In this paper, we propose a BGP anomaly detection framework called BAlet that delivers both temporal and spatial localization of the potential anomalies. It requires only a simple count of BGP update messages collected over a certain period. We first investigate the self-similarity in BGP update traffic and present a quantitative validation. The strength of wavelet analysis in handling signals with scaling property and earlier success in applying it for network anomaly detection motivate us to apply the same technique on BGP routing traffic. Later by clustering the anomalies detected at different locations, BAlet is capable of identifying possible network-wide anomalous events. Our method does not rely on any information within the BGP messages, and serves as a complementary tool to reduce the candidate data set for further detailed root cause analysis. We evaluate BAlet on real BGP data sets that are known to contain anomalies. Results show that it is capable of detecting network-wide events such as message volume surges caused by slammer worm attack, and separating affected ASes from the rest.

Sampling techniques are widely used for traffic measurements at high link speed to conserve router resources. Traditionally, sampled traffic data is used for network management tasks such as traffic matrix estimations, but recently it has also been used in numerous anomaly detection algorithms, as security analysis becomes increasingly critical for network providers. While the impact of sampling on traffic engineering metrics such as flow size and mean rate is well studied, its impact on anomaly detection remains an open question. This paper presents a comprehensive study on whether existing sampling techniques distort traffic features critical for effective anomaly detection. We sampled packet traces captured from a Tier-1 IP-backbone using four popular methods: random packet sampling, random flow sampling, smart sampling, and sample-and-hold. The sampled data is then used as input to detect two common classes of anomalies: volume anomalies and port scans. Since it is infeasible to enumerate all existing solutions, we study three representative algorithms: a wavelet-based volume anomaly detection and two portscan detection algorithms based on hypotheses testing. Our results show that all the four sampling methods introduce fundamental bias that degrades the performance of the three detection schemes, however the degradation curves are very different. We also identify the traffic features critical for anomaly detection and analyze how they are affected by sampling. Our work demonstrates the need for better measurement techniques, since anomaly detection operates on a drastically different information region, which is often overlooked by existing traffic accounting methods that target heavy-hitters.

Security concerns are becoming increasingly critical in networked systems. Firewalls provide important defense for network security. However, misconfigurations in firewalls are very common and significantly weaken the desired security. This paper introduces FIREMAN, a static analysis toolkit for firewall modeling and analysis. By treating firewall configurations as specialized programs, FIREMAN applies static analysis techniques to check misconfigurations, such as policy violations, inconsistencies, and inefficiencies, in individual firewalls as well as among distributed firewalls. FIREMAN performs symbolic model checking of the firewall configurations for all possible IP packets and along all possible data paths. It is both sound and complete because of the finite state nature of firewall configurations. FIREMAN is implemented by modeling firewall rules using binary decision diagrams (BDDs), which have been used successfully in hardware verification and model checking. We have experimented with FIREMAN and used it to uncover several real misconfigurations in enterprise networks, some of which have been subsequently confirmed and corrected by the administrators of these networks.

Packet sampling is commonly deployed in high-speed backbone routers to minimize resources used for network monitoring. It is known that packet sampling distorts traffic statistics and its impact has been extensively studied for traffic engineering metrics such as flow size and mean rate. However, it is unclear how packet sampling impacts anomaly detection, which has become increasingly critical to network providers. This paper is the first attempt to address this question by focusing on one common class of nonvolume-based anomalies, portscans , which are associated with worm/virus propagation. Existing portscan detection algorithms fall into two general approaches: target-specific and traffic profiling. We evaluated representative algorithms for each class, namely: 1) TRWSYN that performs stateful traffic analysis; 2) TAPS that tracks connection pattern of scanners; and 3) entropy-based traffic profiling. We applied these algorithms to detect portscans in both the original and sampled packet traces from a Tier-1 provider's backbone network. Our results demonstrate that sampling introduces fundamental bias that degrades the effectiveness of these detection algorithms and dramatically increases false positives. Through both experiments and analysis, we identify the traffic features critical for anomaly detection that are affected by sampling. Finally, using insight gained from this study, we show how portscan algorithms can be enhanced to be more robust to sampling.