Abstract not found

Abstract not found

One widely-used technique by which network attackers attain anonymity and complicate their apprehension is by employing stepping stones: they launch attacks not from their own computer but from intermediary hosts that they previously compromised. We develop an efficient algorithm for detecting stepping stones by monitoring a site’s Internet access link. The algorithm is based on the distinctive characteristics (packet size, timing) of interactive traffic, and not on connection contents, and hence can be used to find stepping stones even when the traffic is encrypted. We evaluate the algorithm on large Internet access traces and find that it performs quite well. However, the success of the algorithm is tempered by the discovery that large sites have many users who routinely traverse stepping stones for a variety of legitimate reasons. Hence, stepping-stone detection also requires a significant policy component for separating allowable stepping-stone pairs from surreptitious access.

A denial-of-service bandwidth attack is an attempt to disrupt an online service by generating a traffic overload that clogs links or causes routers near the victim to crash. We propose a heuristic and a datastructure that network devices (such as routers) can use to detect (and eliminate) such attacks. With our method, each network device maintains a datastructure, MULTOPS , that monitors certain traffic characteristics. MULTOPS (MUlti-Level Tree for Online Packet Statistics) is a tree of nodes that contains packet rate statistics for subnet prefixes at different aggregation levels. The tree expands and contracts within a fixed memory budget.

A critical problem faced by a Network Intrusion Detection System (NIDS) is that of ambiguity. The NIDS cannot always determine what traffic reaches a given host nor how that host will interpret the traffic, and attackers may exploit this ambiguity to avoid detection or cause misleading alarms. We present a novel, lightweight solution, Active Mapping, which eliminates TCP/IP-based ambiguity in a NIDS' analysis with minimal runtime cost. Active Mapping efficiently builds profiles of the network topology and the TCP/IP policies of hosts on the network; a NIDS may then use the host profiles to disambiguate the interpretation of the network traffic on a per-host basis. Active Mapping avoids the semantic and performance problems of traffic normalization, in which traffic streams are modified to remove ambiguities.

The monitoring of unused Internet address space has been shown to be an effective method for characterizing Internet threats including Internet worms and DDOS attacks. Because there are no legitimate hosts in an unused address block, traffic must be the result of misconfiguration, backscatter from spoofed source addresses, or scanning from worms and other probing. This paper extends previous work characterizing traffic seen at specific unused address blocks by examining differences observed between these blocks. While past research has attempted to extrapolate the results from a small number of blocks to represent global Internet traffic, we present evidence that distributed address blocks observe dramatically different traffic patterns. This work uses a network of blackhole sensors which are part of the Internet Motion Sensor (IMS) collection infrastructure. These sensors are deployed in networks belonging to service providers, large enterprises, and academic institutions representing a diverse sample of the IPv4 address space. We demonstrate differences in traffic observed along three dimensions: over all protocols and services, over a specific protocol and service, and over a particular worm signature. This evidence is then combined with additional experimentation to build a list of sensor properties providing plausible explanations for these differences. Using these properties, we conclude with recommendations for better understanding the implications of sensor placement.

An alarming trend in malware attacks is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based anti-malware systems is that they run inside the very hosts they are protecting (“in the box”), making them vulnerable to counter-detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM (“out of the box”). However, they gain tamper resistance at the cost of losing the native, semantic view of the host which is enjoyed by the “in the box” approach, thus leading to a technical challenge known as the semantic gap. In this paper, we present the design, implementation, and evaluation of VMwatcher – an “out-of-the-box” approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to systematically reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM from the outside in a non-intrusive manner. Specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. With the semantic gap bridged, we identify two unique malware detection capabilities: (1) view comparison-based malware detection and its demonstration in rootkit detection and (2) “out-of-the-box” deployment of hostbased anti-malware software with improved detection accuracy and tamper-resistance. We have implemented a proof-of-concept prototype on both Linux and Windows platforms and our experimental results with real-world malware, including elusive kernel-level rootkits, demonstrate its practicality and effectiveness.

In this paper we present a new approach for network intrusion detection based on concise specifications that characterize normal and abnormal network packet sequences. Our specification language is geared for a robust network intrusion detection by enforcing a strict type discipline via a combination of static and dynamic type checking. Unlike most previous approaches in network intrusion detection, our approach can easily support new network protocols as information relating to the protocols are not hard-coded into the system. Instead, we simply add suitable type definitions in the specifications and define intrusion patterns on these types. We compile these specifications into a highperformance network intrusion detection system. Important components of our approach include efficient algorithms for patternmatching and information aggregation on sequences of network packets. In particular, our techniques ensure that the matching time is insensitive to the number of patterns characterizing different network intrusions, and that the aggregation operations typically take constant time per packet. Our system participated in an intrusion detection evaluation organized by MIT Lincoln Labs, where our system demonstrated its effectiveness (96% detection rate on low-level network attacks) and performance (real-time detection at 500Mbps), while producing very few false positives (0.05 to 0.1 per attack).

Abstract—Alert correlation is a process that analyzes the alerts produced by one or more intrusion detection systems and provides a more succinct and high-level view of occurring or attempted intrusions. Even though the correlation process is often presented as a single step, the analysis is actually carried out by a number of components, each of which has a specific goal. Unfortunately, most approaches to correlation concentrate on just a few components of the process, providing formalisms and techniques that address only specific correlation issues. This paper presents a general correlation model that includes a comprehensive set of components and a framework based on this model. A tool using the framework has been applied to a number of well-known intrusion detection data sets to identify how each component contributes to the overall goals of correlation. The results of these experiments show that the correlation components are effective in achieving alert reduction and abstraction. They also show that the effectiveness of a component depends heavily on the nature of the data set analyzed. Index Terms—Intrusion detection, alert correlation, alert reduction, correlation data sets. 1

Backdoors are often installed by attackers who have compromised a system to ease their subsequent return to the system. We consider the problem of identifying a large class of backdoors, namely those providing interactive access on nonstandard ports, by passively monitoring a site’s Internet access link. We develop a general algorithm for detecting interactive traffic based on packet size and timing characteristics, and a set of protocol-specific algorithms that look for signatures distinctive to particular protocols. We evaluate the algorithms on large Internet access traces and find that they perform quite well. In addition, some of the algorithms are amenable to prefiltering using a stateless packet filter, which yields a major performance increase at little or no loss of accuracy. However, the success of the algorithms is tempered by the discovery that large sites have many users who routinely access what are in fact benign backdoors, such as servers running on non-standard ports not to hide, but for mundane administrative reasons. Hence, backdoor detection also requires a significant policy component for separating allowable backdoor access from surreptitious access. 1