L. Feinstein

Bio: L. Feinstein is an academic researcher. The author has contributed to research in topics: Application layer DDoS attack & Denial-of-service attack. The author has an hindex of 2, co-authored 2 publications receiving 546 citations.

Statistical approaches to DDoS attack detection and response

Abstract: The nature of the threats posed by distributed denial of service (DDoS) attacks on large networks, such as the Internet, demands effective detection and response methods. These methods must be deployed not only at the edge but also at the core of the network This paper presents methods to identify DDoS attacks by computing entropy and frequency-sorted distributions of selected packet attributes. The DDoS attacks show anomalies in the characteristics of the selected packet attributes. The detection accuracy and performance are analyzed using live traffic traces from a variety of network environments ranging from points in the core of the Internet to those inside an edge network The results indicate that these methods can be effective against current attacks and suggest directions for improving detection of more stealthy attacks. We also describe our detection-response prototype and how the detectors can be extended to make effective response decisions.

DDoS tolerant networks

Abstract: The nature of the threats posed by distributed denial of service (DDoS) attacks on large networks, such as the Internet, demands effective detection and response methods. These methods must be deployed not only at the edge but also at the core of the network. The DDoS tolerant networks technology incorporates methods to detect, characterize, and respond to DDoS attacks by computing entropy and frequency-sorted distributions of selected packet attributes. Preliminary results indicate that these methods can be effective against current attacks and suggest directions for improving detection of more stealthy attacks.

Mining anomalies using traffic feature distributions

Abstract: The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

Eviction of Misbehaving and Faulty Nodes in Vehicular Networks

Abstract: Vehicular networks (VNs) are emerging, among civilian applications, as a convincing instantiation of the mobile networking technology. However, security is a critical factor and a significant challenge to be met. Misbehaving or faulty network nodes have to be detected and prevented from disrupting network operation, a problem particularly hard to address in the life-critical VN environment. Existing networks rely mainly on node certificate revocation for attacker eviction, but the lack of an omnipresent infrastructure in VNs may unacceptably delay the retrieval of the most recent and relevant revocation information; this will especially be the case in the early deployment stages of such a highly volatile and large-scale system. In this paper, we address this specific problem. We propose protocols, as components of a framework, for the identification and local containment of misbehaving or faulty nodes, and then for their eviction from the system. We tailor our design to the VN characteristics and analyze our system. Our results show that the distributed approach to contain nodes and contribute to their eviction is efficiently feasible and achieves a sufficient level of robustness.

Denial-of-service attack-detection techniques

Abstract: Denial-of-service (DoS) detection techniques - such as activity profiling, change-point detection, and wavelet-based signal analysis - face the considerable challenge of discriminating network-based flooding attacks from sudden increases in legitimate activity or flash events. This survey of techniques and testing results provides insight into our ability to successfully identify DoS flooding attacks. Although each detector shows promise in limited testing, none completely solve the detection problem. Combining various approaches with experienced network operators most likely produce the best results.

Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics

Abstract: A low-rate distributed denial of service (DDoS) attack has significant ability of concealing its traffic because it is very much like normal traffic. It has the capacity to elude the current anomaly-based detection schemes. An information metric can quantify the differences of network traffic with various probability distributions. In this paper, we innovatively propose using two new information metrics such as the generalized entropy metric and the information distance metric to detect low-rate DDoS attacks by measuring the difference between legitimate traffic and attack traffic. The proposed generalized entropy metric can detect attacks several hops earlier (three hops earlier while the order α = 10 ) than the traditional Shannon metric. The proposed information distance metric outperforms (six hops earlier while the order α = 10) the popular Kullback-Leibler divergence approach as it can clearly enlarge the adjudication distance and then obtain the optimal detection sensitivity. The experimental results show that the proposed information metrics can effectively detect low-rate DDoS attacks and clearly reduce the false positive rate. Furthermore, the proposed IP traceback algorithm can find all attacks as well as attackers from their own local area networks (LANs) and discard attack traffic.

An empirical evaluation of entropy-based traffic anomaly detection

Abstract: Entropy-based approaches for anomaly detection are appealing since they provide more fine-grained insights than traditional traffic volume analysis. While previous work has demonstrated the benefits of entropy-based anomaly detection, there has been little effort to comprehensively understand the detection power of using entropy-based analysis of multiple traffic distributions in conjunction with each other. We consider two classes of distributions: flow-header features (IP addresses, ports, and flow-sizes), and behavioral features (degree distributions measuring the number of distinct destination/source IPs that each host communicates with). We observe that the timeseries of entropy values of the address and port distributions are strongly correlated with each other and provide very similar anomaly detection capabilities. The behavioral and flow size distributions are less correlated and detect incidents that do not show up as anomalies in the port and address distributions. Further analysis using synthetically generated anomalies also suggests that the port and address distributions have limited utility in detecting scan and bandwidth flood anomalies. Based on our analysis, we discuss important implications for entropy-based anomaly detection.