Proactive Defense for Internet-of-things: Moving Target Defense With Cyberdeception

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software-defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address “when” to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address “how” to perform network topology shuffling on a decoy-populated IoT network, and we analyze which strategy can best achieve a system goal, such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. We demonstrated that a software-defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of “when” and “how” strategies is identified for executing our proposed technique under which the specified goal can be best achieved.

ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR

Most existing SAR moving target shadow detectors not only tend to generate missed detections because of their limited feature extraction capacity among complex scenes, but also tend to bring about numerous perishing false alarms due to their poor foreground–background discrimination capacity. Therefore, to solve these problems, this paper proposes a novel deep learning network called “ShadowDeNet” for better shadow detection of moving ground targets on video synthetic aperture radar (SAR) images. It utilizes five major tools to guarantee its superior detection performance, i.e., (1) histogram equalization shadow enhancement (HESE) for enhancing shadow saliency to facilitate feature extraction, (2) transformer self-attention mechanism (TSAM) for focusing on regions of interests to suppress clutter interferences, (3) shape deformation adaptive learning (SDAL) for learning moving target deformed shadows to conquer motion speed variations, (4) semantic-guided anchor-adaptive learning (SGAAL) for generating optimized anchors to match shadow location and shape, and (5) online hard-example mining (OHEM) for selecting typical difficult negative samples to improve background discrimination capacity. We conduct extensive ablation studies to confirm the effectiveness of the above each contribution. We perform experiments on the public Sandia National Laboratories (SNL) video SAR data. Experimental results reveal the state-of-the-art performance of ShadowDeNet, with a 66.01% best f1 accuracy, in contrast to the other five competitive methods. Specifically, ShadowDeNet is superior to the experimental baseline Faster R-CNN by a 9.00% f1 accuracy, and superior to the existing first-best model by a 4.96% f1 accuracy. Furthermore, ShadowDeNet merely sacrifices a slight detection speed in an acceptable range.

Data Mining Algorithm for Moving Object Trajectory

Aiming at the poor effect and long recognition time of data mining algorithm for moving target trajectory recognition, a data mining algorithm based on improved Hausdorff distance is proposed. The position and angle of abnormal trajectory data are detected by calculating the distance between trajectory classification and sub trajectory line segments, and the trajectory unit is established by using the improved Hausdorff distance algorithm to optimize the similarity matching structure. Experimental results show that the algorithm has low error pruning rate in identifying moving target trajectory, improves the detection efficiency of moving target trajectory recognition data, and ensures the quality of moving target trajectory recognition data mining

DAB Signal Preprocessing for Passive Coherent Location

Digital Audio Broadcast (DAB) transmitters can be successfully used as illumination sources in Passive Coherent Location (PCL). However, extending the integration time in such a configuration leads to the occurrence of periodical artifacts in the bistatic range/Doppler plots, resulting from the time structure of the DAB signal. In this paper, we propose some methods of signal preprocessing (based on symbol removal, substitution by noise, and duplication) that operate on the DAB transmission frame level and improve the received signal’s correlation properties. We also demonstrate that two of these methods allow us to avoid the mentioned artifacts and thus to improve the quality of the range/Doppler plots with detection results. We evaluate the performance of the proposed methods using real DAB signals acquired in an experimental PCL platform. We also provide the analysis of the Signal to Noise Ratio (SNR) during the detection of a moving target which shows that the proposed solution, based on symbol duplication, can offer around 3 dB of gain in SNR. Finally, we carry out the computational complexity analysis showing that the proposed method can be implemented with a minimal cost after some optimizations.

An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR

This paper studies the imaging of a ground moving target with airborne circular stripmap synthetic aperture radar (CSSAR). First, the range equation of a target moving with accelerations is developed. Then, a new range model of high accuracy is proposed, since the commonly used second-order Taylor-approximated range model is inaccurate when the azimuth resolution is relatively high or the target moves with accelerations. The proposed range model also makes it easy to derive an accurate analytical expression for the target’s 2-D spectrum. Third, based on the proposed range model, the target’s 2-D spectrum is derived and an efficient imaging method is proposed. The proposed imaging method implements focusing via a phase multiplication in the 2-D frequency domain and utilizes the genetic algorithm to accomplish an efficient search of the parameters of the proposed range model. Finally, numerical experiments are conducted to validate the proposed range model and the proposed imaging method.