Biblio

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.

Traditional intrusion detection systems (IDSs) work in isolation and can be easily compromised by unknown threats. An intrusion detection network (IDN) is a collaborative IDS network intended to overcome this weakness by allowing IDS peers to share detection knowledge and experience, and hence improve the overall accuracy of intrusion assessment. In this work, we design an IDN system, called GUIDEX, using gametheoretic modeling and trust management for peers to collaborate truthfully and actively. We first describe the system architecture and its individual components, and then establish a gametheoretic framework for the resource management component of GUIDEX. We establish the existence and uniqueness of a Nash equilibrium under which peers can communicate in a reciprocal incentive compatible manner. Based on the duality of the problem, we develop an iterative algorithm that converges geometrically to the equilibrium. Our numerical experiments and discrete event simulation demonstrate the convergence to the Nash equilibrium and the security features of GUIDEX against free riders, dishonest insiders and DoS attacks

Executive Order (EO) 14028, Improving the Nation's Cybersecurity, 12 May 2021, directs the National Institute of Standards and Technology (NIST) to recommend minimum standards for software testing within 60 days. This document describes eleven recommendations for software verification techniques as well as providing supplemental information about the techniques and references for further information. It recommends the following techniques: • Threat modeling to look for design-level security issues • Automated testing for consistency and to minimize human effort • Static code scanning to look for top bugs • Heuristic tools to look for possible hardcoded secrets • Use of built-in checks and protections • "Black box" test cases • Code-based structural test cases • Historical test cases • Fuzzing • Web app scanners, if applicable • Address included code (libraries, packages, services) The document does not address the totality of software verification, but instead recommends techniques that are broadly applicable and form the minimum standards. The document was developed by NIST in consultation with the National Security Agency. Additionally, we received input from numerous outside organizations through papers submitted to a NIST workshop on the Executive Order held in early June, 2021 and discussion at the workshop as well as follow up with several of the submitters.

User's behavioral biometrics are promising as authentication factors in particular if accuracy is sufficiently guaranteed. They can be used to augment security in combination with other authentication factors. A gesture-based pattern lock system is a good example of such multi-factor authentication, using touch dynamics in a smartphone. However, touch dynamics can be significantly affected by a shape of gestures with regard to the performance and accuracy, and our concern is that user-chosen patterns are likely far from producing such a good shape of gestures. In this poster, we raise this problem and show our experimental study conducted in this regard. We investigate if there is a reproducible correlation between shape and accuracy and if we can derive effective attribute values for user guidance, based on the gesture-based pattern lock system. In more general, we discuss a guided approach to behavioral authentication.

Cognitive Radio is a prominent solution for effective spectral resource utilization. The rapidly growing device to device (D2D) communications and the next generation networks urge the cognitive radio networks to facilitate wideband spectrum sensing in order to assure newer spectral opportunities. As Nyquist sampling rates are formidable owing to complexity and cost of the ADCs, compressive sampling approaches are becoming increasingly popular. One such approach exploited in this paper is the Modulated Wideband Converter (MWC) to recover the spectral support. On the multiple measurement vector (MMV) framework provided by the MWC, threshold based Orthogonal Matching Pursuit (OMP) and Sparse Bayesian Learning (SBL) algorithms are employed for support recovery. We develop a Graphical User Interface (GUI) that assists a beginner to simulate the RF front-end of a MWC and thereby enables the user to explore support recovery as a function of Signal to Noise Ratio (SNR), number of measurement vectors and threshold. The GUI enables the user to explore spectrum sensing in DVB-T, 3G and 4G bands and recovers the support using OMP or SBL approach. The results show that the performance of SBL is better than that of OMP at a lower SNR values.

The six papers in this special section focuses on services and software computing. Services computing provides a foundation to build software systems and applications over the Internet as well as emerging hybrid networked platforms motivated by it. Due to the open, dynamic, and evolving nature of the Internet, new features were born with these Internet-scale and service-based software systems. Such systems should be situation- aware, adaptable, and able to evolve to effectively deal with rapid changes of user requirements and runtime contexts. These emerging software systems enable and require novel methods in conducting software requirement, design, deployment, operation, and maintenance beyond existing services computing technologies. New programming and lifecycle paradigms accommodating such Internet- scale and service-based software systems, referred to as Internetware, are inevitable. The goal of this special section is to present the innovative solutions and challenging technical issues, so as to explore various potential pathways towards Internet-scale and service-based software systems.

The papers in this special section focus on network science for high confidence cyber-physical systems (CPS) Here CPS refers to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. To enable high-confidence CPS for achieving better benefits as well as supporting emerging applications, network science-based theories and methodologies are needed to cope with the ever-growing complexity of smart CPS, to predict the system behaviors, and to model the deep inter-dependencies among CPS and the natural world. The major objective of this special section is to exploit various network science techniques such as modeling, analysis, mining, visualization, and optimization to advance the science of supporting high-confidence CPS for greater assurances of security, safety, scalability, efficiency, and reliability. These papers bring a timely and important research topic. The challenges and opportunities of applying network science approaches to high-confidence CPS are profound and far-reaching.

The papers in this special section explore recent advancements in parallel graph processing. In the sphere of modern data science and data-driven applications, graph algorithms have achieved a pivotal place in advancing the state of scientific discovery and knowledge. Nearly three centuries of ideas have made graph theory and its applications a mature area in computational sciences. Yet, today we find ourselves at a crossroads between theory and application. Spurred by the digital revolution, data from a diverse range of high throughput channels and devices, from across internet-scale applications, are starting to mark a new era in data-driven computing and discovery. Building robust graph models and implementing scalable graph application frameworks in the context of this new era are proving to be significant challenges. Concomitant to the digital revolution, we have also experienced an explosion in computing architectures, with a broad range of multicores, manycores, heterogeneous platforms, and hardware accelerators (CPUs, GPUs) being actively developed and deployed within servers and multinode clusters. Recent advances have started to show that in more than one way, these two fields—graph theory and architectures–are capable of benefiting and in fact spurring new research directions in one another. This special section is aimed at introducing some of the new avenues of cutting-edge research happening at the intersection of graph algorithm design and their implementation on advanced parallel architectures.

What is the minimum number of guesses needed on average to correctly guess a realization of a random variable? The answer to this question led to the introduction of the notion of a quantity called guesswork by Massey in 1994, which can be viewed as an alternate security criterion to entropy. In this paper, we consider guesswork in the presence of quantum side information, and show that a general sequential guessing strategy is equivalent to performing a single quantum measurement and choosing a guessing strategy based on the outcome. We use this result to deduce entropic one-shot and asymptotic bounds on the guesswork in the presence of quantum side information, and to formulate a semi-definite program (SDP) to calculate the quantity. We evaluate the guesswork for a simple example involving the BB84 states, and we prove a continuity result that certifies the security of slightly imperfect key states when the guesswork is used as the security criterion.

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The complex architecture of browser technologies and dynamic characteristics of JavaScript make it difficult to ensure security in client-side web applications. Browser-level security policies alone are not sufficient because it is difficult to apply them correctly and they can be bypassed. As a result, they need to be completed by application-level security policies. In this paper, we survey existing solutions for specifying and enforcing application-level security policies for client-side web applications, and distill a number of desirable features. Based on these features we developed Guardia, a framework for declaratively specifying and dynamically enforcing application-level security policies for JavaScript web applications without requiring VM modifications. We describe Guardia enforcement mechanism by means of JavaScript reflection with respect to three important security properties (transparency, tamper-proofness, and completeness). We also use Guardia to specify and deploy 12 access control policies discussed in related work in three experimental applications that are representative of real-world applications. Our experiments indicate that Guardia is correct, transparent, and tamper-proof, while only incurring a reasonable runtime overhead.

In the last years, networking scenarios have been evolving, hand-in-hand with new and varied applications with heterogeneous Quality of Service (QoS) requirements. These requirements must be efficiently and effectively delivered. Given its static layered structure and almost complete lack of built-in QoS support, the current TCP/IP-based Internet hinders such an evolution. In contrast, the clean-slate Recursive InterNetwork Architecture (RINA) proposes a new recursive and programmable networking model capable of evolving with the network requirements, solving in this way most, if not all, TCP/IP protocol stack limitations. Network providers can better deliver communication services across their networks by taking advantage of the RINA architecture and its support for QoS. This support allows providing complete information of the QoS needs of the supported traffic flows, and thus, fulfilment of these needs becomes possible. In this work, we focus on the importance of path selection to better ensure QoS guarantees in long-haul RINA networks. We propose and evaluate a programmable strategy for path selection based on flow QoS parameters, such as the maximum allowed latency and packet losses, comparing its performance against simple shortest-path, fastest-path and connection-oriented solutions.

Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. Unfortunately, no work so far shows how to utilize GPUs to accelerate TADOC. We describe G-TADOC, the first framework that provides GPU-based text analytics directly on compression, effectively enabling efficient text analytics on GPUs without decompressing the input data. G-TADOC solves three major challenges. First, TADOC involves a large amount of dependencies, which makes it difficult to exploit massive parallelism on a GPU. We develop a novel fine-grained thread-level workload scheduling strategy for GPU threads, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, in developing G-TADOC, thousands of GPU threads writing to the same result buffer leads to inconsistency while directly using locks and atomic operations lead to large synchronization overheads. We develop a memory pool with thread-safe data structures on GPUs to handle such difficulties. Third, maintaining the sequence information among words is essential for lossless compression. We design a sequence-support strategy, which maintains high GPU parallelism while ensuring sequence information. Our experimental evaluations show that G-TADOC provides 31.1× average speedup compared to state-of-the-art TADOC.

Traffic flow prediction is an important foundation for intelligent transportation systems. The traffic data are generated from a traffic network and evolved dynamically. So spatio-temporal relation exploration plays a support role on traffic data analysis. Most researches focus on spatio-temporal information fusion through a convolution operation. To the best of our knowledge, this is the first work to suggest that it is necessary to distinguish the two aspects of spatial correlations and propose the two types of spatial graphs, named as geographic graph and semantic graph. Then two novel stereo convolutions with irregular acceptive fields are proposed. The geographic-semantic-temporal contexts are dynamically jointly captured through performing the proposed convolutions on graph sequences. We propose a geographic-semantic-temporal graph convolutional network (GST-GCN) model that combines our graph convolutions and GRU units hierarchically in a unified end-to-end network. The experiment results on the Caltrans Performance Measurement System (PeMS) dataset show that our proposed model significantly outperforms other popular spatio-temporal deep learning models and suggest the effectiveness to explore geographic-semantic-temporal dependencies on deep learning models for traffic flow prediction.

The concealment and confusion nature of insider threat makes it a challenging task for security analysts to identify insider threat from log data. To detect insider threat, we propose a novel gated recurrent unit (GRU) and multi-autoencoder based insider threat detection method, which is an unsupervised anomaly detection method. It takes advantage of the extremely unbalanced characteristic of insider threat data and constructs a normal behavior autoencoder with low reconfiguration error through multi-level filter behavior learning, and identifies the behavior data with high reconfiguration error as abnormal behavior. In order to achieve the high efficiency of calculation and detection, GRU and multi-head attention are introduced into the autoencoder. Use dataset v6.2 of the CERT insider threat as validation data and threat detection recall as evaluation metric. The experimental results show that the effect of the proposed method is obviously better than that of Isolation Forest, LSTM autoencoder and multi-channel autoencoders based insider threat detection methods, and it's an effective insider threat detection technology.

The de facto approach to Web security today is HTTPS. While HTTPS ensures complete security for clients and servers, it also interferes with transparent content-caching at middleboxes. To address this problem and support both security and caching, we propose a new approach to Web security and privacy called GroupSec. The key innovation of GroupSec is that it replaces the traditional session-based security model with a new model based on content group membership. We introduce the GroupSec security model and show how HTTP can be easily adapted to support GroupSec without requiring changes to browsers, servers, or middleboxes. Finally, we present results of a threat analysis and performance experiments which show that GroupSec achieves notable performance benefits at the client and server while remaining as secure as HTTPS.

A strong designated verifier signature (SDVS) scheme only permits an intended verifier to validate the signature by employing his/her private key. Meanwhile, for the sake of signer anonymity, the designated verifier is also able to generate a computationally indistinguishable transcript, which prevents the designated verifier from arbitrarily transferring his conviction to any third party. To extend the applications of conventional SDVS schemes, in this paper, we propose a group-oriented strong designated verifier signature (GO-SDVS) scheme from bilinear pairings. In particular, our scheme allows a group of signers to cooperatively generate a signature for a designated verifier. A significant property of our mechanism is constant-size signatures, i.e., the signature length remains constant when the number of involved signers increases. We also prove that the proposed GO-SDVS scheme is secure against adaptive chosen-message attacks in the random oracle model and fulfills the essential properties of signer ambiguity and non-transferability.