A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

记忆成像

感知和理解大规模动态场景需要高性能的成像系统。传统的成像系统通过简单地通过拼接相机提高像素分辨率来追求更高的性能,而牺牲了庞大的系统。此外,它们严格遵循前馈路径,即它们的像素级感知独立于语义理解。不同的是,人类视觉系统在前馈和反馈两种通路上都具有优势:前馈通路从视觉输入中提取物体表征(称为记忆印痕),而在反馈通路中,相关的印痕被重新激活以产生关于物体的假设。受此启发,我们提出了一种双通道成像机制,称为刻痕驱动摄像。我们从抽象场景的整体表示开始,它与本地细节双向关联,由实例级印痕驱动。从技术上讲,整个系统的工作原理是在兴奋-抑制和联想状态之间交替进行。在前一种状态下,像素级细节被动态整合或抑制,以加强实例级印记。在关联状态下,空间和时间上一致的内容在其印痕的驱动下被合成,以获得未来场景出色的录像质量。联想状态通过综合由其印痕驱动的空间和时间上一致的内容,作为未来场景的成像。大量的仿真和实验结果表明,该系统彻底改变了传统的录像模式,在多目标大场景的录像中显示出巨大的潜力。

Lightweight Method for Plant Disease Identification Using Deep Learning

In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.

RIS-Assisted UAV-D2D Communications Exploiting Deep Reinforcement Learning

Device-to-device(D2D)communications underlying cellular networks enabled by unmanned aerial vehicles(UAV)have been regarded as promising techniques for next-generation communications.To mitigate the strong interference caused by the line-of-sight(LoS)airto-ground channels,we deploy a reconfigurable intelligent surface(RIS)to rebuild the wireless channels.A joint optimization problem of the transmit power of UAV,the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service(QoS)requirement of cellular users.Due to the high channel dynamics and the coupling among cellular users,the RIS,and the D2D users,it is challenging to find a proper solution.Thus,a RIS softmax deep double deterministic(RIS-SD3)policy gradient method is proposed,which can smooth the optimization space as well as reduce the number of local optimizations.Specifically,the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced.Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user.Moreover,the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic(TD3)policy gradient algorithm in a dynamic environment.

High-sensitivity refractive index sensors based on Fano resonance in a metal-insulator-metal based arc-shaped resonator coupled with a rectangular stub

A metal-insulator-metal(MIM)-based arc-shaped resonator coupled with a rectangular stub(MARS) structure is proposed. This structure can generate two tunable Fano resonances originating from two different mechanisms. The structure has the advantage of being sensitive to the refractive index, and this feature makes it favorable for application in various microsensors. The relationship between the structural parameters and Fano resonance is researched using the finite element method(FEM) based on the software COMSOL Multiphysics 5.4. The simulation reveals that the sensitivity reaches1900 nm/refractive index unit(RIU), and the figure of merit(FOM) is 23.75.

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Potential behavior prediction involves understanding the latent human behavior of specific groups,and can assist organizations in making strategic decisions.Progress in information technology has made it possible to acquire more and more data about human behavior.In this paper,we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects(humans and actions)associated with various attributes and three types of relationships(human-human,human-action,and action-action),which we call the heterogeneous behavior network(HBN).To exploit the abundance and heterogeneity of the HBN,we propose a novel network embedding method,human-action-attribute-aware heterogeneous network embedding(a4 HNE),which jointly considers structural proximity,attribute resemblance,and heterogeneity fusion.Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

cFos-ANAB:A cFos-based Web Tool for Exploring Activated Neurons and Associated Behaviors

cFos is one of the most widely-studied genes in the field of neuroscience.Currently,there is no systematic database focusing on cFos in neuroscience.We developed a curated database-cFos-ANAB-a cFos-based web tool for exploring activated neurons and associated behaviors in rats and mice,comprising 398 brain nuclei and sub-nuclei,and five associated behaviors:pain,fear,feeding,aggression,and sexual behavior.Direct relationships among behaviors and nuclei(even cell types)under specific stimulating conditions were constructed based on cFos expression profiles extracted from original publications.Moreover,overlapping nuclei and sub-nuclei with potentially complex functions among different associated behaviors were emphasized,leading to results serving as important clues to the development of valid hypotheses for exploring as yet unknown circuits.Using the analysis function of cFos-ANAB,multi-layered pictures of networks and their relationships can quickly be explored depending on users’purposes.These features provide a useful tool and good reference for early exploration in neuroscience.The cFos-ANAB database is available at www.cfos-db.net.