Tupleware: Redefining Modern Analytics

There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the data and infrastructure of the Googles and Facebooks of the world---petabytes of data distributed across large cloud deployments consisting of thousands of cheap commodity machines. Yet, the vast majority of users operate clusters ranging from a few to a few dozen nodes, analyze relatively small datasets of up to a few terabytes, and perform primarily compute-intensive operations. Targeting these users fundamentally changes the way we should build analytics systems. This paper describes the design of Tupleware, a new system specifically aimed at the challenges faced by the typical user. Tupleware's architecture brings together ideas from the database, compiler, and programming languages communities to create a powerful end-to-end solution for data analysis. We propose novel techniques that consider the data, computations, and hardware together to achieve maximum performance on a case-by-case basis. Our experimental evaluation quantifies the impact of our novel techniques and shows orders of magnitude performance improvement over alternative systems

Towards Smart Hybrid Fuzzing for Smart Contracts

Smart contracts are Turing-complete programs that are executed across a blockchain network. Unlike traditional programs, once deployed they cannot be modified. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In recent years, smart contracts suffered major exploits, costing millions of dollars, due to programming errors. As a result, a variety of tools for detecting bugs has been proposed. However, majority of these tools often yield many false positives due to over-approximation or poor code coverage due to complex path constraints. Fuzzing or fuzz testing is a popular and effective software testing technique. However, traditional fuzzers tend to be more effective towards finding shallow bugs and less effective in finding bugs that lie deeper in the execution. In this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary fuzzing with constraint solving in order to execute more code and find more bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts of a smart contract, while constraint solving is used to generate inputs which satisfy complex conditions that prevent the evolutionary fuzzing from exploring deeper paths. Moreover, we use data dependency analysis to efficiently generate sequences of transactions, that create specific contract states in which bugs may be hidden. We evaluate the effectiveness of our fuzzing strategy, by comparing CONFUZZIUS with state-of-the-art symbolic execution tools and fuzzers. Our evaluation shows that our hybrid fuzzing approach produces significantly better results than state-of-the-art symbolic execution tools and fuzzers

Network Traffic Analysis Framework For Cyber Threat Detection

The growing sophistication of attacks and newly emerging cyber threats requires advanced cyber threat detection systems. Although there are several cyber threat detection tools in use, cyber threats and data breaches continue to rise. This research is intended to improve the cyber threat detection approach by developing a cyber threat detection framework using two complementary technologies, search engine and machine learning, combining artificial intelligence and classical technologies. In this design science research, several artifacts such as a custom search engine library, a machine learning-based engine and different algorithms have been developed to build a new cyber threat detection framework based on self-learning search and machine learning engines. Apache Lucene.Net search engine library was customized in order to function as a cyber threat detector, and Microsoft ML.NET was used to work with and train the customized search engine. This research proves that a custom search engine can function as a cyber threat detection system. Using both search and machine learning engines in the newly developed framework provides improved cyber threat detection capabilities such as self-learning and predicting attack details. When the two engines run together, the search engine is continuously trained by the machine learning engine and grow smarter to predict yet unknown threats with greater accuracy. While customizing the search engine to function as a cyber threat detector, this research also identified and proved the best algorithms for the search engine based cyber threat detection model. For example, the best scoring algorithm was found to be the Manhattan distance. The validation case study also shows that not every network traffic feature makes an equal contribution to determine the status of the traffic, and thus the variable-dimension Vector Space Model (VSM) achieves better detection accuracy than n-dimensional VSM. Although the use of different technologies and approaches improved detection results, this research is primarily focused on developing techniques rather than building a complete threat detection system. Additional components such as those that can track and investigate the impact of network traffic on the destination devices make the newly developed framework robust enough to build a comprehensive cyber threat detection appliance

Stacco: Differentially Analyzing Side-Channel Traces for Detecting SSL/TLS Vulnerabilities in Secure Enclaves

Intel Software Guard Extension (SGX) offers software applications enclave to protect their confidentiality and integrity from malicious operating systems. The SSL/TLS protocol, which is the de facto standard for protecting transport-layer network communications, has been broadly deployed for a secure communication channel. However, in this paper, we show that the marriage between SGX and SSL may not be smooth sailing. Particularly, we consider a category of side-channel attacks against SSL/TLS implementations in secure enclaves, which we call the control-flow inference attacks. In these attacks, the malicious operating system kernel may perform a powerful man-in-the-kernel attack to collect execution traces of the enclave programs at page, cacheline, or branch level, while positioning itself in the middle of the two communicating parties. At the center of our work is a differential analysis framework, dubbed Stacco, to dynamically analyze the SSL/TLS implementations and detect vulnerabilities that can be exploited as decryption oracles. Surprisingly, we found exploitable vulnerabilities in the latest versions of all the SSL/TLS libraries we have examined. To validate the detected vulnerabilities, we developed a man-in-the-kernel adversary to demonstrate Bleichenbacher attacks against the latest OpenSSL library running in the SGX enclave (with the help of Graphene) and completely broke the PreMasterSecret encrypted by a 4096-bit RSA public key with only 57286 queries. We also conducted CBC padding oracle attacks against the latest GnuTLS running in Graphene-SGX and an open-source SGX-implementation of mbedTLS (i.e., mbedTLS-SGX) that runs directly inside the enclave, and showed that it only needs 48388 and 25717 queries, respectively, to break one block of AES ciphertext. Empirical evaluation suggests these man-in-the-kernel attacks can be completed within 1 or 2 hours.Comment: CCS 17, October 30-November 3, 2017, Dallas, TX, US

First-Order Models for Configuration Analysis

Our world teems with networked devices. Their configuration exerts an ever-expanding influence on our daily lives. Yet correctly configuring systems, networks, and access-control policies is notoriously difficult, even for trained professionals. Automated static analysis techniques provide a way to both verify a configuration\u27s correctness and explore its implications. One such approach is scenario-finding: showing concrete scenarios that illustrate potential (mis-)behavior. Scenarios even have a benefit to users without technical expertise, as concrete examples can both trigger and improve users\u27 intuition about their system. This thesis describes a concerted research effort toward improving scenario-finding tools for configuration analysis. We developed Margrave, a scenario-finding tool with special features designed for security policies and configurations. Margrave is not tied to any one specific policy language; rather, it provides an intermediate input language as expressive as first-order logic. This flexibility allows Margrave to reason about many different types of policy. We show Margrave in action on Cisco IOS, a common language for configuring firewalls, demonstrating that scenario-finding with Margrave is useful for debugging and validating real-world configurations. This thesis also presents a theorem showing that, for a restricted subclass of first-order logic, if a sentence is satisfiable then there must exist a satisfying scenario no larger than a computable bound. For such sentences scenario-finding is complete: one can be certain that no scenarios are missed by the analysis, provided that one checks up to the computed bound. We demonstrate that many common configurations fall into this subclass and give algorithmic tests for both sentence membership and counting. We have implemented both in Margrave. Aluminum is a tool that eliminates superfluous information in scenarios and allows users\u27 goals to guide which scenarios are displayed. We quantitatively show that our methods of scenario-reduction and exploration are effective and quite efficient in practice. Our work on Aluminum is making its way into other scenario-finding tools. Finally, we describe FlowLog, a language for network programming that we created with analysis in mind. We show that FlowLog can express many common network programs, yet demonstrate that automated analysis and bug-finding for FlowLog are both feasible as well as complete

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques