Biblio

In this paper, we design NETHCF, a line-rate in-network system for filtering spoofed traffic. NETHCF leverages the opportunity provided by programmable switches to design a novel defense against spoofed IP traffic, and it is highly efficient and adaptive. One key challenge stems from the restrictions of the computational model and memory resources of programmable switches. We address this by decomposing the HCF system into two complementary components-one component for the data plane and another for the control plane. We also aggregate the IP-to-Hop-Count (IP2HC) mapping table for efficient memory usage, and design adaptive mechanisms to handle end-to-end routing changes, IP popularity changes, and network activity dynamics. We have built a prototype on a hardware Tofino switch, and our evaluation demonstrates that NETHCF can achieve line-rate and adaptive traffic filtering with low overheads.

Increasingly modern data science platforms today have non-intrusive and extensible provenance ingestion mechanisms to collect rich provenance and context information, handle modifications to the same file using distinguishable versions, and use graph data models (e.g., property graphs) and query languages (e.g., Cypher) to represent and manipulate the stored provenance/context information. Due to the schema-later nature of the metadata, multiple versions of the same files, and unfamiliar artifacts introduced by team members, the resulting "provenance graphs" are quite verbose and evolving; further, it is very difficult for the users to compose queries and utilize this valuable information just using standard graph query model. In this paper, we propose two high-level graph query operators to address the verboseness and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the retrospective provenance between a set of source vertices and a set of destination vertices via flexible boundary criteria to help users get insight about the derivation relationships among those vertices. We show the semantics of such a query in terms of a context-free grammar, and develop efficient algorithms that run orders of magnitude faster than state-of-the-art. Second, we propose a graph summarization operator that combines similar segments together to query prospective provenance of the underlying project. The operator allows tuning the summary by ignoring vertex details and characterizing local structures, and ensures the provenance meaning using path constraints. We show the optimal summary problem is PSPACE-complete and develop effective approximation algorithms. We implement the operators on top of Neo4j, evaluate our query techniques extensively, and show the effectiveness and efficiency of the proposed methods.

Hardware Trojan threats caused by malicious designers and untrusted manufacturers have become one of serious issues in modern VLSI systems. In this paper, we show some experimental results to insert hardware Trojans into asynchronous circuits. As a result, the overhead of hardware Trojan insertion in asynchronous circuits may be small for malicious designers who have enough knowledge about the asynchronous circuits. In addition, we also show several Trojan detection methods using deep learning schemes which have been proposed to detect synchronous hardware Trojan in the netlist level. We apply them to asynchronous hardware Trojan circuits and show their results. They have a great potential to detect a hardware Trojan in asynchronous circuits.

Network virtualization is a fundamental technology for datacenters and upcoming wireless communications (e.g., 5G). It takes advantage of software-defined networking (SDN) that provides efficient network management by converting networking fabrics into SDN-capable devices. Moreover, white-box switches, which provide flexible and fast packet processing, are broadly deployed in commercial datacenters. A white-box switch requires a specific and restricted packet processing pipeline; however, to date, there has been no SDN-based network hypervisor that can support the pipeline of white-box switches. Therefore, in this paper, we propose WhiteVisor: a network hypervisor which can support the physical network composed of white-box switches. WhiteVisor converts a flow rule from the virtual network into a packet processing pipeline compatible with the white-box switch. We implement the prototype herein and show its feasibility and effectiveness with pipeline conversion and overhead.

Return Oriented Programming is one of the most important software security challenges nowadays. It exploits memory vulnerabilities to control the state of the program and hijacks its control flow. Existing defenses usually focus on how to protect the control flow or face the challenge of how to maintain the taint markings for memory data. In this paper, we directly focus on the adversary-controlled states, simplify the classic dynamic taint analysis method to only track registers and propose Hardware-based Adversary-controlled States Tracking (HAST). HAST dynamically tracks registers that may be controlled by the adversary to detect ROP attack. It is transparent to user application and makes few modifications to existing hardware. Our evaluation demonstrates that HAST will introduce almost no performance overhead and can effectively detect ROP attacks without false positives on the tested common Linux applications.

In order to study the stress detection method on long-distance oil and gas pipeline, the distribution characteristics of the surface remanence signals in the stress concentration regions must be known. They were studied by using the magnetic domain model in the non-magnetic saturation state. The finite element method was used herein with the aim to analyse the static and mechanical characteristics of a ferromagnetic specimen. The variation law of remanence signal in stress concentration regions was simulated. The results show that a residue signal in the stress concentration region exists. In addition, a one-to-one correspondence in the non-magnetic saturation environment is evident. In the case of magnetic saturation, the remanence signal of the stress concentration region is covered and the signal cannot be recognised.

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ?642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.

We, humans, have the ability to easily imagine scenes that depict sentences such as ``Today is a beautiful sunny day'' or ``There is a Christmas feel, in the air''. While it is hard to precisely describe what one person may imagine, the essential high-level themes associated with such sentences largely remains the same. The ability to synthesize novel images that depict the feel of a sentence is very useful in a variety of applications such as education, advertisement, and entertainment. While existing papers tackle this problem given a style image, we aim to provide a far more intuitive and easy to use solution that synthesizes novel renditions of an existing image, conditioned on a given sentence. We present a method for cross-modal style transfer between an English sentence and an image, to produce a new image that imbibes the essential theme of the sentence. We do this by modifying the style transfer mechanism used in image style transfer to incorporate a style component derived from the given sentence. We demonstrate promising results using the YFCC100m dataset.

In context of Industry 4.0 Augmented Reality (AR) is frequently mentioned as the upcoming interface technology for human-machine communication and collaboration. Many prototypes have already arisen in both the consumer market and in the industrial sector. According to numerous experts it will take only few years until AR will reach the maturity level to be deployed in productive applications. Especially for industrial usage it is required to assess security risks and challenges this new technology implicates. Thereby we focus on plant operators, Original Equipment Manufacturers (OEMs) and component vendors as stakeholders. Starting from several industrial AR use cases and the structure of contemporary AR applications, in this paper we identify security assets worthy of protection and derive the corresponding security goals. Afterwards we elaborate the threats industrial AR applications are exposed to and develop an edge computing architecture for future AR applications which encompasses various measures to reduce security risks for our stakeholders.

This work describes how automated data generation integrates in a big data pipeline. A lack of veracity in big data can cause models that are inaccurate, or biased by trends in the training data. This can lead to issues as a pipeline matures that are difficult to overcome. This work describes the use of a Generative Adversarial Network to generate sketch data, such as those that might be used in a human verification task. These generated sketches are verified as recognizable using a crowd-sourcing methodology, and finds that the generated sketches were correctly recognized 43.8% of the time, in contrast to human drawn sketches which were 87.7% accurate. This method is scalable and can be used to generate realistic data in many domains and bootstrap a dataset used for training a model prior to deployment.

In video surveillance, face recognition (FR) systems seek to detect individuals of interest appearing over a distributed network of cameras. Still-to-video FR systems match faces captured in videos under challenging conditions against facial models, often designed using one reference still per individual. Although CNNs can achieve among the highest levels of accuracy in many real-world FR applications, state-of-the-art CNNs that are suitable for still-to-video FR, like trunk-branch ensemble (TBE) CNNs, represent complex solutions for real-time applications. In this paper, an efficient CNN architecture is proposed for accurate still-to-video FR from a single reference still. The CCM-CNN is based on new cross-correlation matching (CCM) and triplet-loss optimization methods that provide discriminant face representations. The matching pipeline exploits a matrix Hadamard product followed by a fully connected layer inspired by adaptive weighted cross-correlation. A triplet-based training approach is proposed to optimize the CCM-CNN parameters such that the inter-class variations are increased, while enhancing robustness to intra-class variations. To further improve robustness, the network is fine-tuned using synthetically-generated faces based on still and videos of non-target individuals. Experiments on videos from the COX Face and Chokepoint datasets indicate that the CCM-CNN can achieve a high level of accuracy that is comparable to TBE-CNN and HaarNet, but with a significantly lower time and memory complexity. It may therefore represent the better trade-off between accuracy and complexity for real-time video surveillance applications.

Named Data Networking (NDN) is one of the future internet architectures, which is a clean-slate approach. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/Data packets and innetwork caching. The continually increasing demand for rapid dissemination of large-scale scientific data is driving the use of NDN in data-intensive science experiments. In this paper, we establish an intercontinental NDN testbed. In the testbed, an NDN-based application that targets climate science as an example data intensive science application is designed and implemented, which has differentiated features compared to those of previous studies. We verify experimental justification of using NDN for climate science in the intercontinental network, through performance comparisons between classical delivery techniques and NDN-based climate data delivery.

Over the past few years we have articulated theory that describes ‘encrypted computing’, in which data remains in encrypted form while being worked on inside a processor, by virtue of a modified arithmetic. The last two years have seen research and development on a standards-compliant processor that shows that near-conventional speeds are attainable via this approach. Benchmark performance with the US AES-128 flagship encryption and a 1GHz clock is now equivalent to a 433MHz classic Pentium, and most block encryptions fit in AES's place. This summary article details how user data is protected by a system based on the processor from being read or interfered with by the computer operator, for those computing paradigms that entail trust in data-oriented computation in remote locations where it may be accessible to powerful and dishonest insiders. We combine: (i) the processor that runs encrypted; (ii) a slightly modified conventional machine code instruction set architecture with which security is achievable; (iii) an ‘obfuscating’ compiler that takes advantage of its possibilities, forming a three-point system that provably provides cryptographic "semantic security" for user data against the operator and system insiders.

At the RELENG 2014 Q&A, the question was asked, “What is your greatest concern?” and the response was “someone subverting our deployment pipeline”. That is the motivation for this paper. We explore what it means to subvert a pipeline and provide several different scenarios of subversion. We then focus on the issue of securing a pipeline. As a result, we provide an engineering process that is based on having trusted components mediate access to sensitive portions of the pipeline from other components, which can remain untrusted. Applying our process to a pipeline we constructed involving Chef, Jenkins, Docker, Github, and AWS, we find that some aspects of our process result in easy to make changes to the pipeline, whereas others are more difficult. Consequently, we have developed a design that hardens the pipeline, although it does not yet completely secure it.

The emergence of new network applications, such as the network intrusion detection system and packet-level accounting, requires packet classification to report all matched rules instead of only the best matched rule. Although several schemes have been proposed recently to address the multimatch packet classification problem, most of them require either huge memory or expensive ternary content addressable memory (TCAM) to store the intermediate data structure, or they suffer from steep performance degradation under certain types of classifiers. In this paper, we decompose the operation of multimatch packet classification from the complicated multidimensional search to several single-dimensional searches, and present an asynchronous pipeline architecture based on a signature tree structure to combine the intermediate results returned from single-dimensional searches. By spreading edges of the signature tree across multiple hash tables at different stages, the pipeline can achieve a high throughput via the interstage parallel access to hash tables. To exploit further intrastage parallelism, two edge-grouping algorithms are designed to evenly divide the edges associated with each stage into multiple work-conserving hash tables. To avoid collisions involved in hash table lookup, a hybrid perfect hash table construction scheme is proposed. Extensive simulation using realistic classifiers and traffic traces shows that the proposed pipeline architecture outperforms HyperCuts and B2PC schemes in classification speed by at least one order of magnitude, while having a similar storage requirement. Particularly, with different types of classifiers of 4K rules, the proposed pipeline architecture is able to achieve a throughput between 26.8 and 93.1 Gb/s using perfect hash tables.

DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.

Trusted Platform Module (TPM) has gained its popularity in computing systems as a hardware security approach. TPM provides the boot time security by verifying the platform integrity including hardware and software. However, once the software is loaded, TPM can no longer protect the software execution. In this work, we propose a dynamic TPM design, which performs control flow checking to protect the program from runtime attacks. The control flow checker is integrated at the commit stage of the processor pipeline. The control flow of program is verified to defend the attacks such as stack smashing using buffer overflow and code reuse. We implement the proposed dynamic TPM design in FPGA to achieve high performance, low cost and flexibility for easy functionality upgrade based on FPGA. In our design, neither the source code nor the Instruction Set Architecture (ISA) needs to be changed. The benchmark simulations demonstrate less than 1% of performance penalty on the processor, and an effective software protection from the attacks.