Biblio

Language-based information flow control (IFC) aims to provide guarantees about information propagation in computer systems having multiple security levels. Existing IFC systems extend the lattice model of Denning's, enforcing transitive security policies by tracking information flows along with a partially ordered set of security levels. They yield a transitive noninterference property of either confidentiality or integrity. In this paper, we explore IFC for security policies that are not necessarily transitive. Such nontransitive security policies avoid unwanted or unexpected information flows implied by transitive policies and naturally accommodate high-level coarse-grained security requirements in modern component-based software. We present a novel security type system for enforcing nontransitive security policies. Unlike traditional security type systems that verify information propagation by subtyping security levels of a transitive policy, our type system relaxes strong transitivity by inferring information flow history through security levels and ensuring that they respect the nontransitive policy in effect. Such a type system yields a new nontransitive noninterference property that offers more flexible information flow relations induced by security policies that do not have to be transitive, therefore generalizing the conventional transitive noninterference. This enables us to directly reason about the extent of information flows in the program and restrict interactions between security-sensitive and untrusted components.

As the next-generation network architecture, Information-Centric Networking (ICN) has emerged as a novel paradigm to cope with the increasing demand for content delivery on the Internet. In contrast to the conventional host-centric architectures, ICN focuses on content retrieval based on their name rather than their storage location. However, ICN is vulnerable to various security and privacy attacks due to the inherent attributes of the ICN architectures. For example, a curious ICN node can monitor the network traffic to reveal the sensitive data issued by specific users. Hence, further research on privacy protection for ICN is needed. This paper presents a practical approach to effectively enhancing the security and privacy of ICN by utilizing Intel SGX, a commodity trusted execution environment. The main idea is to leverage secure enclaves residing on ICN nodes to do computations on sensitive data. Performance evaluations on the real-world datasets demonstrate the efficiency of the proposed scheme. Moreover, our scheme outperforms the cryptography based method.

This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.

This paper proposes an approach that combines a deep learning-based method and a traditional machine learning-based method to efficiently detect malicious requests Web servers received. The first few layers of Convolutional Neural Network for Text Classification (TextCNN) are used to automatically extract powerful semantic features and in the meantime transferable statistical features are defined to boost the detection ability, specifically Web request parameter tampering. The semantic features from TextCNN and transferable statistical features from artificially-designing are grouped together to be fed into Support Vector Machine (SVM), replacing the last layer of TextCNN for classification. To facilitate the understanding of abstract features in form of numerical data in vectors extracted by TextCNN, this paper designs trace-back functions that map max-pooling outputs back to words in Web requests. After investigating the current available datasets for Web attack detection, HTTP Dataset CSIC 2010 is selected to test and verify the proposed approach. Compared with other deep learning models, the experimental results demonstrate that the approach proposed in this paper is competitive with the state-of-the-art.

Blockchain technology has attracted much attention due to the great success of the cryptocurrencies. Owing to its immutability property and consensus protocol, blockchain offers a new solution for trusted storage and computation services. To scale up the services, prior research has suggested a hybrid storage architecture, where only small meta-data are stored onchain and the raw data are outsourced to off-chain storage. To protect data integrity, a cryptographic proof can be constructed online for queries over the data stored in the system. However, the previous schemes only support simple key-value queries. In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. The key challenge lies in how to design an authenticated data structure (ADS) that can be efficiently maintained by the blockchain, in which a unique gas cost model is employed. By analyzing the performance of the existing techniques, we propose a novel ADS, called GEM2-tree, which is not only gas-efficient but also effective in supporting authenticated queries. To further reduce the ADS maintenance cost without sacrificing much the query performance, we also propose an optimized structure, GEM2*-tree, by designing a two-level index structure. Theoretical analysis and empirical evaluation validate the performance of the proposed ADSs.

Software verification has been well applied in safety critical areas and has shown the ability to provide better quality assurance for modern software. However, as lines of code and complexity of software systems increase, the scalability of verification becomes a challenge. In this paper, we present an automatic software verification framework TSV to address the scalability issues: (i) the extended structural abstraction and property-guided program slicing to solve large-scale program verification problem, saving time and memory without losing accuracy; (ii) automatically select different verification methods according to the program and property context to improve the verification efficiency. For evaluation, we compare TSV's different configurations with existing C program verifiers based on open benchmarks. We found that TSV with auto-selection performs better than with bounded model checking only or with extended structural abstraction only. Compared to existing tools such as CMBC and CPAChecker, it acquires 10%-20% improvement of accuracy and 50%-90% improvement of memory consumption.

The SDN (Software Defined Networking) paradigm rings flexibility to the network management and is an enabler to offer huge opportunities for network programmability. And, to solve the scalability issue raised by the centralized architecture of SDN, multi-controllers deployment (or distributed controllers system) is envisioned. In this paper, we focus on increasing the diversity of SDN control plane so as to enhance the network security. Our goal is to limit the ability of a malicious controller to compromise its neighboring controllers, and by extension, the rest of the controllers. We investigate a heterogeneous Susceptible-Infectious-Susceptible (SIS) epidemic model to evaluate the security performance and propose a coloring algorithm to increase the diversity based on community detection. And the simulation results demonstrate that our algorithm can reduce infection rate in control plane and our work shows that diversity must be introduced in network design for network security.

Third-party IME (Input Method Editor) apps are often the preference means of interaction for Android users' input. In this paper, we first discuss the insecurity of IME apps, including the Potentially Harmful Apps (PHA) and malicious IME apps, which may leak users' sensitive keystrokes. The current defense system, such as I-BOX, is vulnerable to the prefix-substitution attack and the colluding attack due to the post-IME nature. We provide a deeper understanding that all the designs with the post-IME nature are subject to the prefix-substitution and colluding attacks. To remedy the above post-IME system's flaws, we propose a new idea, pre-IME, which guarantees that "Is this touch event a sensitive keystroke?" analysis will always access user touch events prior to the execution of any IME app code. We designed an innovative TrustZone-based framework named IM-Visor which has the pre-IME nature. Specifically, IM-Visor creates the isolation environment named STIE as soon as a user intends to type on a soft keyboard, then the STIE intercepts, translates and analyzes the user's touch input. If the input is sensitive, the translation of keystrokes will be delivered to user apps through a trusted path. Otherwise, IM-Visor replays non-sensitive keystroke touch events for IME apps or replays non-keystroke touch events for other apps. A prototype of IM-Visor has been implemented and tested with several most popular IMEs. The experimental results show that IM-Visor has small runtime overheads.

Electro-hydraulic servo actuation system is a mechanical, electrical and hydraulic mixing complex system. If it can't be repaired for a long time, it is necessary to consider the possibility of occurrence of multiple faults. Considering this possibility, this paper presents an extended Kalman filter (EKF) based method for multiple faults diagnosis. Through analysing the failure modes and mechanism of the electro-hydraulic servo actuation system and modelling selected typical failure modes, the relationship between the key parameters of the system and the faults is obtained. The extended Kalman filter which is a commonly used algorithm for estimating parameters is used to on-line fault diagnosis. Then use the extended Kalman filter to diagnose potential faults. The simulation results show that the multi-fault diagnosis method based on extended Kalman filter is effective for multi-fault diagnosis of electro-hydraulic servo actuation system.