The IoT consists of a lot of devices such as embedded systems, wireless sensor nodes (WSNs), control systems, etc. It is essential for some of these devices to protect information that they process and transmit. The issue is that an adversary may steal these devices to gain a physical access to the device. There is a variety of ways that allows to reveal cryptographic keys. One of them are optical Fault Injection attacks. We performed successful optical Fault Injections into different type of gates, in particular INV, NAND, NOR, FF. In our work we concentrate on the selection of the parameters configured by an attacker and their influence on the success of the Fault Injections.
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《計算機信息》雜志發表高質量的論文，擴大了運籌學和計算的范圍，尋求有關理論、方法、實驗、系統和應用方面的原創研究論文、新穎的調查和教程論文，以及描述新的和有用的軟件工具的論文。官網鏈接： ·
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損失函數（機器學習）
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Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting "poisoned" data into the training process, have attracted significant recent attention. In this work, we take a closer look at existing poisoning attacks and connect them with old and new algorithms for solving sequential Stackelberg games. By choosing an appropriate loss function for the attacker and optimizing with algorithms that exploit second-order information, we design poisoning attacks that are effective on neural networks. We present efficient implementations that exploit modern auto-differentiation packages and allow simultaneous and coordinated generation of tens of thousands of poisoned points, in contrast to existing methods that generate poisoned points one by one. We further perform extensive experiments that empirically explore the effect of data poisoning attacks on deep neural networks.
We introduce a subclass of concurrent game structures (CGS) with imperfect information in which agents are endowed with private data-sharing capabilities. Importantly, our CGSs are such that it is still decidable to model-check these CGSs against a relevant fragment of ATL. These systems can be thought as a generalisation of architectures allowing information forks, in the sense that, in the initial states of the system, we allow information forks from agents outside a given set A to agents inside this A. For this reason, together with the fact that the communication in our models underpins a specialised form of broadcast, we call our formalism A-cast systems. To underline, the fragment of ATL for which we show the model-checking problem to be decidable over A-cast is a large and significant one; it expresses coalitions over agents in any subset of the set A. Indeed, as we show, our systems and this ATL fragments can encode security problems that are notoriously hard to express faithfully: terrorist-fraud attacks in identity schemes.
Internet of things (IoT) connects all items to the Internet through information-sensing devices to exchange information for intelligent identification and management. Sybil attack is a famous and crippling attack in IoT. Most of the previous methods of detecting Sybil attacks in IoT mainly focus on static IoT while there are very rare methods applicable to mobile IoT. In this paper, a novel, lightweight, and distributive detection scheme based on edge computing is proposed for detecting Sybil attacks in mobile IoT. In the proposed scheme, a detection consists of two rounds. In each round, member nodes are required to send packets to edge nodes. Edge nodes calculate a possible interval of the received signal strength indication (RSSI) from the first round and check whether the RSSI from the second round is in the interval to detect Sybil attack. Extensive experimental studies are included to show that the presented approach outperforms many existing approaches in terms of true detection and false detection rates. Moreover, experimental results show that the fault tolerance design in the proposed approach greatly enhances the detection scheme.
Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review
Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learning (ML) to embed deep intelligence at the far edge. TinyML (Tiny Machine Learning) has enabled the deployment of ML models for embedded vision on extremely lean edge hardware, bringing the power of IoT and ML together. However, TinyML powered embedded vision applications are still in a nascent stage, and they are just starting to scale to widespread real-world IoT deployment. To harness the true potential of IoT and ML, it is necessary to provide product developers with robust, easy-to-use software engineering (SE) frameworks and best practices that are customized for the unique challenges faced in TinyML engineering. Through this systematic literature review, we aggregated the key challenges reported by TinyML developers and identified state-of-art SE approaches in large-scale Computer Vision, Machine Learning, and Embedded Systems that can help address key challenges in TinyML based IoT embedded vision. In summary, our study draws synergies between SE expertise that embedded systems developers and ML developers have independently developed to help address the unique challenges in the engineering of TinyML based IoT embedded vision.
Cross-site scripting (XSS) is the most common vulnerability class in web applications over the last decade. Much research attention has focused on building exploit mitigation defenses for this problem, but no technique provides adequate protection in the face of advanced attacks. One technique that bypasses XSS mitigations is the scriptless attack: a content injection technique that uses (among other options) CSS and HTML injection to infiltrate data. In studying this technique and others, we realized that the common property among the exploitation of all content injection vulnerabilities, including not just XSS and scriptless attacks, but also command injections and several others, is an unintended context switch in the victim program's parsing engine that is caused by untrusted user input. In this paper, we propose Context-Auditor, a novel technique that leverages this insight to identify content injection vulnerabilities ranging from XSS to scriptless attacks and command injections. We implemented Context-Auditor as a general solution to content injection exploit detection problem in the form of a flexible, stand-alone detection module. We deployed instances of Context-Auditor as (1) a browser plugin, (2) a web proxy (3) a web server plugin, and (4) as a wrapper around potentially-injectable system endpoints. Because Context-Auditor targets the root cause of content injection exploitation (and, more specifically for the purpose of our prototype, XSS exploitation, scriptless exploitation, and command injection), our evaluation results demonstrate that Context-Auditor can identify and block content injection exploits that modern defenses cannot while maintaining low throughput overhead and avoiding false positives.
Collision avoidance is a widely investigated topic in robotic applications. When applying collision avoidance techniques to a mobile robot, how to deal with the spatial structure of the robot still remains a challenge. In this paper, we design a configuration-aware safe control law by solving a Quadratic Programming (QP) with designed Control Barrier Functions (CBFs) constraints, which can safely navigate a mobile robotic arm to a desired region while avoiding collision with environmental obstacles. The advantage of our approach is that it correctly and in an elegant way incorporates the spatial structure of the mobile robotic arm. This is achieved by merging geometric restrictions among mobile robotic arm links into CBFs constraints. Simulations on a rigid rod and the modeled mobile robotic arm are performed to verify the feasibility and time-efficiency of proposed method. Numerical results about the time consuming for different degrees of freedom illustrate that our method scales well with dimension.
The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.
In this paper, two reputation based algorithms called Reputation and audit based clustering (RAC) algorithm and Reputation and audit based clustering with auxiliary anchor node (RACA) algorithm are proposed to defend against Byzantine attacks in distributed detection networks when the fusion center (FC) has no prior knowledge of the attacking strategy of Byzantine nodes. By updating the reputation index of the sensors in cluster-based networks, the system can accurately identify Byzantine nodes. The simulation results show that both proposed algorithms have superior detection performance compared with other algorithms. The proposed RACA algorithm works well even when the number of Byzantine nodes exceeds half of the total number of sensors in the network. Furthermore, the robustness of our proposed algorithms is evaluated in a dynamically changing scenario, where the attacking parameters change over time. We show that our algorithms can still achieve superior detection performance.
This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.
Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy. Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner. By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy. Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).
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