Semantic attacks against programmable logic controllers (PLCs) can manipulate industrial control systems (ICS) and cause severe physical disruptions. Yet, existing researches remain target-agnostic: they fail to determine which PLC tags or physical process are most critical when exploited. To address this gap, we introduce Pipe4Stux, a semantic attack framework that generates effective PLC attacks by leveraging key tags and attack semantics. Pipe4Stux first constructs a lexical analyzer and a data dependency subgraph from PLC control logic to capture the relationships among variables and operations. It then identifies key tags, derives semantic features of attacks, and formulates process-aware attack tactic. Experimental results on real ICS scenarios demonstrate that Pipe4Stux enables destructive and reproducible semantic attacks, revealing the potential impact of compromising key tags during specific physical states. In addition, Pipe4Stux provides the first systematic and objective methodology for semantic attack synthesis, offering practical guidance for defense. The framework can also support continuous monitoring by designating key tags as alarm variables within supervisory control and data acquisition (SCADA) systems such as WINCC.

PLC, as the core of industrial control systems, has been turned into a focal point of research for attackers targeting industrial control systems. However, current researched methods for attacking PLCs suffer from issues such as lack of precision and limited specificity. This paper proposes a novel attack method called AOIFF. Specially, AOIFF extracts the binary control logic code from a running PLC and reverses the binary code into assemble code. And then awareness of industrial field information is extracted from assemble code. Finally, it is based on awareness that attack code is generated and injected into a PLC, which can disrupt the normal control logic and then launch precise attacks on industrial control systems. Experimental results demonstrate that AOIFF can effectively perceive information in industrial field and initiate precise and targeted attacks on industrial control systems. Additionally, AOIFF achieves excellent results in the reverse engineering of binary code, enabling effective analysis of binary code.

In the field of change detection, detecting changes in images with different resolutions is crucial for both long-term interval scenes and scenarios that require rapid detection. However, existing methods face two main issues. Firstly, they require more stringent prior knowledge. The SPM-based methods require pixel-level class labeling of high-resolution(HR) images, while the approaches based on image super-resolution require HR images corresponding to low-resolutionr(LR) images. Such prior knowledge is either costly for labeling or difficult to meet in realistic scenarios. The second is that redundant error accumulation affects detection accuracy. Whether in traditional sub-pixel mapping(SPM) methods or deep learning methods based on image super-resolution, the final detection results are obtained after generating HR images from LR images. The redundant error produced in this step will be accumulated and affect the final detection results. Although the unsupervised methods do not have this problem, the detection accuracy is not as good as that of the supervised. To address these issues, we propose a multi-scale Transformer network(MTN). This model first uses a multi-scale feature extractor(MFE) to extract multi-scale features and perform scale matching at the feature level. Then, the Transformer is used to extract long-range relationships of ground objects on the multi-scale features to enhance the features. Finally, the multi-scale features are fused, and a classifier composed of a convolutional network is used to obtain binary change detection results. In addition, we consider that the edges of objects may be affected during the scale matching process, and introduce a CEBoundary Loss to better detect object edges. The results on the LEVIR and Google datasets demonstrate the effectiveness of our proposed method. The source code of MTN is available at https://github.com/Gavin-debug/MultiResolutionCD.

Satellite communication contributes tremendously to the Industrial Internet of Things (IIoT) with telecommunication efficiency and data accessibility at global-scale coverage. However, realizing proactive defense for satellite communication remains a challenge. In order to address such an issue, we first explore state-of-the-art proactive defense methods and, following next, a step forward on proposing an end hopping scheme based on fixed hopping timeslot and strict time synchronization strategy by utilizing moving target defense (MTD). In addition, we worked on a Proof of Concept (PoC) to evaluate the scheme’s theoretical protection performance for Distributed Denial of Service (DDoS). Experimental evaluation and analysis of the proposed scheme show that it is efficient and secure, as seen when the attack rate is 100 times/s, the response time of the hopping state is 69.98% shorter than that of the normal state, and when the attack rate is 1000 times/s, the response time of the hopping state is 90.15% shorter.

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume all the data are already in one place, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with “minimal” communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while a 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing.

Nowadays, Flying Ad hoc NETworks (FANETs) which consists of multiple Unmanned Aerial Vehicles (UAVs) are widely used in various military and civilian applications which need different Quality of Service (QoS) guarantees, for example, low delay for safety packet and high throughput for service packet. Meanwhile, to provide high bandwidth and spatial reuse, directional antennas are equipped on the UAVs more and more. However, due to the high mobility of UAVs, how to support different QoS with directional antenna is challenging for Media Access Control (MAC) protocol of FANETs. Recently, multi-channel MAC protocol has been proved to be effective to support different QoS. In this paper, we propose a FANETs multi-channel MAC protocol called FM-MAC, which combines the advantages of multi-channel and directional antenna to provide different QoS guarantees. Firstly, a reservation scheme based on mobile prediction is proposed to address the link interruption issue brought by high mobility of UAVs. Secondly, we propose a preemption mechanism to provide priority for service packets. Simulation results show that compared with other two representative protocols, FM-MAC not only improves the throughput of service packets, but also achieves lower delay and higher reliability for safety packets.

An interesting target detection framework with transferred deep convolutional neural network (CNN) is proposed. For CNN, many labeled samples are needed to train the multi-layer network. However, for target detection tasks, only few target spectral signatures are available, or they are unknown in anomaly detection. In this work, we employ a reference data and further generate pixel-pairs to enlarge the sample size. A multi-layer CNN is trained by using difference between pixel-pairs generated from the reference image scene. During testing, there are two cases: (1) for anomaly detection, difference between pixel-pairs, constructed by combing the center pixel and its surrounding pixels, is classified by the trained CNN with result of similarity measurement; and (2) for supervised target detection, difference between pixel-pairs, constructed by combing the testing pixel and the known spectral signatures, is classified. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed strategy outperforms the classic detectors.

In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage of CNN can be realized only if the number of training samples is sufficient. A multilayer CNN is trained by using difference between pixel pairs generated from the reference image scene. Then, for each pixel in the image for anomaly detection, difference between pixel pairs, constructed by combining the center pixel and its surrounding pixels, is classified by the trained CNN with the result of similarity measurement. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed algorithm outperforms the classic Reed-Xiaoli and the state-of-the-art representation-based detectors, such as sparse representation-based detector (SRD) and collaborative representation-based detector.

The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.