Data Leak Detection As a Service: Challenges and Solutions

We describe a network-based data-leak detection (DLD) technique, the main feature of which is that the detection does not require the data owner to reveal the content of the sensitive data. Instead, only a small amount of specialized digests are needed. Our technique – referred to as the fuzzy fingerprint – can be used to detect accidental data leaks due to human errors or application flaws. The privacy-preserving feature of our algorithms minimizes the exposure of sensitive data and enables the data owner to safely delegate the detection to others.We describe how cloud providers can offer their customers data-leak detection as an add-on service with strong privacy guarantees. We perform extensive experimental evaluation on the privacy, efficiency, accuracy and noise tolerance of our techniques. Our evaluation results under various data-leak scenarios and setups show that our method can support accurate detection with very small number of false alarms, even when the presentation of the data has been transformed. It also indicates that the detection accuracy does not degrade when partial digests are used. We further provide a quantifiable method to measure the privacy guarantee offered by our fuzzy fingerprint framework

User-Behavior Based Detection of Infection Onset

A major vector of computer infection is through exploiting software or design flaws in networked applications such as the browser. Malicious code can be fetched and executed on a victim’s machine without the user’s permission, as in drive-by download (DBD) attacks. In this paper, we describe a new tool called DeWare for detecting the onset of infection delivered through vulnerable applications. DeWare explores and enforces causal relationships between computer-related human behaviors and system properties, such as file-system access and process execution. Our tool can be used to provide real time protection of a personal computer, as well as for diagnosing and evaluating untrusted websites for forensic purposes. Besides the concrete DBD detection solution, we also formally define causal relationships between user actions and system events on a host. Identifying and enforcing correct causal relationships have important applications in realizing advanced and secure operating systems. We perform extensive experimental evaluation, including a user study with 21 participants, thousands of legitimate websites (for testing false alarms), as well as 84 malicious websites in the wild. Our results show that DeWare is able to correctly distinguish legitimate download events from unauthorized system events with a low false positive rate (< 1%)

Visualizing traffic causality for analyzing network anomalies

ABSTRACT Monitoring network traffic and detecting anomalies are essential tasks that are carried out routinely by security analysts. The sheer volume of network requests often makes it difficult to detect attacks and pinpoint their causes. We design and develop a tool to visually represent the causal relations for network requests. The traffic causality information enables one to reason about the legitimacy and normalcy of observed network events. Our tool with a special visual locality property supports different levels of visualbased querying and reasoning required for the sensemaking process on complex network data. Leveraging the domain knowledge, security analysts can use our tool to identify abnormal network activities and patterns due to attacks or stealthy malware. We conduct a user study that confirms our tool can enhance the readability and perceptibility of the dependency for host-based network traffic

Adaptive Key Protection in Complex Cryptosystems with Attributes

In the attribute-based encryption (ABE) model, attributes (as opposed to identities) are used to encrypt messages, and all the receivers with qualifying attributes can decrypt the ciphertext. However, compromised attribute keys may affect the communications of many users who share the same access control policies. We present the notion of forward-secure attribute-based encryption (fs-ABE) and give a concrete construction based on bilinear map and decisional bilinear Diffie-Hellman assumption. Forward security means that a compromised private key by an adversary at time t does not break the confidentiality of the communication that took place prior to t. We describe how to achieve both forward security and encryption with attributes, and formally prove our security against the adaptive chosen-ciphertext adversaries. Our scheme is non-trivial, and the key size only grow

Detection of stealthy malware activities with traffic causality and scalable triggering relation discovery.

ABSTRACT Studies show that a significant portion of networked computers are infected with stealthy malware. Infection allows remote attackers to control, utilize, or spy on victim machines. Conventional signature-scan or counting-based techniques are limited, as they are unable to stop new zero-day exploits. We describe a traffic analysis method that can effectively detect malware activities on a host. Our new approach efficiently discovers the underlying triggering relations of a massive amount of network events. We use these triggering relations to reason the occurrences of network events and to pinpoint stealthy malware activities. We define a new problem of triggering relation discovery of network events. Our solution is based on domain-knowledge guided advanced learning algorithms. Our extensive experimental evaluation involving 6+ GB traffic of various types shows promising results on the accuracy of our triggering relation discovery