Biblio

Analyzing the security of closed source binaries is currently impractical for end-users, or even developers who rely on third-party libraries. Such analysis relies on automatic vulnerability discovery techniques, most notably fuzzing with sanitizers enabled. The current state of the art for applying fuzzing or sanitization to binaries is dynamic binary translation, which has prohibitive performance overhead. The alternate technique, static binary rewriting, cannot fully recover symbolization information and hence has difficulty modifying binaries to track code coverage for fuzzing or to add security checks for sanitizers.The ideal solution for binary security analysis would be a static rewriter that can intelligently add the required instrumentation as if it were inserted at compile time. Such instrumentation requires an analysis to statically disambiguate between references and scalars, a problem known to be undecidable in the general case. We show that recovering this information is possible in practice for the most common class of software and libraries: 64-bit, position independent code. Based on this observation, we develop RetroWrite, a binary-rewriting instrumentation to support American Fuzzy Lop (AFL) and Address Sanitizer (ASan), and show that it can achieve compiler-level performance while retaining precision. Binaries rewritten for coverage-guided fuzzing using RetroWrite are identical in performance to compiler-instrumented binaries and outperform the default QEMU-based instrumentation by 4.5x while triggering more bugs. Our implementation of binary-only Address Sanitizer is 3x faster than Valgrind's memcheck, the state-of-the-art binary-only memory checker, and detects 80% more bugs in our evaluation.

The root causes of many security vulnerabilities include a pernicious combination of two problems, often regarded as inescapable aspects of computing. First, the protection mechanisms provided by the mainstream processor architecture and C/C++ language abstractions, dating back to the 1970s and before, provide only coarse-grain virtual-memory-based protection. Second, mainstream system engineering relies almost exclusively on test-and-debug methods, with (at best) prose specifications. These methods have historically sufficed commercially for much of the computer industry, but they fail to prevent large numbers of exploitable bugs, and the security problems that this causes are becoming ever more acute.In this paper we show how more rigorous engineering methods can be applied to the development of a new security-enhanced processor architecture, with its accompanying hardware implementation and software stack. We use formal models of the complete instruction-set architecture (ISA) at the heart of the design and engineering process, both in lightweight ways that support and improve normal engineering practice - as documentation, in emulators used as a test oracle for hardware and for running software, and for test generation - and for formal verification. We formalise key intended security properties of the design, and establish that these hold with mechanised proof. This is for the same complete ISA models (complete enough to boot operating systems), without idealisation.We do this for CHERI, an architecture with hardware capabilities that supports fine-grained memory protection and scalable secure compartmentalisation, while offering a smooth adoption path for existing software. CHERI is a maturing research architecture, developed since 2010, with work now underway on an Arm industrial prototype to explore its possible adoption in mass-market commercial processors. The rigorous engineering work described here has been an integral part of its development to date, enabling more rapid and confident experimentation, and boosting confidence in the design.

In this paper, an Intrusion Detection System (IDS) in the Controller Area Network (CAN) bus of modern vehicles has been proposed. NESLIDS is an anomaly detection algorithm based on the supervised Deep Neural Network (DNN) architecture that is designed to counter three critical attack categories: Denial-of-service (DoS), fuzzy, and impersonation attacks. Our research scope included modifying DNN parameters, e.g. number of hidden layer neurons, batch size, and activation functions according to how well it maximized detection accuracy and minimized the false positive rate (FPR) for these attacks. Our methodology consisted of collecting CAN Bus data from online and in real-time, injecting attack data after data collection, preprocessing in Python, training the DNN, and testing the model with different datasets. Results show that the proposed IDS effectively detects all attack types for both types of datasets. NESLIDS outperforms existing approaches in terms of accuracy, scalability, and low false alarm rates.

This paper studies the current status and future directions of RTOS (Real-Time Operating Systems) for time-sensitive CPS (Cyber-Physical Systems). GPOS (General Purpose Operating Systems) existed before RTOS but did not meet performance requirements for time sensitive CPS. Many GPOS have put forward adaptations to meet the requirements of real-time performance, and this paper compares RTOS and GPOS and shows their pros and cons for CPS applications. Furthermore, comparisons among select RTOS such as VxWorks, RTLinux, and FreeRTOS have been conducted in terms of scheduling, kernel, and priority inversion. Various tools for WCET (Worst-Case Execution Time) estimation are discussed. This paper also presents a CPS use case of RTOS, i.e. JetOS for avionics, and future advancements in RTOS such as multi-core RTOS, new RTOS architecture and RTOS security for CPS.

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.

As a general interface for chip system testing and on-chip debugging, JTAG is facing serious security threats. By analyzing the typical JTAG attack model and security protection measures, this paper designs a secure JTAG debugging model based on Schnorr identity authentication protocol, and takes RISCV as an example to build a set of SoC prototype system to complete functional verification. Experiments show that this secure JTAG debugging model has high security, flexible implementation, and good portability. It can meet the JTAG security protection requirements in various application scenarios. The maximum clock frequency can reach 833MHZ, while the hardware overhead is only 47.93KGate.

The rise of supply chain finance has provided effective assistance to SMEs with financing difficulties. This study mainly explores the financial risk evaluation of supply chain under the leadership of 3PL. According to the risk identification, 27 comprehensive rating indicators were established, and then the model under the BP neural network was constructed through empirical data. The actual verification results show that the model performs very well in risk assessment which helps 3PL companies to better evaluate the business risks of supply chain finance, so as to take more effective risk management measures.

IoT and big data are energetic technology of the world for quite a time, and both of these have become a necessity. On the one side where IoT is used to connect different objectives via the internet, the big data means having a large number of the set of structured, unstructured, and semi-structured data. The device used for processing based on the tools used. These tools help provide meaningful information used for effective management in different domains. Some of the commonly faced issues with the inadequate about the technologies are related to data privacy, insufficient analytical capabilities, and this issue is faced by in different domains related to the big data. Data analytics tools help discover the pattern of data and consumer preferences which is resulting in better decision making for the organizations. The major part of this work is to review different types of data analytics techniques for the effective management of big data using IoT. For the effective management of the ABD solution collection, analysis and control are used as the components. Each of the ingredients is described to find an effective way to manage big data. These components are considered and used in the validation criteria. The solution of effective data management is a stage towards the management of big data in IoT devices which will help the user to understand different types of elements of data management.

Using steganography for data hiding is becoming a main subject to ensure both information security and picture quality. Traditional steganography algorithms usually convert secret information into a binary string and embed it in the pixel data of the cover image. In order to ensure the information security as well as convenient transmission, this work studies the steganography algorithm of embedding the QR code containing secret information into the cover image, based on the JSteg algorithm. Secret messages with different sizes have been tested by many cover images and standard parameters have adopted to verify the efficiency. According to the experimental results, all the PSNR in a value that is greater than 47.6 dB. The proposed method has high security and more imperceptibility.

The most important advantage of database is that it can form an intensive management system and serve a large number of information users, which shows the importance of information security in network development. However, there are many problems in the current computer software engineering industry, which seriously hinder the development of computer software engineering, among which the most remarkable and prominent one is that the database programming technology is difficult to be effectively utilized. In this paper, virtualization technology is used to manage the underlying resources of data center with the application background of big data technology, and realize the virtualization of network resources, storage resources and computing resources. It can play a constructive role in the construction of data center, integrate traditional and old resources, realize the computing data center system through virtualization, distributed storage and resource scheduling, and realize the clustering and load balancing of non-relational databases.

With the development of business, management companies are currently facing a series of problems and challenges in terms of resource allocation and task management. In terms of the technical route, this research will use cloud services to implement the public honesty system, and carry out secondary development and interface development on this basis, the architecture design and the formulation of the process are realized for various types, relying on the support of the knowledge base and case library, through the system intelligent configuration corresponding work instructions, safety work instructions, case references and other reference information to the existing work plan to provide managers Reference; managers can configure and adjust the work content by themselves through specific requirements to efficiently and flexibly adapt to the work content.

In recent years, the Internet of Things technology has developed rapidly. RFID technology, as an important branch of the Internet of Things technology, is widely used in logistics, medical, military and other fields. RFID technology not only brings convenience to people's production and life, but also hides many security problems. However, the current research on RFID technology mainly focuses on the technology application, and there are relatively few researches on its security analysis. This paper firstly studies the authentication mechanism and storage mechanism of RFID technology, then analyzes the common vulnerabilities of RFID, and finally gives the security protection suggestions.

In STOC 1988, Ben-Or, Goldwasser, and Wigderson (BGW) established an important milestone in the fields of cryptography and distributed computing by showing that every functionality can be computed with perfect (information-theoretic and error-free) security at the presence of an active (aka Byzantine) rushing adversary that controls up to n/3 of the parties. We study the round complexity of general secure multiparty computation in the BGW model. Our main result shows that every functionality can be realized in only four rounds of interaction, and that some functionalities cannot be computed in three rounds. This completely settles the round-complexity of perfect actively-secure optimally-resilient MPC, resolving a long line of research. Our lower-bound is based on a novel round-reduction technique that allows us to lift existing three-round lower-bounds for verifiable secret sharing to four-round lower-bounds for general MPC. To prove the upper-bound, we develop new round-efficient protocols for computing degree-2 functionalities over large fields, and establish the completeness of such functionalities. The latter result extends the recent completeness theorem of Applebaum, Brakerski and Tsabary (TCC 2018, Eurocrypt 2019) that was limited to the binary field.

To achieve a substantial reliability and safety level, it is imperative to provide electronic computing systems with appropriate mechanisms to tackle soft errors. This paper proposes a low-cost system-level soft error mitigation technique, which allocates the critical application function to a pool of specific general-purpose processor registers. Both the critical function and the register pool are automatically selected by a developed profiling tool. The proposed technique was validated through more than 320K fault injections considering a Linux kernel, different benchmarks and two multicore ARM processors. Results show that our technique significantly reduces the code size and performance overheads while providing reliability improvement, w.r.t. the Triple Modular Redundancy (TMR) technique.

The Internet of things is an extremely enormous space and still, IoT is spreading over a wide range of zones of development with very fast speed. The IoT is going to create a new world of efficient services. IoT is a collective system consisting of hardware like sensors, Radio Frequency Identification RFID, Bluetooth devices, Near Field Communication (NFC) devices, etc. and software that provides data queries, exchange, repository and exchanges, etc. Security of the IoT network is also a big and important issue of concern. This paper reviews the DDoS attack impact on IoT network and its mitigation methods for IoT in network, also discusses CoAP protocol, RPL protocol and 6LoWPAN network. This paper also represents the security framework to detect and monitor the DDoS attack for low power devices based IoT network.

Face capturing is a task to capture and store the "best" face of each person passing by the monitor. To some extent, it is similar to face tracking, but uses a different criterion and requires a valuable (i.e., high-quality and recognizable) face selection procedure. Face capturing systems play a critical role in public security. When deployed on edge devices, it is capable of reducing redundant storage in data center and speeding up retrieval of a certain person. However, high computation complexity and high repetition rate caused by ID switch errors are major challenges. In this paper, we propose a novel solution to constructing a real-time low-repetition face capturing system on chips. First, we propose a two-stage association algorithm for memory-efficient and accurate face tracking. Second, we propose a fast and reliable face quality estimation algorithm for valuable face selection. Our pipeline runs at over 20fps on Hisiv 3559A SoC with a single NNIE device for neural network inference, while achieving over 95% recall and less than 0.4 repetition rate in real world surveillance videos.

The full integration of Remotely Piloted Aircraft Systems (RPAS) in non-segregated airspace is one of the major objectives for the worldwide aviation organizations and authorities. However, there are several technological and regulatory issues due to the increase of the air traffic in the next years and to the need of keeping high safety levels. In this framework, a real-time validation environment capable to simulate complex scenarios related to future air traffic management (ATM) conditions is of paramount importance. These facilities allow detailed testing and tuning of new technologies and procedures before executing flight tests. With such motivations, the Italian Aerospace Research Centre has developed the Integrated Simulation Facility (ISF) able to accurately reproduce ATM complex scenarios in real-time with hardware and human in-the-loop simulations, aiming to validate new ATM procedures and innovative system prototypes for RPAS and General Aviation aircraft. In the present work, the ISF facility has been used for reproducing relevant ATM scenarios to validate the functionalities of a Detect and Avoid system (DAA). The results of the ISF test campaign demonstrate the effectiveness of the developed algorithm in the autonomous resolution of mid-air collisions in presence of both air traffic and fixed obstacles (i.e. bad weather areas, no-fly-zone and terrain) and during critical flight phases, thus exceeding the current DAA state-of-the-art.

One of the typical autonomous driving systems is a human-machine cooperative system that intervenes in the driver operation. The autonomous driving needs to make consideration of the driver individuality in addition to safety. This paper considers a human-machine cooperative system balancing safety with the driver individuality using the Human-In-The-Loop System (HITLS) for rear-wheel steering control. This paper assumes that it is safe for HITLS to follow the target side-slip angle and target angular velocity without conflicts between the controller and driver operations. We propose HITLS using the primal-dual algorithm and the internal model control (IMC) type I-PD controller. In HITLS, the signal expander delimits the human-selectable operating range and the controller cooperates stably the human operation and automated control in that range. The primal-dual algorithm realizes the driver and the signal expander. Our outcomes are the making of the rear-wheel steering system which converges to the target value while reflecting the driver individuality.

Information-Centric Networking (ICN) is attracting attention as a content distribution method against increasing network traffic. Content distribution in ICN adopts a pull-type communication method that returns data to Interest. However, in this case, the push-type communication method is advantageous. Therefore, the authors have proposed a method in which a server pushes content to reduce the node load in an environment where a large amount of Interest to specific content occurs in a short time. In this paper, we analyze the packet processing delay time with and without the proposed method in an environment where a router processes a large number of packets using a simulator. Simulation results show that the proposed method can reduce packet processing delay time and node load.

The paper is devoted to the development and research of the redundancy cyber resiliency technique based on fast rerouting under security metric with the implementation of the basic schemes for network elements protection, namely node, link, path, and bandwidth. Within the model, the secure fast rerouting task is formulated as an optimization problem of nonlinear programming. The model is configured in order to calculate primary and backup paths that contain links with the minimum values of the probability of compromise that is achieved by using the appropriate weights in the objective function, the value of which is minimized. Numerical research has been conducted, results of which proved the proposed model efficiency and adequacy for the practical application.

The internal structure of the flexible-DC transformer is complicated and the lack of a reliable vibration calculation model limits the application of the vibration analysis method in the fault diagnosis of the flexible-DC transformer. In response to this problem, this paper analyzes the correlation between the vibration signals of multiple measuring points and establishes a ``black box'' model of transformer vibration detection. Using the correlation analysis of multiple measuring points and BP neural network, a ``black box'' model that simulates the internal vibration transmission relationship of the transformer is established. The vibration signal of the multiple measuring points can be used to calculate the vibration signal of the target measuring point under specific working conditions. This can provide effective information for fault diagnosis and judgment of the running status of the flexible-DC transformer.

Code injection attacks, like the one used in the high-profile 2017 Equifax breach, have become increasingly common, now ranking \#1 on OWASP's list of critical web application vulnerabilities. Static analyses for detecting these vulnerabilities can overwhelm developers with false positive reports. Meanwhile, most dynamic analyses rely on detecting vulnerabilities as they occur in the field, which can introduce a high performance overhead in production code. This paper describes a new approach for detecting injection vulnerabilities in applications by harnessing the combined power of human developers' test suites and automated dynamic analysis. Our new approach, Rivulet, monitors the execution of developer-written functional tests in order to detect information flows that may be vulnerable to attack. Then, Rivulet uses a white-box test generation technique to repurpose those functional tests to check if any vulnerable flow could be exploited. When applied to the version of Apache Struts exploited in the 2017 Equifax attack, Rivulet quickly identifies the vulnerability, leveraging only the tests that existed in Struts at that time. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks, finding that Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities.

Cloud computing is applied in various domains, among which social recommender systems are well-received because of their effectivity to provide suggestions for users. Social recommender systems perform well in alleviating cold start problem, but it suffers from shilling attack due to its natural openness. Shilling attack is an injection attack mainly acting on the training process of machine learning, which aims to advance or suppress the recommendation ranking of target items. Some researchers have studied the influence of shilling attacks in two perspectives simultaneously, which are user-item's rating and user-user's relation. However, they take more consideration into user-item's rating, and up to now, the construction of user-user's relation has not been explored in depth. To explore shilling attacks with complex relations, in this paper, we propose two novel attack models based on triangle relations in social networks. Furthermore, we explore the influence of these models on five social recommendation algorithms. The experimental results on three datasets show that the recommendation can be affected by the triangle relation attacks. The attack model combined with triangle relation has a better attack effect than the model only based on rating injection and the model combined with random relation. Besides, we compare the functions of triangle relations in friend recommendation and product recommendation.

Container technology has been used for running multiple isolated operating system distros on a host or deploying large scale microservice-based applications. In most cases, containers share the same kernel with the host and other containers on the same host, and the application in the container can make system calls of the host kernel like a normal process on the host. Seccomp is a security mechanism for the Linux kernel, through which we can prohibit certain system calls from being executed by the program. Docker began to support the seccomp mechanism from version 1.10 and disables around 44 system calls out of 300+ by default. However, for a particular container, there are still many system calls that are unnecessary for running it allowed to be executed, and the abuse of system calls by a compromised container can trigger the security vulnerabilities of a host kernel. Unfortunately, Docker does not provide a way to get the necessary system calls for a particular container. In this paper, we propose RSDS, a method combining dynamic analysis and static analysis to get the necessary system calls for a particular container. Our experiments show that our solution can reduce system calls by 69.27%-85.89% compared to the default configuration on an x86-64 PC with Ubuntu 16.04 host OS and does not affect the functionalities of these containers.

Recent work has quantified the degradations that occur in convolutional neural nets (CNN) deployed in harsh environments like space-based image or radio frequency (RF) processing applications. Such degradations yield a robust correlation and causality between single-event upset (SEU) induced errors in memory weights of on-orbit CNN implementations. However, minimal considerations have been given to how the resilience of CNNs can be improved algorithmically as opposed to via enhanced hardware. This paper focuses on RF-processing CNNs and performs an in-depth analysis of applying software-based error detection and correction mechanisms, which may subsequently be combined with protections of radiation-hardened processor platforms. These techniques are more accessible for low cost smallsat platforms than ruggedized hardware. Additionally, methods for minimizing the memory and computational complexity of the resulting resilience techniques are identified. Combined with periodic scrubbing, the resulting techniques are shown to improve expected lifetimes of CNN-based RF-processing algorithms by several orders of magnitude.