Biblio
Wake locks are widely used in Android apps to protect critical computations from being disrupted by device sleeping. Inappropriate use of wake locks often seriously impacts user experience. However, little is known on how wake locks are used in real-world Android apps and the impact of their misuses. To bridge the gap, we conducted a large-scale empirical study on 44,736 commercial and 31 open-source Android apps. By automated program analysis and manual investigation, we observed (1) common program points where wake locks are acquired and released, (2) 13 types of critical computational tasks that are often protected by wake locks, and (3) eight patterns of wake lock misuses that commonly cause functional and non-functional issues, only three of which had been studied by existing work. Based on our findings, we designed a static analysis technique, Elite, to detect two most common patterns of wake lock misuses. Our experiments on real-world subjects showed that Elite is effective and can outperform two state-of-the-art techniques.
User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of difficulty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space. We propose two effective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet effective optimization methods are used for optimizing objective function: the first one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the first work to propose a unified framework to address the following two important aspects of the multi-platform user identity linkage problem –- (I) the platform multiplicity and (II) online data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.
Network flow classification is fundamental to network management and network security. However, it is challenging to classify network flows at very high line rates while simultaneously preserving user privacy. Machine learning based classification techniques utilize only meta-information of a flow and have been shown to be effective in identifying network flows. We analyze a group of widely used machine learning classifiers, and observe that the effectiveness of different classification models depends highly upon the protocol types as well as the flow features collected from network data.We propose vTC, a design of virtual network functions to flexibly select and apply the best suitable machine learning classifiers at run time. The experimental results show that the proposed NFV for flow classification can improve the accuracy of classification by up to 13%.
In recent years, online programming and software engineering education via information technology has gained a lot of popularity. Typically, popular courses often have hundreds or thousands of students but only a few course sta members. Tool automation is needed to maintain the quality of education. In this paper, we envision that the capability of quantifying behavioral similarity between programs is helpful for teaching and learning programming and software engineering, and propose three metrics that approximate the computation of behavioral similarity. Speci cally, we leverage random testing and dynamic symbolic execution (DSE) to generate test inputs, and run programs on these test inputs to compute metric values of the behavioral similarity. We evaluate our metrics on three real-world data sets from the Pex4Fun platform (which so far has accumulated more than 1.7 million game-play interactions). The results show that our metrics provide highly accurate approximation to the behavioral similarity. We also demonstrate a number of practical applications of our metrics including hint generation, progress indication, and automatic grading.
Mobile applications frequently access sensitive personal information to meet user or business requirements. Because such information is sensitive in general, regulators increasingly require mobile-app developers to publish privacy policies that describe what information is collected. Furthermore, regulators have fined companies when these policies are inconsistent with the actual data practices of mobile apps. To help mobile-app developers check their privacy policies against their apps' code for consistency, we propose a semi-automated framework that consists of a policy terminology-API method map that links policy phrases to API methods that produce sensitive information, and information flow analysis to detect misalignments. We present an implementation of our framework based on a privacy-policy-phrase ontology and a collection of mappings from API methods to policy phrases. Our empirical evaluation on 477 top Android apps discovered 341 potential privacy policy violations.
Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial settings) outperformed all of the research tools in the study. In this paper, we present two signi cant extensions of that study. First, we conduct the rst industrial case study of applying Monkey against WeChat, a popular messenger app with over 762 million monthly active users, and report the empirical ndings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey. We conclude the paper with empirical insights for future enhancements to both Monkey and our approach.
We present ConVenus, a system that performs rapid congestion verification of network updates in softwaredefined networks. ConVenus is a lightweight middleware between the SDN controller and network devices, and is capable to intercept flow updates from the controller and verify whether the amount of traffic in any links and switches exceeds the desired capacity. To enable online verification, ConVenus dynamically identifies the minimum set of flows and switches that are affected by each flow update, and creates a compact network model. ConVenus uses a four-phase simulation algorithm to quickly compute the throughput of every flow in the network model and report network congestion. The experimental results demonstrate that ConVenus manages to verify 90% of the updates in a network consisting of over 500 hosts and 80 switches within 5 milliseconds.
Security isolation is a foundation of computing systems that enables resilience to different forms of attacks. This article seeks to understand existing security isolation techniques by systematically classifying different approaches and analyzing their properties. We provide a hierarchical classification structure for grouping different security isolation techniques. At the top level, we consider two principal aspects: mechanism and policy. Each aspect is broken down into salient dimensions that describe key properties. We break the mechanism into two dimensions: enforcement location and isolation granularity, and break the policy aspect down into three dimensions: policy generation, policy configurability, and policy lifetime. We apply our classification to a set of representative papers that cover a breadth of security isolation techniques and discuss trade-offs among different design choices and limitations of existing approaches.
In recent years, we have seen a number of successful attacks against high-profile targets, some of which have even caused severe physical damage. These examples have shown us that resourceful and determined attackers can penetrate virtually any system, even those that are secured by the "air-gap." Consequently, in order to minimize the impact of stealthy attacks, defenders have to focus not only on strengthening the first lines of defense but also on deploying effective intrusion-detection systems. Intrusion-detection systems can play a key role in protecting sensitive computer systems since they give defenders a chance to detect and mitigate attacks before they could cause substantial losses. However, an over-sensitive intrusion-detection system, which produces a large number of false alarms, imposes prohibitively high operational costs on a defender since alarms need to be manually investigated. Thus, defenders have to strike the right balance between maximizing security and minimizing costs. Optimizing the sensitivity of intrusion detection systems is especially challenging in the case when multiple interdependent computer systems have to be defended against a strategic attacker, who can target computer systems in order to maximize losses and minimize the probability of detection. We model this scenario as an attacker-defender security game and study the problem of finding optimal intrusion detection thresholds.
(No abstract.)
Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.
Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.
— Recent experimental studies have shown that traf- fic management systems are vulnerable to cyber-attacks on sensor data. This paper studies the vulnerability of fixedtime control of signalized intersections when sensors measuring traffic flow information are compromised and perturbed by an adversary. The problems are formulated by considering three malicious objectives: 1) worst-case network accumulation, which aims to destabilize the overall network as much as possible; 2) worst-case lane accumulation, which aims to cause worstcase accumulation on some target lanes; and 3) risk-averse target accumulation, which aims to reach a target accumulation by making the minimum perturbation to sensor data. The problems are solved using bilevel programming optimization methods. Finally, a case study of a real network is used to illustrate the results.
The Internet of Things(IoT) has become a popular technology, and various middleware has been proposed and developed for IoT systems. However, there have been few studies on the data management of IoT systems. In this paper, we consider graph database models for the data management of IoT systems because these models can specify relationships in a straightforward manner among entities such as devices, users, and information that constructs IoT systems. However, applying a graph database to the data management of IoT systems raises issues regarding distribution and security. For the former issue, we propose graph database operations integrated with REST APIs. For the latter, we extend a graph edge property by adding access protocol permissions and checking permissions using the APIs with authentication. We present the requirements for a use case scenario in addition to the features of a distributed graph database for IoT data management to solve the aforementioned issues, and implement a prototype of the graph database.
Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.
The Internet of Things (IoT) architecture is expected to evolve into a model containing various open systems, integrated environments, and platforms, which can be programmed and can provide secure services on demand. However, not much effort has been devoted towards the security of such an IoT architecture. In this paper, we present an IoT architecture that supports deploying dynamic security policies for IoT services. In this approach, IoT devices, gateways, and data are open and programmable to IoT application developers and service operators. Fine-grained security policies can be programmed and dynamically adjusted according to users' requirements, devices' capabilities and networking environments. The implementation and test results show that new security policies can be created and deployed rapidly and demonstrate the feasibility of the architecture.
The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from past literature, we capture such interactive effect, and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data, and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis, as well as extensive simulations.
A finite state machine (FSM) is responsible for controlling the overall functionality of most digital systems and, therefore, the security of the whole system can be compromised if there are vulnerabilities in the FSM. These vulnerabilities can be created by improper designs or by the synthesis tool which introduces additional don't-care states and transitions during the optimization and synthesis process. An attacker can utilize these vulnerabilities to perform fault injection attacks or insert malicious hardware modifications (Trojan) to gain unauthorized access to some specific states. To our knowledge, no systematic approaches have been proposed to analyze these vulnerabilities in FSM. In this paper, we develop a framework named Analyzing Vulnerabilities in FSM (AVFSM) which extracts the state transition graph (including the don't-care states and transitions) from a gate-level netlist using a novel Automatic Test Pattern Generation (ATPG) based approach and quantifies the vulnerabilities of the design to fault injection and hardware Trojan insertion. We demonstrate the applicability of the AVFSM framework by analyzing the vulnerabilities in the FSM of AES and RSA encryption module. We also propose a low-cost mitigation technique to make FSM more secure against these attacks.
SDN’s logically centralized control provides an insertion point for programming the network. While it is generally agreed that higherlevel abstractions are needed to make that programming easy, there is little consensus on what are the “right” abstractions. Indeed, as SDN moves beyond its initial specialized deployments to broader use cases, it is likely that network control applications will require diverse abstractions that evolve over time. To this end, we champion a perspective that SDN control fundamentally revolves around data representation. We discard any application-specific structure that might be outgrown by new demands. Instead, we adopt a plain data representation of the entire network — network topology, forwarding, and control applications — and seek a universal data language that allows application programmers to transform the primitive representation into any high-level representations presented to applications or network operators. Driven by this insight, we present a system, Ravel, that implements an entire SDN network control infrastructure within a standard SQL database. In Ravel, network abstractions take the form of user-defined SQL views expressed by SQL queries that can be added on the fly. A key challenge in realizing this approach is to orchestrate multiple simultaneous abstractions that collectively affect the same underlying data. To achieve this, Ravel enhances the database with novel data integration mechanisms that merge the multiple views into a coherent forwarding behavior. Moreover, Ravel is exposed to applications through the one simple, familiar and highly interoperable SQL interface. While this is an ambitious long-term goal, our prototype built on the PostgreSQL database exhibits promising performance even for large scale networks.