Cooperative Coevolution (CC) effectively addresses Large-Scale Global Optimization (LSGO) via decomposition but struggles with the emerging class of Heterogeneous LSGO (H-LSGO) problems arising from real-world applications, where subproblems exhibit diverse dimensions and distinct landscapes. The prevailing CC paradigm, relying on a fixed low-dimensional optimizer, often fails to navigate this heterogeneity. To address this limitation, we propose the Learning-Based Heterogeneous Cooperative Coevolution Framework (LH-CC). By formulating the optimization process as a Markov Decision Process, LH-CC employs a meta-agent to adaptively select the most suitable optimizer for each subproblem. We also introduce a flexible benchmark suite to generate diverse H-LSGO problem instances. Extensive experiments on 3000-dimensional problems with complex coupling relationships demonstrate that LH-CC achieves superior solution quality and computational efficiency compared to state-of-the-art baselines. Furthermore, the framework exhibits robust generalization across varying problem instances, optimization horizons, and optimizers. Our findings reveal that dynamic optimizer selection is a pivotal strategy for solving complex H-LSGO problems.

Embedded systems are evolving in complexity, leading to the emergence of multiple threats. The co-design and execution of software on the embedded systems further exacerbate the attack surface, making them more vulnerable to sophisticated attacks. As embedded systems are used in critical areas, ensuring their security is crucial. In this special session paper, primarily four major topics regarding embedded systems’ security are discussed. Firstly, this paper initially explores timing channel analysis at a microarchitectural level in heterogeneous hardware to address the security challenges. It then delves into exploring software-based fuzzing techniques to detect vulnerabilities and enhance embedded system security. Additionally, the paper discusses strategies for improving security in IoT devices with a layered defense strategy known as Snowflake IoT. Finally, it examines approaches to securing large and complex monolithic systems. The challenges and opportunities for securing the embedded systems according to the scale and type of attacks.

Despite their success in LIDAR object detection, modern detectors are vulnerable to uncommon instances and corner cases (e.g., a runaway tire) since they are closed-set and static. Networks under the closed-set setup only predict labels of seen classes, while static models suffer from catastrophic forgetting when gradually learning novel concepts. This motivates us to formulate the open-world 3D object detection task for autonomous driving, which aims to 1) tackle the closed-set issue by identifying unseen instances as unknown and 2) incrementally learn novel classes without forgetting previously obtained knowledge. To achieve the open-world objectives, we propose Open-World 3D Detector (OW3Det), the first framework for open-world 3D object detection. The OW3Det comprises a base detector, a self-supervised unknown identifier, and a knowledge-distillation-restricted incremental learner. Although knowledge distillation facilitates preserving memories, imposing penalties on areas containing unknown objects hinders the incremental learning process. We mitigate this hindrance by employing unknown-driven pivotal mask, which eliminates unnecessary restrictions on regions overlapping with novel instances. Abundant experiments and visualizations demonstrate that the proposed OW3Det attains state-of-the-art performance.

We propose a novel on-line multi-UAV coverage path planning method for 3D reconstruction. UAVs play homogeneous roles in most existing multi-UAV path planning methods, where their flexibility is not fully exploited. The fact that UAVs can explore with different missions and strategies at different heights simultaneously is not well considered. Therefore, we give UAVs different identities by presenting a global-local pattern, where global UAV locates the interesting regions while local UAV explores these regions in succession. Leveraging the mechanism of sensorimotor coordination, our strategy adjusts the paths of local UAV on-line according to the input from global UAV in order to gather more information and save time. In addition, our method is compatible with real-time SLAM systems. Simulation shows that our strategy takes less time than other strategies based on area-division, and indicates that it gathers more information for better reconstruction.

A trustworthy 3D detector is essential in the perception system of autonomous vehicles, ensuring accurate detection of their surroundings. However, autonomous vehicles have to operate in ever-changing real-world driving scenes, where unknown objects that do not belong to the training set are commonly encountered. Confusion about known and unknown objects could result in severe and dangerous consequences for road safety. To address this problem, we improve the reliability of autonomous driving systems by formulating open-set 3D object detection task. An Open-set 3D Detector (Open3Det) is proposed to reject unknown instances while maintaining performance on known categories. Distinct from 2D objects, clear space separation exists between each 3D instance. Motivated by this, we propose selective forgetting, a novel method capable of filtering out misleading predictions. Given a close-set teacher model, knowledge distillation is introduced to build a open-set student model. The student model preserves its predictions for known objects, whereas predictions of backgrounds and unknown instances are discarded to minimize misleading results. Extensive experiments and visualizations reveal the efficacy of the proposed method.

A trustworthy 3D detector is essential in the perception system of autonomous vehicles, ensuring accurate detection of their surroundings. However, autonomous vehicles have to operate in ever-changing real-world driving scenes, where unknown objects that do not belong to the training set are commonly encountered. Confusion about known and unknown objects could result in severe and dangerous consequences for road safety. To address this problem, we improve the reliability of autonomous driving systems by formulating open-set 3D object detection task. An Open-set 3D Detector (Open3Det) is proposed to reject unknown instances while maintaining performance on known categories. Distinct from 2D objects, clear space separation exists between each 3D instance. Motivated by this, we propose selective forgetting, a novel method capable of filtering out misleading predictions. Given a close-set teacher model, knowledge distillation is introduced to build a open-set student model. The student model preserves its predictions for known objects, whereas predictions of backgrounds and unknown instances are discarded to minimize misleading results. Extensive experiments and visualizations reveal the efficacy of the proposed method.

Side channels within the processor mi-croarchitecture are notorious for their ability to leak information without leaving any physical traces for forensic examination. Most prior detection frame-works typically choose to continuously sample a select subset of hardware events without attempting to understand the mechanics behind the side channel activity. In this work, we propose SC-K9, a novel framework that synchronizes its sampling frequency with that of the adversary, thereby improving the detection accuracy even when the frequency of attack operations vary with specific implementations. We then deploy a hardware-based deception strategy to trick the adversary and annul its observations from the side channel activities. We illustrate our design and demonstrate its effectiveness in identifying some of the potent side channels exposed by recent speculative execution attacks. Our experimental results show that SC-K9 can effectively spot adversaries at different operational modes, and incurs very low rate of false alarms among the benign workloads.

Abstract The recently proposed intelligent reflecting surface (IRS) is considered as a promising technology to combat the propagation distance problem in the future communications. However, due to the lack of quantitative analysis and IRS selection scheme from the perspective of symbol error rate (SER) performance, the performance analysis method of asymptotically tight approximation SER is proposed. Firstly, a communication system model aided by an IRS is established. Then, through the reasonable reconstruction between the reflection coefficient and the link coefficient of IRS, the SNR on the forwarding link of IRS satisfies the harmonic mean form. Finally, based on the calculation method of moment generating function (MGF), the asymptotically tight approximation SER formula of the system with simple structure is derived. The simulation results show that, the asymptotically tight approximation SER formula can accurately describe the system SER performance under the low SNR condition. Before the number of IRS reflection unit increases to a certain value, the system SER performance can be effectively improved. Compared with the AF multi‐relay system, IRS aided system has the slightly better SER performance only in the case of low SNR. Our work will provide the important theoretical basis for the IRS selection in practical communications.

Modern computing systems typically have multiple users sharing hardware resources. While such shared hardware have typically been performance boosters, they have also led to inadvertent side-effects such as side channels. Caches, that present the largest attack surface, have been popular among adversaries for side channel attacks. In this work, we repurpose a classic cache performance metric namely, reuse distance, to capture the activity of an adversary in cache timing channels. We design Reuse-trap, an efficient cache side channel mitigation framework to record reuse distances during victim accesses and carefully inject noise to mislead the spy from inferring the victim’s activity. Our experimental results show that we can identify adversaries with zero false positives and make timing channels suffer from over 50% bit error rate on average.

Graphics Processing Units (GPU) play a major role in speeding up computational tasks of the users, especially in applications such as high volume text and image processing. Recent works have demonstrated the security problems associated with GPU that do not erase the remnant data left behind by previous applications prior to OS context switching. In these attacks, adversaries are able to allocate their memory region on the same memory region used by previous applications and are able to steal their secrets. To overcome this problem, one needs to erase every modified memory page, and this process incurs very high latencies (order of several seconds to even minutes). In this work, we propose EraseMe, a lightweight, content-aware memory-cleansing framework that identifies and erases the sensitive memory pages left behind by victim applications. Our preliminary evaluation shows that EraseMe is able to increase the difficulty of image reconstruction by over 10× for the attacker.

Physically shared micro-architecture can be exploited by adversaries to communicate covertly via timing modulation without leaving any physical traces. Among different micro-architecture units, caches provide one of the largest attack surfaces because it is frequently accessed by multiple processes and it cannot be disabled. In this work, we show that by collecting cache occupancy traces, we can distinguish adversary from benign workloads through multiple signal processing techniques. When two processes are communicating by creating conflict misses, they would take cache memory space from each other. Consequently. the cache occupancies of two involved processes would be negatively correlated. Besides, the activity of the adversary in occupying the victim's cache space would be repetitive as a result of long-term, continuous transmission of secret information in a covert manner. By filtering the non-negatively correlated part and analyzing the repetitiveness of cache occupancy trace, we can achieve zero false negative rate and 4% false positive rate in cache timing channel detection.