Network worms are a clear and growing threat to the security of today’s Internet-connected hosts and networks. The combination of the Internet’s unrestricted connectivity and widespread software homogeneity allows network pathogens to exploit tremendous parallelism in their propagation. In fact, modern worms can spread so quickly, and so widely, that no human-mediated reaction can hope to contain an outbreak. In this paper, we propose an automated approach for quickly detecting previously unknown worms and viruses based on two key behavioral characteristics – a common exploit sequence together with a range of unique sources generating infections and destinations being targeted. More importantly, our approach – called “content sifting ” – automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. Using a combination of existing and novel algorithms we have developed a scalable content sifting implementation with low memory and CPU requirements. Over months of active use at UCSD, our Earlybird prototype system has automatically detected and generated signatures for all pathogens known to be active on our network as well as for several new worms and viruses which were unknown at the time our system identified them. Our initial experience suggests that, for a wide range of network pathogens, it may be practical to construct fully automated defenses – even against so-called “zero-day” epidemics. 1

Pushback is a mechanism for defending against distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestion-control problem, but because most such congestion is caused by malicious hosts not obeying traditional end-to-end congestion control, the problem must be handled by the routers. Functionality is added to each router to detect and preferentially drop packets that probably belong to an attack. Upstream routers are also notified to drop such packets (hence the term Pushback) in order that the router's resources be used to route legitimate traffic. In this paper we present an architecture for Pushback, its implementation under FreeBSD, and suggestions for how such a system can be implemented in core routers.

angelos,misra,danr¥ Denial of service (DoS) attacks continue to threaten the reliability of networking systems. Previous approaches for protecting networks from DoS attacks are reactive in that they wait for an attack to be launched before taking appropriate measures to protect the network. This leaves the door open for other attacks that use more sophisticated methods to mask their traffic. We propose an architecture called Secure Overlay Services (SOS) that proactively prevents DoS attacks, geared toward supporting Emergency Services or similar types of communication. The architecture is constructed using a combination of secure overlay tunneling, routing via consistent hashing, and filtering. We reduce the probability of successful attacks by (i) performing intensive filtering near protected network edges, pushing the attack point perimeter into the core of the network, where high-speed routers can handle the volume of attack traffic, and (ii) introducing randomness and anonymity into the architecture, making it difficult for an attacker to target nodes along the path to a specific SOS-protected destination. Using simple analytical models, we evaluate the likelihood that an attacker can successfully launch a DoS attack against an SOSprotected network. Our analysis demonstrates that such an architecture reduces the likelihood of a successful attack to minuscule levels.

Distributed denial-of-service (DDoS) is a rapidly growing problem. The multitude and variety of both the attacks and the defense approaches is overwhelming. This paper presents two taxonomies for classifying attacks and defenses, and thus provides researchers with a better understanding of the problem and the current solution space. The attack classification criteria was selected to highlight commonalities and important features of attack strategies, that define challenges and dictate the design of countermeasures. The defense taxonomy classifies the body of existing DDoS defenses based on their design decisions; it then shows how these decisions dictate the advantages and deficiencies of proposed solutions.

Network intrusions have been a fact of life in the Internet for many years. However, as is the case with many other types of Internet-wide phenomena, gaining insight into the global characteristics of intrusions is challenging. In this paper we address this problem by systematically analyzing a set of firewall logs collected over four months from over 1600 different networks world wide. The first part of our study is a general analysis focused on the issues of distribution, categorization and prevalence of intrusions. Our data shows both a large quantity and wide variety of intrusion attempts on a daily basis. We also find that worms like CodeRed, Nimda and SQL Snake persist long after their original release. By projecting intrusion activity as seen in our data sets to the entire Internet we determine that there are typically on the order of 25B intrusion attempts per day and that there is an increasing trend over our measurement period. We further find that sources of intrusions are uniformly spread across the Autonomous System space. However, deeper investigation reveals that a very small collection of sources are responsible for a significant fraction of intrusion attempts in any given month and their on/off patterns exhibit cliques of correlated behavior. We show that the distribution of source IP addresses of the non-worm intrusions as a function of the number of attempts follows Zipf's law. We also find that at daily timescales, intrusion targets often depict significant spatial trends that blur patterns observed from individual "IP telescopes"; this underscores the necessity for a more global approach to intrusion detection. Finally, we investigate the benefits of shared information, and the potential for using this as a foundation for an automated, global intrus...

Abstract – Recent denial of service attacks are mounted by professionals using Botnets of tens of thousands of compromised machines. To circumvent detection, attackers are increasingly moving away from bandwidth floods to attacks that mimic the Web browsing behavior of a large number of clients, and target expensive higher-layer resources such as CPU, database and disk bandwidth. The resulting attacks are hard to defend against using standard techniques, as the malicious requests differ from the legitimate ones in intent but not in content. We present the design and implementation of Kill-Bots, a kernel extension to protect Web servers against DDoS attacks that masquerade as flash crowds. Kill-Bots provides authentication using graphical tests but is different from other systems that use graphical tests. First, Kill-Bots uses an intermediate stage to identify the IP addresses that ignore the test, and persistently bombard the server with requests despite repeated failures at solving the tests. These machines are bots because their intent is to congest the server. Once these machines are identified, Kill-Bots blocks their requests, turns the graphical tests off, and allows access to legitimate users who are unable or unwilling to solve graphical tests. Second, Kill-Bots sends a test and checks the client’s answer without allowing unauthenticated clients access to sockets, TCBs, and worker processes. Thus, it protects the authentication mechanism from being DDoSed. Third, Kill-Bots combines authentication with admission control. As a result, it improves performance, regardless of whether the server overload is caused by DDoS or a true Flash Crowd. 1

As national infrastructure becomes intertwined with emerging global data networks, the stability and integrity of the two have become synonymous. This connection, while necessary, leaves network assets vulnerable to the rapidly moving threats of today’s Internet, including fast moving worms, distributed denial of service attacks, and routing exploits. This paper introduces the Internet Motion Sensor (IMS), a globally scoped Internet monitoring system whose goal is to measure, characterize, and track threats. The IMS architecture is based on three novel components. First, a Distributed Monitoring Infrastructure increases visibility into global threats. Second, a Lightweight Active Responder provides enough interactivity that traffic on the same service can be differentiated independent of application semantics. Third, a Payload Signatures and Caching mechanism avoids recording duplicated payloads, reducing overhead and assisting in identifying new and unique payloads. We explore the architectural tradeoffs of this system in the context of a 3 year deployment across multiple dark address blocks ranging in size from /24s to a /8. These sensors represent a range of organizations and a diverse sample of the routable IPv4 space including nine of all routable /8 address ranges. Data gathered from these deployments is used to demonstrate the ability of the IMS to capture and characterize several important Internet threats: the Blaster worm (August 2003), the Bagle backdoor scanning efforts

We present Shadow Honeypots, a novel hybrid architecture that combines the best features of honeypots and anomaly detection. At a high level, we use a variety of anomaly detectors to monitor all traffic to a protected network/service. Traffic that is considered anomalous is processed by a “shadow honeypot ” to determine the accuracy of the anomaly prediction. The shadow is an instance of the protected software that shares all internal state with a regular (“production”) instance of the application, and is instrumented to detect potential attacks. Attacks against the shadow are caught, and any incurred state changes are discarded. Legitimate traffic that was misclassified will be validated by the shadow and will be handled correctly by the system transparently to the end user. The outcome of processing a request by the shadow is used to filter future attack instances and could be used to update the anomaly detector. Our architecture allows system designers to fine-tune systems for performance, since false positives will be filtered by the shadow. Contrary to regular honeypots, our architecture can be used both for server and client applications. We demonstrate the feasibility of our approach in a proof-of-concept implementation of the Shadow Honeypot architecture for the Apache web server and the Mozilla Firefox browser. We show that despite a considerable overhead in the instrumentation of the shadow honeypot (up to 20 % for Apache), the overall impact on the system is diminished by the ability to minimize the rate of false-positives. 1

The LOCKSS project has developed and deployed in a worldwide test a peer-to-peer system for preserving access to journals and other archival information published on the Web. It consists of a large number of independent, low-cost, persistent web caches that cooperate to detect and repair damage to their content by voting in "opinion polls." Based on this experience, we present a design for and simulations of a novel protocol for voting in systems of this kind. It incorporates rate limitation and intrusion detection to ensure that even some very powerful adversaries attacking over many years have only a small probability of causing irrecoverable damage before being detected.

We present a new kind of network perimeter monitoring strategy, which focuses on recognizing the infection and coordination dialog that occurs during a successful malware infection. BotHunter is an application designed to track the two-way communication flows between internal assets and external entities, developing an evidence trail of data exchanges that match a state-based infection sequence model. BotHunter consists of a correlation engine that is driven by three malware-focused network packet sensors, each charged with detecting specific stages of the malware infection process, including inbound scanning, exploit usage, egg downloading, outbound bot coordination dialog, and outbound attack propagation. The BotHunter correlator then ties together the dialog trail of inbound intrusion alarms with those outbound communication patterns that are highly indicative of successful local host infection. When a sequence of evidence is found to match BotHunter’s infection dialog model, a consolidated report is produced to capture all the relevant events and event sources that played a role during the infection process. We refer to this analytical strategy of matching the dialog flows between internal assets and the broader Internet as dialog-based correlation, and contrast this strategy to other intrusion detection and alert correlation methods. We present our experimental results using BotHunter in both virtual and live testing environments, and discuss our Internet release of the BotHunter prototype. BotHunter is made available both for operational use and to help stimulate research in understanding the life cycle of malware infections.