Biblio
Formal security verification of firmware interacting with hardware in modern Systems-on-Chip (SoCs) is a critical research problem. This faces the following challenges: (1) design complexity and heterogeneity, (2) semantics gaps between software and hardware, (3) concurrency between firmware/hardware and between Intellectual Property Blocks (IPs), and (4) expensive bit-precise reasoning. In this paper, we present a co-verification methodology to address these challenges. We model hardware using the Instruction-Level Abstraction (ILA), capturing firmware-visible behavior at the architecture level. This enables integrating hardware behavior with firmware in each IP into a single thread. The co-verification with multiple firmware across IPs is formulated as a multi-threaded program verification problem, for which we leverage software verification techniques. We also propose an optimization using abstraction to prevent expensive bit-precise reasoning. The evaluation of our methodology on an industry SoC Secure Boot design demonstrates its applicability in SoC security verification.
Digital-Twins simulate physical world objects by creating 'as-is' virtual images in a cyberspace. In order to create a well synchronized digital-twin simulator in manufacturing, information and activities of a physical machine need to be virtualized. Many existing digital-twins stream read-only data of machine sensors and do not incorporate operations of manufacturing machines through Internet. In this paper, a new method of virtualization is proposed to integrate machining data and operations into the digital-twins using Internet scale machine tool communication method. A fully functional digital-twin is implemented in CPMC testbed using MTComm and several manufacturing application scenarios are developed to evaluate the proposed method and system. Performance analysis shows that it is capable of providing data-driven visual monitoring of a manufacturing process and performing manufacturing operations through digital twins over the Internet. Results of the experiments also shows that the MTComm based digital twins have an excellent efficiency.
Multimedia data available in various disciplines are usually heterogeneous, containing representations in multi-views, where the cross-modal search techniques become necessary and useful. It is a challenging problem due to the heterogeneity of data with multiple modalities, multi-views in each modality and the diverse data categories. In this paper, we propose a novel multi-view cross-modal hashing method named Multi-view Collective Tensor Decomposition (MCTD) to fuse these data effectively, which can exploit the complementary feature extracted from multi-modality multi-view while simultaneously discovering multiple separated subspaces by leveraging the data categories as supervision information. Our contributions are summarized as follows: 1) we exploit tensor modeling to get better representation of the complementary features and redefine a latent representation space; 2) a block-diagonal loss is proposed to explicitly pursue a more discriminative latent tensor space by exploring supervision information; 3) we propose a new feature projection method to characterize the data and to generate the latent representation for incoming new queries. An optimization algorithm is proposed to solve the objective function designed for MCTD, which works under an iterative updating procedure. Experimental results prove the state-of-the-art precision of MCTD compared with competing methods.
In this paper we examine the use of covert channels based on CPU load in order to achieve persistent user identification through browser sessions. In particular, we demonstrate that an HTML5 video, a GIF image, or CSS animations on a webpage can be used to force the CPU to produce a sequence of distinct load levels, even without JavaScript or any client-side code. These load levels can be then captured either by another browsing session, running on the same or a different browser in parallel to the browsing session we want to identify, or by a malicious app installed on the device. To get a good estimation of the CPU load caused by the target session, the receiver can observe system statistics about CPU activity (app), or constantly measure time it takes to execute a known code segment (app and browser). Furthermore, for mobile devices we propose a sensor-based approach to estimate the CPU load, based on exploiting disturbances of the magnetometer sensor data caused by the high CPU activity. Captured loads can be decoded and translated into an identifying bit string, which is transmitted back to the attacker. Due to the way loads are produced, these methods are applicable even in highly restrictive browsers, such as the Tor Browser, and run unnoticeably to the end user. Therefore, unlike existing ways of web tracking, our methods circumvent most of the existing countermeasures, as they store the identifying information outside the browsing session being targeted. Finally, we also thoroughly evaluate and assess each presented method of generating and receiving the signal, and provide an overview of potential countermeasures.
The Internet of Things (IoT) holds great potential for productivity, quality control, supply chain efficiencies and overall business operations. However, with this broader connectivity, new vulnerabilities and attack vectors are being introduced, increasing opportunities for systems to be compromised by hackers and targeted attacks. These vulnerabilities pose severe threats to a myriad of IoT applications within areas such as manufacturing, healthcare, power and energy grids, transportation and commercial building management. While embedded OEMs offer technologies, such as hardware Trusted Platform Module (TPM), that deploy strong chain-of-trust and authentication mechanisms, still they struggle to protect against vulnerabilities introduced by vendors and end users, as well as additional threats posed by potential technical vulnerabilities and zero-day attacks. This paper proposes a pro-active policy-based approach, enforcing the principle of least privilege, through hardware Security Policy Engine (SPE) that actively monitors communication of applications and system resources on the system communication bus (ARM AMBA-AXI4). Upon detecting a policy violation, for example, a malicious application accessing protected storage, it counteracts with predefined mitigations to limit the attack. The proposed SPE approach widely complements existing embedded hardware and software security technologies, targeting the mitigation of risks imposed by unknown vulnerabilities of embedded applications and protocols.
Nowadays, Cross Site Scripting (XSS) is one of the major threats to Web applications. Since it's known to the public, XSS vulnerability has been in the TOP 10 Web application vulnerabilities based on surveys published by the Open Web Applications Security Project (OWASP). How to effectively detect and defend XSS attacks are still one of the most important security issues. In this paper, we present a novel approach to detect XSS attacks based on deep learning (called DeepXSS). First of all, we used word2vec to extract the feature of XSS payloads which captures word order information and map each payload to a feature vector. And then, we trained and tested the detection model using Long Short Term Memory (LSTM) recurrent neural networks. Experimental results show that the proposed XSS detection model based on deep learning achieves a precision rate of 99.5% and a recall rate of 97.9% in real dataset, which means that the novel approach can effectively identify XSS attacks.
We present an intelligent system that focus on how to ensure the stability of ZigBee network automatically. First, we discussed on the character of ZigBee compared with WIFI. Pointed out advantage of ZigBee resides in security, stability, low power consumption and better expandability. Second, figuring out the shortcomings of ZigBee on application is that physical limitation of the frequency band and weak ability on diffraction, especially coming across a wall or a door in the actual environment of home. The third, to put forward a method which can be used to ensure the strength of ZigBee signal. The method is to detect the strength of ZigBee relay in advance. And then, to compare it with the threshold value which had been defined in previous. The threshold value of strength of ZigBee is the minimal and tolerable value which can ensure stable transmission of ZigBee. If the detected value is out of the range of threshold, system will prompt up warning message which can be used to hint user to add ZigBee reply between the original ZigBee node and ZigBee gateway.
With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.
In this paper, a novel game-theoretic framework is introduced to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, a dynamic, psychological network interdiction game is formulated between a soldier and an attacker. In this game, the soldier seeks to find the optimal path to minimize the time needed to reach a destination, while maintaining a desired bit error rate (BER) performance by selectively communicating with certain IoBT devices. The attacker, on the other hand, seeks to find the optimal IoBT devices to attack, so as to maximize the BER of the soldier and hinder the soldier's progress. In this game, the soldier and attacker's first- order and second-order beliefs on each others' behavior are formulated to capture their psychological behavior. Using tools from psychological game theory, the soldier and attacker's intention to harm one another is captured in their utilities, based on their beliefs. A psychological forward induction-based solution is proposed to solve the dynamic game. This approach can find a psychological sequential equilibrium of the game, upon convergence. Simulation results show that, whenever the soldier explicitly intends to frustrate the attacker, the soldier's material payoff is increased by up to 15.6% compared to a traditional dynamic Bayesian game.
In this paper, the rotor unbalanced magnetic pull (UMP) characteristics of different field winding inter-turn short-circuit (FWISC) positions in turbo-generator are studied. Firstly, the qualitative analysis on the air gap magnetic flux density (MFD), as well as the rotor UMPs in X-direction and Y-direction, is carried out. Then the finite element numerical simulations are respectively taken to calculate the quantitative data of rotor UMP under normal condition and three different short-circuit positions. Finally, the variation rules based on rotor UMP characteristics by experimental analysis are obtained. It is shown that the occurrence of FWISC will induce generally fundamental-frequency UMP acting on the rotor in X-direction. Moreover, the different positions of FWISC are found to be sensitive to the rotor UMP amplitudes. The closer the short-circuit position is to the big teeth, the larger the rotor UMP amplitudes in X-direction will be.
Program authorship attribution has implications for the privacy of programmers who wish to contribute code anonymously. While previous work has shown that complete files that are individually authored can be attributed, these efforts have focused on ideal data sets such as the Google Code Jam data. We explore the problem of attribution "in the wild," examining source code obtained from open source version control systems, and investigate if and how such contributions can be attributed to their authors, either individually or on a per-account basis. In this work we show that accounts belonging to open source contributors containing short, incomplete, and typically uncompilable fragments can be effectively attributed.
Continuous Integration (CI) services, which can automatically build, test, and deploy software projects, are an invaluable asset in distributed teams, increasing productivity and helping to maintain code quality. Prior work has shown that CI pipelines can be sophisticated, and choosing and configuring a CI system involves tradeoffs. As CI technology matures, new CI tool offerings arise to meet the distinct wants and needs of software teams, as they negotiate a path through these tradeoffs, depending on their context. In this paper, we begin to uncover these nuances, and tell the story of open-source projects falling out of love with Travis, the earliest and most popular cloud-based CI system. Using logistic regression, we quantify the effects that open-source community factors and project technical factors have on the rate of Travis abandonment. We find that increased build complexity reduces the chances of abandonment, that larger projects abandon at higher rates, and that a project's dominant language has significant but varying effects. Finally, we find the surprising result that metrics of configuration attempts and knowledge dispersion in the project do not affect the rate of abandonment.
Deep learning technologies, which are the key components of state-of-the-art Artificial Intelligence (AI) services, have shown great success in providing human-level capabilities for a variety of tasks, such as visual analysis, speech recognition, and natural language processing and etc. Building a production-level deep learning model is a non-trivial task, which requires a large amount of training data, powerful computing resources, and human expertises. Therefore, illegitimate reproducing, distribution, and the derivation of proprietary deep learning models can lead to copyright infringement and economic harm to model creators. Therefore, it is essential to devise a technique to protect the intellectual property of deep learning models and enable external verification of the model ownership. In this paper, we generalize the "digital watermarking'' concept from multimedia ownership verification to deep neural network (DNNs) models. We investigate three DNN-applicable watermark generation algorithms, propose a watermark implanting approach to infuse watermark into deep learning models, and design a remote verification mechanism to determine the model ownership. By extending the intrinsic generalization and memorization capabilities of deep neural networks, we enable the models to learn specially crafted watermarks at training and activate with pre-specified predictions when observing the watermark patterns at inference. We evaluate our approach with two image recognition benchmark datasets. Our framework accurately (100$\backslash$%) and quickly verifies the ownership of all the remotely deployed deep learning models without affecting the model accuracy for normal input data. In addition, the embedded watermarks in DNN models are robust and resilient to different counter-watermark mechanisms, such as fine-tuning, parameter pruning, and model inversion attacks.
Crowd sensing is one of the core features of internet of vehicles, the use of internet of vehicles for crowd sensing is conducive to the rational allocation of sensing tasks. This paper mainly studies the problem of task allocation for crowd sensing in internet of vehicles, proposes a trajectory-based task allocation scheme for crowd sensing in internet of vehicles. With limited budget constraints, participants' trajectory is taken as an indicator of the spatiotemporal availability. Based on the solution idea of the minimal-cover problem, select the minimum number of participating vehicles to achieve the coverage of the target area.
Use-After-Free (UAF) vulnerabilities are caused by the program operating on a dangling pointer and can be exploited to compromise critical software systems. While there have been many tools to mitigate UAF vulnerabilities, UAF remains one of the most common attack vectors. UAF is particularly di cult to detect in concurrent programs, in which a UAF may only occur with rare thread schedules. In this paper, we present a novel technique, UFO, that can precisely predict UAFs based on a single observed execution trace with a provably higher detection capability than existing techniques with no false positives. The key technical advancement of UFO is an extended maximal thread causality model that captures the largest possible set of feasible traces that can be inferred from a given multithreaded execution trace. By formulating UAF detection as a constraint solving problem atop this model, we can explore a much larger thread scheduling space than classical happens-before based techniques. We have evaluated UFO on several real-world large complex C/C++ programs including Chromium and FireFox. UFO scales to real-world systems with hundreds of millions of events in their execution and has detected a large number of real concurrency UAFs.
Smart grid utilizes cloud service to realize reliable, efficient, secured, and cost-effective power management, but there are a number of security risks in the cloud service of smart grid. The security risks are particularly problematic to operators of power information infrastructure who want to leverage the benefits of cloud. In this paper, security risk of cloud service in the smart grid are categorized and analyzed characteristics, and multi-layered index system of general technical risks is established, which applies to different patterns of cloud service. Cloud service risk of smart grid can evaluate according indexes.