The Controller Area Network (CAN) is the most widely used protocol for data transmission in in-vehicle networks. However, the lack of a robust security scheme makes it vulnerable to various cyber-attacks, posing significant threats to automotive functional safety. The limited computing resources of Electronic Control Units (ECUs), restricted network bandwidth, and high real-time requirements make it challenging to apply conventional encryption and authentication mechanisms. This paper proposes a lightweight secure communication scheme using encryption and authentication based on Physically Unclonable Function (PUF) technology. We first designed a ring oscillator PUF with a multi-loop comparison circuit (MLC-ROPUF) suitable for in-vehicle networks with constrained resources. The MLC-ROPUF is used to generate key parameter for the lightweight encryption algorithm Salsa20, which is applied to distribute shared key parameters to different ECUs. After receiving the CAN message, ECU utilizes the output response of MLC-ROPUF to generate the key stream of Salsa20 and decrypt the data segment of the CAN message, obtain the shared key parameters. The SM3 algorithm is then utilized to generate shared keys for confidential communication based on the obtained key parameters. Simultaneously, ECU identity authentication is completed to reduce communication costs. To validate the proposed scheme, we conducted experiments on a designed prototype system, and the results show that the proposed method performs well in terms of resource consumption and real-time capabilities.

In this work, we revisit the multi-item order fulfillment model introduced by [Jasin and Sinha 2015]. Specifically, we study a dynamic setting in which an e-commerce platform (or online retailer) with multiple warehouses and finite inventory is faced with the problem of fulfilling orders that may contain multiple items. The platform's goal is to minimize the expected cost incurred from the fulfillment process, subject to warehouses' inventory constraints. Unlike the classical literature on multi-item fulfillment, we propose an alternative offline formulation of the problem. In particular, in our model, the platform sequentially selects methods to fulfill the arriving orders. A method consists of a set of facilities that will determine which warehouses the items will ship from and, more importantly, whether multi-item orders will be split. Under this formulation, we design a class of dynamic policies that combine ideas from randomized fulfillment, prophet inequalities and subgradient methods for the general multi-item fulfillment model. Specifically, by establishing connections between the fulfillment and prophet inequality literature, we prove that our algorithm is both asymptotically optimal and has strong approximation guarantees in non-asymptotic settings. Our result shows that there is a simple and near-optimal procedure for solving multi-item fulfillment problems once the online retailer has enough inventory, independently of other problem parameters. To the best of our knowledge, this is the first result of this type in the context of multi-item order fulfillment. In addition, and of independent interest, our analysis also leads to new asymptotically optimal bounds for network revenue management problems.

Online transaction fraud has become increasingly rampant due to the convenience of mobile payment. Fraud detection is critical to ensure the security of online transactions. With the development of graph neural network, researchers have applied it to the field of fraud detection. The existing fraud detection methods will solve the class imbalance by sampling, but they do not fully consider the various imbalances in the heterogeneous graph, and the data imbalance will directly affect the performance of the model. This work proposes a hybrid graph neural network model for online fraud detection to address this issue. The three types of imbalance in online transactions are feature imbalance, category imbalance, and relation imbalance, and they are all addressed in the proposed model. The entities with the feature most closely related to the fraudsters will be determined for the feature imbalance, and samples will be taken for further identification in the subsequent training phase. The hybrid model then uses under-sampling in combination with the long-distance sampling to find nodes with high similarity of features for the category imbalance. Finally, we propose a reward/punishment mechanism based on reinforcement learning for relation imbalance, which uses the threshold created by training as the sampling weight between relations. This paper conducts experiments on the public datasets Amazon and Yelp. The experimental results show that the model proposed is 5.61% higher than the best model in the comparison model on Amazon dataset, and 1.58% higher on Yelp dataset.

As a widely used industrial field bus, the controller area network (CAN) lacks security mechanisms (e.g., encryption and authentication) and is vulnerable to security attacks (e.g., masquerade). A fingerprint-based intrusion detection system (IDS) in CAN networks can detect masquerade attacks by scanning the unique clock signals of CAN devices. However, most state-of-the-art fingerprint-based IDSs commonly use an analog-to-digital converter module with a low frequency of 60 MHz to sample CAN signals, lowering the detection accuracy of fingerprint-based IDSs. In addition, almost all fingerprint-based IDSs are trained offline and then detected online, ignoring that system clock signals of hardware change over time, resulting in degraded detection performance. This paper proposes an online learning-enabled and fingerprint-based IDS (OFIDS) in CAN networks to increase the sampling frequency, shorten the detection response time, and increase the detection accuracy. OFIDS uses a high-speed comparator (i.e., TLV3501) and FPGA (i.e., Xilinx ZYNQ-7010) to sample the CAN_High signal, achieving a low sampling delay time of 4.5 ns and a high sampling frequency of 1 GHz. The self-adaptability of the backpropagation neural network is taken advantage of and used to train the OFIDS model with a detection accuracy of 99.9992%. OFIDS is deployed to a CAN network prototype with five CAN devices (i.e., two Arduino UNO boards and three STM32 microcontrollers) and a real vehicle. Experimental results show that OFIDS can achieve at least 99.99% detection accuracy within 0.18μs in a CAN network prototype and can achieve 98% detection accuracy in a real vehicle.

As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial environment has generated a significant amount of network traffic. In this type of IoT industrial network where there is a large equipment heterogeneity, security is a fundamental issue; thus, it is very important to detect likely intrusion behaviors. Furthermore, since the proportion of labeled data records is small in the IoT environment, it is challenging to detect various attacks and intrusions accurately. This investigation builds a semisupervised ladder network model for intrusion detection in the Industrial IoT. This model considers the manifold distribution of high-dimensional data and incorporates a manifold regularization constraint in the decoder of the ladder network. Meanwhile, the feature propagation between layers is strengthened by adding more cross-layer connections in this model. On this basis, a random attention-based data fusion approach is proposed to generate global features for intrusion detection. The experiments on the CIC-IDS2018 dataset show that the proposed approach can recognize the intrusion with less false alarm rate, while model training is time efficient.

Reliability requirement assurance is an important quality of service (QoS) for workflow execution in cloud-based services. For a workflow with a reliability requirement, the enough replication for redundancy minimization (ERRM) and quantitative fault-tolerance with minimum execution cost + (QFEC+) algorithms are state-of-the-art algorithms to reduce the redundancy and cost, respectively. In this work, we define the reliability increment ratio (RIR) and propose the redundancy minimization using RIR (R_RIR) algorithm. In addition, we introduce the geometric mean and propose the cost reduction using geometric mean (C_GM) algorithm based on redundancy minimization. Experimental results show the proposed R_RIR and C_GM algorithms are superior to state-of-the-art algorithms: (1) although both R_RIR and ERRM show the same redundancy results, R_RIR is proven to generate minimal redundancy, whereas ERRM cannot; (2) R_RIR only consumes a few seconds to achieve minimal redundancy for large-scale workflows, and it has much higher time efficiency than ERRM; and (3) C_GM generates less cost than QFEC+ in a large part of cases.

Optimisation of timing performance and power consumption on heterogeneous multi-core architectures is gaining increasing attention. Systems and devices may have varying demands on timing and power, which motivates more flexible optimisation. Along this line, we consider a heterogeneous computing architecture with multiple cores, where each core runs a mixed stream of general and dedicated tasks with a certain scheduling strategy. Employing the queuing model, we first propose a load balancing algorithm, which minimises the average response time of the general tasks whilst guaranteeing the timing requirements of the dedicated tasks. Built upon the above, we propose a bi-directional optimisation algorithm that is able to improve the timing performance under the constraint of power consumption, and reduces the power consumption for the given timing requirement. Extensive numerical experiments illustrate the significance of the proposed algorithms. Implementation on a real platform validates the consistency between the theoretical analysis and the practical results.

Heterogeneous processors are increasingly being used in embedded systems where parallel applications with precedence-constrained tasks widely exist. Reliability is an important functional safety requirement and reliability goal should be satisfied for safety-critical parallel applications; meanwhile, resource is limited in embedded systems and it should be minimized. This study solves the problem of resource consumption cost minimization of a reliable parallel application on heterogeneous embedded systems without using fault tolerance. The problem is decomposed into two subproblems, namely, satisfying reliability goal and minimizing resource consumption cost. The first subproblem is solved by transferring the reliability goal of the application to that of each task, and the second subproblem is solved by heuristically assigning each task to the processor with the minimum resource consumption cost while satisfying its reliability goal. Experiments with real parallel applications verify that the proposed algorithm obtains minimum resource consumption costs compared with the state-of-the-art algorithms.

We formulate a Belief Propagation (BP) algorithm in the context of the capacitated minimum-cost network flow problem (ℳ ℱ). Unlike most of the instances of BP studied in the past, the messages of BP in the context of this problem are piecewise-linear functions. We prove that BP converges to the optimal solution in pseudo-polynomial time, provided that the optimal solution is unique and the problem input is integral. Moreover, we present a simple modification of the BP algorithm which gives a fully polynomial-time randomized approximation scheme (FPRAS) for ℳ ℱ. This is the first instance where BP is proved to have fully-polynomial running time.