With the rapid growth of IoT technology, Visual IoT (VIoT) plays a key role in areas like security surveillance and intelligent transportation, where large volumes of sensitive data are collected and transmitted. Many applications face limitations in computing and storage, such as challenges in managing traffic data. Recently, Compressed Sensing (CS) theory has been applied to improve data acquisition and processing efficiency. However, existing CS-based privacy methods focus mainly on protecting single privacy zones through key-based control and lack support for hierarchical protection across multiple sensitive regions. To address this, we propose a multi-level privacy-preserving scheme for VIoT data using CS, offering full, partial, and no access levels to meet varying security needs. We also implement watermarks to prevent key-sharing attacks among partially authorized users. Experimental results demonstrate that the scheme ensures data security while minimizing time and space overhead, making it suitable for resource-limited VIoT scenarios.

Current vulnerability detection methods encounter challenges, such as inadequate feature representation, constrained feature extraction capabilities, and coarse-grained detection. To address these issues, we propose a fine-grained hybrid semantic vulnerability detection framework based on Transformer, named TFHSVul. Initially, the source code is transformed into sequential and graph-based representations to capture multilevel features, thereby solving the problem of insufficient information caused by a single intermediate representation. To enhance feature extraction capabilities, TFHSVul integrates multiscale fusion convolutional neural network, residual graph convolutional network, and pretrained language model into the core architecture, significantly boosting performance. We design a fine-grained detection method based on a self-attention mechanism, achieving statement-level detection to address the issue of coarse detection granularity. In comparison to existing baseline methods on public data sets, TFHSVul achieves a 0.58 improvement in F1 score at the function level compared to the best performing model. Moreover, it demonstrates a 10% enhancement in Top-10 accuracy at the statement-level detection compared to the best performing method.

Malware poses a great threat to information security such as user data, privacy, and assets. Early detection before it has a real impact is the main countermeasure. However, the diversity of carriers and technologies has led to a huge gap between the training scenarios and actual scenarios of detection methods. This makes it difficult for supervision-based detection frameworks to identify new malware variants and complicates threat response. We propose a novel method that integrates frequency domain techniques with feature alignment to enhance variant malware detection, reducing distribution differences between labeled (source) and new (target) samples. By converting malware into grayscale images and applying discrete cosine transform (DCT) for improved feature extraction, followed by feature extraction via a deep residual network from both domains, our model systematically aligns features. This alignment is achieved through a tailored domain adaptation technique involving the minimization of classification and domain alignment losses, which ensures the consistent learning of features across varied domains. Such rigorous alignment not only enhances detection accuracy for both known and variant malware but also supports simultaneous detection across significant distribution differences. We conduct extensive experiments on two real-world datasets to evaluate the performance of various deep learning models under consistent and inconsistent domain distributions. Compared to existing methods, our approach improves accuracy by an average of 1.4% on the BIG2015 dataset, 3.2% on the MDA dataset, 2.75% on the Malimg dataset, and also achieves the best performance on the MaleVis dataset, with similar gains in precision, recall, and F1-score across all datasets.

Due to the unique constraints of industrial control data, industrial control adversarial sample attacks are particularly challenging. Existing methods strive to conduct adversarial sample attacks under the conditions of satisfying industrial control data constraints, however, the results are not ideal. Therefore, this study proposes a new adversarial sample generation method GNN-ASG based on double feature selection. GNN-ASG uses data constraints to ensure the rationality of generated data, and uses Graph Deviation Network (GDN) and Autoencoder to improve the quality and versatility of adversarial samples. A new adversarial sample evaluation metrics Adversarial Sample Attack Impact Rate (ASAIR) is proposed to address the problem that existing evaluation metrics are difficult to accurately judge the effectiveness of adversarial sample attacks. This method considers the principle and application environment of adversarial samples, and fully demonstrates the practical effect of adversarial samples. In a comprehensive experiment conducted on three public datasets, GNN-ASG achieves an impressive ASAIR of 21.83%, higher than existing methods of 13.94%. This paper demonstrates the versatility and effectiveness of GNN-ASG by comparing its performance with three state-of-the-art adversarial sample generation methods on four anomaly detection models. GNN-ASG can maximally reduce the F1-Score of the detection model by 0.7605.

Anomaly detection plays a vital role as a crucial security measure for edge devices in Artificial Intelligence and Internet of Things (AIoT). With the rapid development of IoT ( Internet of Things), changes in system configurations and the introduction of new devices can lead to significant alterations in device relationships and data flows within the IoT, thereby triggering concept drift. Previously trained anomaly detection models fail to adapt to the changed distribution of streaming data, resulting in a high number of false positive events. This paper aims to address the issue of concept drift in IoT anomaly detection by proposing a comprehensive Concept Drift Detection, Interpretation, and Adaptation framework (CDDIA). We focus on accurately capturing the concept drift of normal data in unsupervised scenarios. To interpret drift samples, we integrate a search optimization algorithm and the SHAP method, providing a comprehensive interpretation of drift samples at both the sample and feature levels. Simultaneously, by utilizing the sample-level interpretation results for filtering new and old samples, we retrain the anomaly detection model to mitigate the impact of concept drift and reduce the false positive rate. This integrated strategy demonstrates significant advantages in maintaining model stability and reliability. The experimental results indicate that our method outperforms five baseline methods in adaptability across three datasets and provides interpretability for samples experiencing concept drift.

Deep network-based image compressed sensing (CS) methods have attracted much attention in recent years due to their low reconstruction complexity and high reconstruction quality. However, the existing methods usually use one or multiple convolution layer(s) consisting of convolutional kernels with the same size to extract image features in image sampling, which results in incomplete feature extraction. Besides, the existing models usually focus on the extraction of deep features in image reconstruction, while ignoring the influence of shallow features. To overcome these issues, this paper proposes a multi-scale characteristic residual learning network (dubbed MSCRLNet) for image CS. In this network, convolutional kernels with different sizes are used to capture multi-level spatial features in image sampling, and a multi-scale residual network with channel attention is used to speed up network convergence in image reconstruction. Experiments show that the proposed MSCRLNet outperforms many existing state-of-the-art methods.

The data collected by sensors is streaming data in the Internet of Things (IoT). Although existing deep-learning-based anomaly detection methods generally perform well on static data, they struggle to respond timely to streaming data after distribution changes. However, streaming data suffers from conceptual drift due to the highly dynamic nature of IoT. In network security, concept drift-oriented anomaly detection is a crucial task, because it can adjust the model to adapt to the latest data, and detect attacks in time. Existing streaming anomaly detection methods are confronted with some challenges, including the latency of model updates, the uneven importance of new data, and the self-poisoning due to model self-updates. To tackle the above challenges, we propose a knowledge distillation-based adaptive anomaly detection model toward concept drift, ADTCD. ADTCD transfers the knowledge of the teacher model to the student model and only updates the student model to reduce the delay. We construct an algorithm of dynamically adjusting model parameters, which dynamically adjusts model weights through local inference on new samples, in order to improve the model’s responsiveness to new distribution data, meanwhile solving the problem of uneven importance of new data. In addition, we adopt a one-class support vector-based outlier removal method to tackle the self-poisoning problem. In comprehensive experiments on seven high-dimensional data sets, ADTCD achieves an AUC improvement of 12.46% compared to the state-of-the-art streaming anomaly detection methods. Our future direction will focus on exploring the concept-drift problem using methods beyond autoencoders.

Phase-change memory (PCM) is a nonvolatile memory which suffers slow write performance and limited write endurance. Besides, writing a one to a PCM cell needs longer time but less electrical current than writing a zero. In traditional PCM schemes, zeros and ones in a word are written at the same time and word write time has to be the time to write a one, thus incurring time waste. In this paper, we propose a three-stage write scheme with flip-bit for PCM main memory to reduce the number of changed bits and write latency. In our scheme, write operation is divided into comparison, write-0 and write-1 stages. In the comparison stage, new data and old data are compared and the new data is re-encoded by a flip-bit to minimize changed bits. Then the flip-bit and re-encoded data are written to PCM cells in an accelerating manner. All zero bits and one bits are written separately in later two stages to avoid the time waste in traditional write. Our scheme shrinks time consumption and reduces bit changes caused by write operation over other existing schemes. The experimental results show that this scheme decreases 43.5% bit changes, 16.6% write time and 34.6% write energy consumption on average.

On-chip memory management is essential in design of high performance and energy-efficient embedded systems. While many off-the-shelf embedded processors employ a hybrid on-chip SRAM architecture including both scratchpad memories (SPMs) and caches, many existing work on SPM management ignore the synergy between caches and SPMs. In this work, we propose a static SPM allocation strategy for the hybrid on-chip memory architecture in a multi-tasking environment, which minimizes the overall access latency and energy consumption of the instruction memory subsystem. We capture cache conflict misses via a fine-grained temporal cache behavior model. An integer linear programming (ILP) based formulation is proposed to generate an function-level SPM allocation scheme, where both intra- and inter-task cache interference as well as access frequency are captured for an optimal memory subsystem design. Compared with the state-of-the-art static SPM allocation strategy in a multitasking environment, experimental results show that our SPM management scheme achieves 30.51% further improvement in instruction memory subsystem performance, and up to 34.92% in terms of energy saving.

Due to the nature of distribution and self-organization, Mobile ad hoc networks rely on cooperation between nodes to transfer information. Therefore, one of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next cooperative potential nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All above makes it very difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition, we present a pattern of multi-dimensional fuzzy decision making with feedback. The analysis and experimental computation shows that this scheme is efficient in risk assessment of Ad hoc networks.

A mobile ad hoc network (MANET) is a self-organised system comprised of mobile wireless nodes. All nodes act as both communicators and routers. Owing to multi-hop routing and absence of centralised administration in open environment, MANETs are vulnerable to attacks by malicious nodes. In order to decrease the hazards from malicious nodes, the authors incorporate the concept of trust to MANETs and build a simple trust model to evaluate neighbours’ behaviours – forwarding packets. Extended from the ad hoc on-demand distance vector (AODV) routing protocol and the ad hoc on-demand multipath distance vector (AOMDV) routing protocol, a trust-based reactive multipath routing protocol, ad hoc on-demand trusted-path distance vector (AOTDV), is proposed for MANETs. This protocol is able to discover multiple loop-free paths as candidates in one route discovery. These paths are evaluated by two aspects: hop counts and trust values. This two-dimensional evaluation provides a flexible and feasible approach to choose the shortest path from the candidates that meet the requirements of data packets for dependability or trust. Furthermore, the authors give a routing example in details to describe the procedures of route discovery and the differences among AODV, AOMDV and AOTDV. Several experiments have been conducted to compare these protocols and the results show that AOTDV improves packet delivery ratio and mitigates the impairment from black hole, grey hole and modification attacks.

Data Stream Management Systems (DSMSs) usually need to satisfy multiple QoS requirements of applications, including timing and precision constraints. This paper focuses on the problem of real-time query model and scheduling in a DSMS to meet multiple performance objectives. At first, a mixed real-time query model is introduced which is composed of periodic, con-tinuous and one-time queries with deadlines. Moreover, an adaptive QoS-aware scheduling strategy, termed FC-TBS, is proposed to schedule these queries using feedback control mechanism. The objective of the FC-TBS strategy is to guaran-tee the deadlines of periodic queries and minimize the number of deadline violations for aperiodic queries. Besides the system tries to improve the overall query quality by adaptively adjusting CPU utilization factor for aperiodic queries according to work-load characteristics and application-defined relationship be-tween sample ratio and QoS. Experimental results show that the FC-TBS strategy is more effective than other mixed scheduling algorithms and can deal with workload fluctuations gracefully.

Synchronization and fault tolerance of processes are emphasis in the distributed systems research, but only a few people involves in the mathematics model used in processes synchronization and fault tolerance yet. This paper takes distributed system as an event driven system, classify the events that cause system state variety into four classes, and proposed an extended finite state machine (EFSM) with synchronization and fault tolerant message to the distributed system. Accordingly, a checkpoint set up algorithm based in this EFSM is proposed. During the establishing of the checkpoint, the consistency of checkpoint can be determined by calculating the number of sending and receiving messages. In case of lost message, sending and receiving process that lost message can be found by checking the number of sending and receiving messages, and the lost messages can be retransmitted and received. Thus the establishing of the distributed systems global state has been simplified.

Complex distributed real-time applications require complicated processing and sharing of an extensive amount of data under critical timing constraints. In this paper, we present a comprehensive overview of the Data Distribution Service standard (DDS) and describe its QoS (Quality of Service) features for developing real-time applications. Real-time ECA (RECA) rules are introduced to efficiently describe QoS policy in an active real-time database (ARTDB) named Agilor. And then we propose a novel QoS-Enable Real-Time Publish-Subscribe (QERTPS) service compatible to DDS for distributed real-time data acquisition. QERTPS could support several different QoS levels for various applications at the same time. Furthermore, QERTPS is implemented by object models and RECA rules in Agilor. To illustrate the benefits of QERTPS for real-time data acquisition, an example application is presented. Experimental evaluation shows that the proposed service provides a stable and timely service for providing different QoS levels.