In both modes of operation, we need to maintain rate counters so that we can estimate the flow rate for each address in the monitored network. In victim-oriented mode, the address range depends on the size of the network, which can be significant for a large ISP. The same applies to attackeroriented mode if ingress filtering is used to filter outgoing packets whose source address does not match the address range of the monitored network. However, ingress filtering requires network managers to configure their routers with the range of legal addresses that belong to the monitored network. Since this is never guaranteed, attacker-oriented detection potentially needs to monitor the full IP address space of 2 32 addresses. We require a compact scheme for maintaining rate counters, which should not require significant memory or computational resources from a router. One approach is to use a hash table of records for active IP addresses. A drawback of this scheme is that memory is wasted on IP addresses that are behaving normally. MULTOPS uses a dynamic tree structure to maintain rate counters for address ranges at multiple levels of detail. This has the advantage that rate counters are aggregated together at the top level of the tree, and only refined at the lower levels of the tree when an abnormal flow ratio is detected. Nevertheless, both schemes use a dynamic data structure that is potentially vulnerable to memory exhaustion

3 Table 1 Table 2 Table 3 Example: into the system: (1) rate monitoring algorithms, (2) certainty thresholds, and (3) multi-protocol tables. Rate monitoring algorithm - We require an on-line algorithm to estimate the incoming and outgoing packet rates (P in and P out ) for the addresses corresponding to each table entry. We have used an exponentially weighted moving average scheme (EWMA) to estimate these rates. For a given table entry, let M in represent the number of packets that have arrived in the time interval W for addresses that are hashed to this table entry. We update P in as follows: Table P in = αm in + (1 α)p in where 0 < α < 1. Fig. 1. Example of TOPS table showing entries for IP address attacks. Our aim is to provide a simple, static monitoring structure that requires little computational overhead even when an attack occurs. III. THE TOPS MONITORING SCHEME Our monitoring scheme called TOPS is implemented as a set of fixed length tables. Each table T i, i = 1 N, is indexed by a separate hash function h i (A), where A is an IP address to be monitored. Each entry of a table contains two counters that are used to monitor the flow rate of the addresses that are mapped to that entry, i.e., T i (h i (A)).P in records the incoming rate and T i (h i (A)).P out records the outgoing rate. Each time a packet arrives for address A in the monitored network, we update T i (h i (A)).P in for i = 1 N. Similarly, when a packet leaves from address A, we update T i (h i (A)).P out for i = 1 N. This indexing scheme is similar to a Bloom filter [1], where an object is indexed using multiple hash functions. Although many addresses are mapped to the same entry in any one table, no two addresses are mapped to the same entries in all N tables. This approach requires far less memory than a complete map of the IP address space. It also uses a fixed memory allocation, which means that no dynamic memory management is required, and there is no risk of the TOPS data structure becoming the victim of a memory exhaustion attack. In practice, we use N = 4 tables, each with 256 entries. The i th hash function uses the i th octet of the IP address to index T i. Figure 1 shows an example, highlighting the table entries that correspond to the IP address TOPS is used to detect attacks by testing whether the traffic ratio for a given address is abnormal, i.e., R(A) > R max for victim-oriented mode or R(A) < R min for attackeroriented mode. An attack is flagged for an address A if at least K table entries for A indicate an abnormal traffic ratio. As a default, we have used K = 3. If a packet arrives to a monitored address in victim-oriented mode, and at least K tables indicate an abnormal traffic ratio for that address, then the packet is dropped. Similarly, packets from a monitored address in attacker-oriented mode are dropped if at least K tables indicate an abnormal traffic ratio. In order to ensure the accuracy of TOPS, several key features have been incorporated An equivalent expression is used for P out. Certainty threshold - An abnormal traffic ratio should only cause packets to be dropped if either the incoming or outgoing packet rate is very large. If the traffic rate is low, then an abnormal traffic ratio can be caused by random fluctuations in traffic. Consequently, we maintain a cumulative distribution of the probability of different packet rates to or from the target network. If the probability of the packet rate being greater than the current level x is less than a certainty threshold θ, then packets can be dropped, i.e., if P r(p in > x) < θ. Multi-protocol tables - So far we have a single set of tables to record the incoming and outgoing packet rates for all types of traffic. As mentioned earlier, many attacks involve only a single type of traffic, e.g., TCP, UDP or ICMP. By using a separate set of tables for each protocol we can monitor the packet rate of each traffic type separately. This means that we can more easily detect abnormal traffic ratios for an attack that uses a particular protocol. We introduce 4 additional sets of tables corresponding to TCP, UDP, ICMP and other protocols. Each set of tables requires 32 bytes/entry and 256 entries/table for 4 tables, which is a total of 32 kbytes. If we keep 4 sets of tables in order to monitor each protocol separately, then we require 128 kbytes. In addition, we require memory for the cumulative distribution of packet rates, which is used in testing the certainty threshold. If we use a single distribution for all traffic types, then we require 8 kbytes of memory to gather statistics for the distribution. If we maintain a separate distribution of packet rates for each protocol, then we require an additional 32 kbytes of memory. IV. EVALUATION We have evaluated our TOPS monitoring scheme using packet traces from the DARPA 1999 Intrusion Detection dataset [8]. This dataset provides labeled examples of different types of attacks in a test network that also carries normal traffic. Our aims are to investigate the accuracy of the flow imbalance heuristic for detecting DoS bandwidth attacks, and to test the effectiveness of our TOPS monitoring scheme as an implementation of the flow imbalance heuristic. In particular, we have evaluated (1) the accuracy of the flow imbalance heuristic, (2) the effectiveness of single vs multi-protocol TOPS, (3) the effectiveness of certainty thresholds, and (4) the analysis speed of TOPS.

4 Number of alarms (thousands) False alarms Day Single Multi-protocol Table Set Tables Wk 4 Mon 99.5% 99.9% Wk 4 Wed 81.2% 91.0% Wk 4 Fri 95.0% 90.9% Wk 5 Mon 37.6% 94.5% Wk 5 Tue 0% 97.0% Wk 5 Thu 0% 0.9% TABLE I PERCENTAGE OF ATTACK PACKETS DETECTED USING A SINGLE TABLE SET COMPARED TO MULTI-PROTOCOL TABLES 4 2 Real alarms 5.4 x Traffic Ratio (Rmax) Fig. 2. Number of real and false alarms raised when the traffic ratio threshold is varied (Week 4 Wednesday) We tested TOPS on weeks 4 and 5 of the DARPA dataset, which contain a wide variety of network attacks. Our goal is to detect attacks on the monitored network from external hosts. Two types of bandwidth attacks from external hosts appear in this dataset: ICMP floods and TCP SYN floods. A closer inspection of the TCP SYN floods showed that most SYN packets were acknowledged by the victim. Consequently, this type of low intensity TCP SYN flood could not be detected using the flow imbalance heuristic. In the rest of this evaluation we focus on the ICMP floods in the dataset, namely pod and smurf attacks. We refer to these as the base attack types that we should detect. We also note the presence of several other types of DoS attacks that involve abnormal packets rather than large traffic volumes. These attacks ( apache2 and back ) can involve a moderate number of packets and exhibit a flow imbalance. Although it is not our intention to detect these extra attack types, we consider their detection a fringe benefit. All testing was performed on an AMD Duron 1.0 GHz running Windows Flow imbalance heuristic - We evaluated the traffic ratio heuristic by varying the minimum traffic ratio R max that is needed to raise an alarm. We then recorded the number of real alarms (i.e., attack packets detected) and false alarms (i.e., normal packets classified as part of an attack) reported by TOPS. The results are shown in Figure 2 for Week 4 Wednesday of the DARPA data. Note that similar trends were observed on the other days. The number of false alarms is high when R max < 6. Increasing R max dramatically reduces the false alarm rate. Once R max 20, the flow imbalance heuristic is able to detect nearly all the real alarms with no false alarms being reported. Single vs multi-protocol tables - Table 1 shows the results of using a single set of tables in TOPS compared to using multiprotocol tables. We show the results for several days in the DARPA dataset. Those days not shown did not contain any Number of alarms Single table set Multi protocol tables 90% 99% 99.9% 99.99% % % Certainty threshold Fig. 3. Number of alarms raised when the certainty threshold is varied (Week 4 Monday) base attacks, and did not generate any alarms. Using TOPS with a single set of tables resulted in fewer attack packets being detected than when using four tables to monitor traffic by protocol. In general, the single table detected fewer attack packets because traffic to the victim using other protocols obscured the attack traffic rate. The exception is Week 4 Friday, which was due to a low outgoing rate of the other protocols at the time of the attack. Certainty threshold - Certainty thresholds aim to reduce false positives by filtering alarms when the flow rate is low. We can monitor the flow rate by protocol, or for all protocols aggregated together. A certainty threshold of 90% means that the flow rate must be in the top 10-percentile in order to raise an alarm. Figure 3 shows the number of alarms raised as we vary the certainty threshold on Week 4 Monday. When all protocols are aggregated together there is an almost linear reduction in alarms as the certainty threshold is increased. In contrast, there is no change in alarms when traffic is monitored by protocol. Moreover, we found that over all two weeks of test data we could eliminate all false positive without losing any real alarms for bandwidth attacks by using a certainty threshold of % when monitoring traffic by protocol. Analysis speed - Maximum accuracy was achieved with

5 TOPS when certainty thresholds were used and traffic was monitored by protocol. When TOPS is used in victim-oriented mode using multiple protocols, we measured an average processing speed of 527,000 packets per second using certainty thresholds, or 670,000 packets per second without certainty thresholds. If TOPS is used in both attacker and victim modes, then the average processing rate was 334,000 packets per second. Since we did not have an implementation of MULTOPS, we cannot make a direct comparison. However, MULTOPS was reported to process around 270,000 packets per second in victim-oriented mode using aggregated traffic monitoring. V. CONCLUSION In this paper we have presented a novel system based on the flow balance heuristic to detect and filter DoS bandwidth attacks. In particular, we have shown how a form of Bloom filter based on a static tabular memory structure can efficiently detect attacks using few computational resources. We have also shown how we can improve the accuracy and reduce the false alarm rate of our system by monitoring traffic by protocol, and maintaining a probability distribution of traffic flow rates. The efficiency and accuracy of TOPS makes it highly suited for implementation in routers. As an issue for further research, we intend to investigate how TOPS can adapt to changing traffic flow rates according to time of day, as well as for different network hosts. VI. ACKNOWLEDGEMENTS This work was supported by the Australian Research Council. The permission of the Chief Technology Officer of Telstra Corporation Limited to publish this paper is hereby acknowledged. [7] K. Kendall. A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems. Master s Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, [8] R. Lippmann, et al. Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation. In Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection (RAID 2000), pp [9] K. Park and H. Lee. On the effectiveness of probabilistic packet marking for IP traceback under denial of service attack. In Proceedings of IEEE INFOCOM 2001, April 2001, Anchorage, Alaska, USA, Vol. 1, pp [10] T. Peng, C. Leckie and R. Kotagiri. Adjusted Probabilistic Packet Marking for IP Traceback. In Proceedings of the Second IFIP Networking Conference (Networking 2002), May 2002, Pisa, Italy. [11] T. Peng, C. Leckie and R. Kotagiri. Protection from Distributed Denial of Service Attack Using History-based IP Filtering. To appear in the IEEE International Conference on Communications (ICC 2003), May 2003, Anchorage, Alaska, USA. [12] S. Savage, D. Wetherall, A. Karlin and T. Anderson. Network support for IP traceback. In IEEE/ACM Transactions on Networking, Vol. 9 No. 3, June 2001, pp [13] D. Song and A. Perrig. Advanced and authenticated marking schemes for IP traceback. In Proceedings of IEEE INFOCOM 2001, April 2001, Anchorage, Alaska, USA, Vol. 2, pp VII. REFERENCES [1] B. Bloom. Space/time trade-offs in hash coding with allowable errors. In Communications of the ACM, Vol. 13 No. 7, July 1970, pp [2] R. Chang. Defending against flooding-based distributed denial-of-service attacks: a tutorial. In IEEE Communications Magazine, Vol. 40 No. 10, Oct 2002, pp [3] P. Ferguson and D. Senie. Network Ingress Filtering: Defeating Denial of Service Attacks which employ IP Source Address Spoofing. RFC2827, May 2000, [4] L. Garber. Denial-of-service attacks rip the internet. In IEEE Computer, Vol. 33 No. 4, April 2000, pp [5] T. Gil and M. Poletto. MULTOPS: a data-structure for bandwidth attack detection. In Proceedings of the 10th USENIX Security Symposium, August 2001, Washington D.C., USA. [6] J. Ioannidis and S. Bellovin. Implementing Pushback: Router-Based Defense Against DDoS Attacks. In Proceedings of the Network and Distributed System Security Symposium (NDSS 2002), February 2002.

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