461 research outputs found
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Recent years have seen the rise of more sophisticated attacks including
advanced persistent threats (APTs) which pose severe risks to organizations and
governments by targeting confidential proprietary information. Additionally,
new malware strains are appearing at a higher rate than ever before. Since many
of these malware are designed to evade existing security products, traditional
defenses deployed by most enterprises today, e.g., anti-virus, firewalls,
intrusion detection systems, often fail at detecting infections at an early
stage.
We address the problem of detecting early-stage infection in an enterprise
setting by proposing a new framework based on belief propagation inspired from
graph theory. Belief propagation can be used either with "seeds" of compromised
hosts or malicious domains (provided by the enterprise security operation
center -- SOC) or without any seeds. In the latter case we develop a detector
of C&C communication particularly tailored to enterprises which can detect a
stealthy compromise of only a single host communicating with the C&C server.
We demonstrate that our techniques perform well on detecting enterprise
infections. We achieve high accuracy with low false detection and false
negative rates on two months of anonymized DNS logs released by Los Alamos
National Lab (LANL), which include APT infection attacks simulated by LANL
domain experts. We also apply our algorithms to 38TB of real-world web proxy
logs collected at the border of a large enterprise. Through careful manual
investigation in collaboration with the enterprise SOC, we show that our
techniques identified hundreds of malicious domains overlooked by
state-of-the-art security products
Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring
system whose goal is to measure, detect, characterize, and track threats such
as distribute denial of service(DDoS) attacks and worms. To block the
monitoring system in the internet the attackers are targeted the ITM system. In
this paper we address flooding attack against ITM system in which the attacker
attempt to exhaust the network and ITM's resources, such as network bandwidth,
computing power, or operating system data structures by sending the malicious
traffic. We propose an information-theoretic frame work that models the
flooding attacks using Botnet on ITM. Based on this model we generalize the
flooding attacks and propose an effective attack detection using Honeypots
Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences
In this survey, we first briefly review the current state of cyber attacks,
highlighting significant recent changes in how and why such attacks are
performed. We then investigate the mechanics of malware command and control
(C2) establishment: we provide a comprehensive review of the techniques used by
attackers to set up such a channel and to hide its presence from the attacked
parties and the security tools they use. We then switch to the defensive side
of the problem, and review approaches that have been proposed for the detection
and disruption of C2 channels. We also map such techniques to widely-adopted
security controls, emphasizing gaps or limitations (and success stories) in
current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages.
Listing abstract compressed from version appearing in repor
Fast-Flux Botnet Detection Based on Traffic Response and Search Engines Credit Worthiness
Botnets are considered as the primary threats on the Internet and there have been many research efforts to detect and mitigate them. Today, Botnet uses a DNS technique fast-flux to hide malware sites behind a constantly changing network of compromised hosts. This technique is similar to trustworthy Round Robin DNS technique and Content Delivery Network (CDN). In order to distinguish the normal network traffic from Botnets different techniques are developed with more or less success. The aim of this paper is to improve Botnet detection using an Intrusion Detection System (IDS) or router. A novel classification method for online Botnet detection based on DNS traffic features that distinguish Botnet from CDN based traffic is presented. Botnet features are classified according to the possibility of usage and implementation in an embedded system. Traffic response is analysed as a strong candidate for online detection. Its disadvantage lies in specific areas where CDN acts as a Botnet. A new feature based on search engine hits is proposed to improve the false positive detection. The experimental evaluations show that proposed classification could significantly improve Botnet detection. A procedure is suggested to implement such a system as a part of IDS
Scalable Techniques for Anomaly Detection
Computer networks are constantly being attacked by malicious entities for various reasons. Network based attacks include but are not limited to, Distributed Denial of Service (DDoS), DNS based attacks, Cross-site Scripting (XSS) etc. Such attacks have exploited either the network protocol or the end-host software vulnerabilities for perpetration. Current network traffic analysis techniques employed for detection and/or prevention of these anomalies suffer from significant delay or have only limited scalability because of their huge resource requirements. This dissertation proposes more scalable techniques for network anomaly detection.
We propose using DNS analysis for detecting a wide variety of network anomalies. The use of DNS is motivated by the fact that DNS traffic comprises only 2-3% of total network traffic reducing the burden on anomaly detection resources. Our motivation additionally follows from the observation that almost any Internet activity (legitimate or otherwise) is marked by the use of DNS. We propose several techniques for DNS traffic analysis to distinguish anomalous DNS traffic patterns which in turn identify different categories of network attacks.
First, we present MiND, a system to detect misdirected DNS packets arising due to poisoned name server records or due to local infections such as caused by worms like DNSChanger. MiND validates misdirected DNS packets using an externally collected database of authoritative name servers for second or third-level domains. We deploy this tool at the edge of a university campus network for evaluation.
Secondly, we focus on domain-fluxing botnet detection by exploiting the high entropy inherent in the set of domains used for locating the Command and Control (C&C) server. We apply three metrics namely the Kullback-Leibler divergence, the Jaccard Index, and the Edit distance, to different groups of domain names present in Tier-1 ISP DNS traces obtained from South Asia and South America. Our evaluation successfully detects existing domain-fluxing botnets such as Conficker and also recognizes new botnets. We extend this approach by utilizing DNS failures to improve the latency of detection. Alternatively, we propose a system which uses temporal and entropy-based correlation between successful and failed DNS queries, for fluxing botnet detection.
We also present an approach which computes the reputation of domains in a bipartite graph of hosts within a network, and the domains accessed by them. The inference technique utilizes belief propagation, an approximation algorithm for marginal probability estimation. The computation of reputation scores is seeded through a small fraction of domains found in black and white lists. An application of this technique, on an HTTP-proxy dataset from a large enterprise, shows a high detection rate with low false positive rates
Advances in modern botnet understanding and the accurate enumeration of infected hosts
Botnets remain a potent threat due to evolving modern architectures, inadequate remediation methods, and inaccurate measurement techniques. In response, this re- search exposes the architectures and operations of two advanced botnets, techniques to enumerate infected hosts, and pursues the scientific refinement of infected-host enu- meration data by recognizing network structures which distort measurement. This effort is motivated by the desire to reveal botnet behavior and trends for future mit- igation, methods to discover infected hosts for remediation in real time and threat assessment, and the need to reveal the inaccuracy in population size estimation when only counting IP addresses. Following an explanation of theoretical enumeration techniques, the architectures, deployment methodologies, and malicious output for the Storm and Waledac botnets are presented. Several tools developed to enumerate these botnets are then assessed in terms of performance and yield. Finally, this study documents methods that were developed to discover the boundaries and impact of NAT and DHCP blocks in network populations along with a footprint measurement based on relative entropy which better describes how uniformly infections communi- cate through their IP addresses. Population data from the Waledac botnet was used to evaluate these techniqu
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