Abstract

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

Abstract

— Multi-domain vehicle to grid (V2G) is a network environment in which numerous service providers offer charging and discharging services to EV users. This can enhance energy management and traffic flow for efficient intelligent transportation systems (ITS). However, the combination of multiple domains can suffer from various security vulnerabilities, highlighting the need for robust countermeasures. Moreover, existing multidomain V2G protocols utilized a central trusted authority (TA) which can create a single point of failure (SPOF), or required high computational resources. In this paper, we propose a multi-domain authentication protocol for secure and efficient V2G services using consortium blockchain. The proposed protocol provides lightweight intra-domain authentication using hash functions and XOR operators. Furthermore, the proposed protocol ensures secure cross-domain authentication by integrating elliptic curve cryptography (ECC) and physical unclonable function (PUF). Therefore, the proposed protocol can establish trust, enable efficient communications, and prevent congestion at charging stations. To validate security robustness, comprehensive evaluations are conducted using “Real-Or-Random (ROR) model”, “Scyther tool”, and informal analyses. Comparative computational overheads of the proposed and related protocols are measured using “Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL)” testbed experiments. Additionally, a simulation of the practical deployment is conducted using “Network Simulator-3 (NS-3)”. Results indicate that the proposed protocol can improve ITS by providing secure and efficient services for multi-domain V2G environments

Abstract

Unmanned aerial vehicles (UAVs) integrated with the internet of things (IoT) guarantee useful advantages such as facilitating ground communications in regions where the availability of connectivity is restricted owing to physical obstacles. However, the data transmitted by sensors and IoT embedded in UAVs are facing new security issues and privacy challenges with the known security attacks over time. To address these security attacks and threats and meet lightweight UAV communication requirements, a secure and lightweight authentication and key agreement (AKA) scheme is essential. Recently, researchers have designed a lightweight blockchain-enabled AKA scheme with privacy-preserving for UAVs to provide useful and reliable services. However, we prove that the existing scheme is fragile to various security attacks and does not ensure mutual authentication. Thus, we propose a robust and lightweight blockchain-based AKA scheme for PUF-enabled UAVs, called RLBA-UAV to enhance the security problems of the existing scheme. We demonstrate the security of RLBA-UAV by using informal/formal security analyses such as the ROR oracle model and AVISPA simulation. Moreover, we demonstrate the performance comparison analysis between RLBA-UAV and related schemes for UAVs. We demonstrate an implementation of a network simulator (NS) 3 compliant with IEEE 802.11p standards to show its validation and feasibility that RLBA-UAV is appropriate for practical UAVs. Thus, RLBA-UAV offers enhanced security and operational efficiency compared to related schemes and can be applied to practical blockchain-based AKA systems for UAVs.

Abstract

In this work, we study the natural monotone analogues of various equivalent definitions of : a well studied class (Poizat 2008, Koiran & Perifel 2009, Malod 2011, Mahajan & Rao 2013) that is believed to be larger than . We observe that these monotone analogues are not equivalent unlike their non-monotone counterparts, and propose monotone ( ) to be defined as the monotone analogue of Poizat's definition. With this definition, turns out to be exponentially stronger than and also satisfies several desirable closure properties that the other analogues may not. Our initial goal was to understand the monotone complexity of transparent polynomials, a concept that was recently introduced by Hrubeš & Yehudayoff (2021). In that context, we show that transparent polynomials with large support are hard for the monotone analogues of all the known definitions of , except for the one due to Poizat.

Abstract

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Abstract

Reconfiguring two shortest paths in a graph means modifying one shortest path to the other by changing one vertex at a time so that all the intermediate paths are also shortest paths. This problem has several natural applications, namely: (a) repaving road networks, (b) rerouting data packets in a synchronous multiprocessing setting, (c) the shipping container stowage problem, and (d) the train marshalling problem. When modelled as graph problems, (a) is the most general case while (b), (c), (d) are restrictions to different graph classes. We show that (a) does not admit polynomial-time algorithms (assuming ), even for relaxed variants of the problem (assuming ). For (b), (c), (d), we present polynomial-time algorithms to solve the respective problems. We also generalize the problem to when at most k (for a fixed integer ) contiguous vertices on a shortest path can be changed at a time.

Abstract

Image Attribution seeks to reveal the importance of image regions in the classifier’s final decision. Of the various ways to tackle this problem, the optimization based perspective is particularly intuitive: It applies the attribution as a mask on the image and reduces the attribution task to a loss, that can be optimized using gradient descent. Previous work has considered the goal as searching for the single best mask. Under this setup, however, there is a tendency towards trivial solutions of large masks with reduced discernment of the relative importances of regions. This has typically required auxiliary loss terms to control the area of the mask, however, their strength relative to the primary loss needs to be tuned. We challenge this necessity, by re-imagining attribution as an ordering of pixels according to importance. This ordering may be interpreted as a schedule which determines which locations get seen earlier and which later, allowing us to create a trajectory of masks from completely OFF to completely ON. We optimize through this sequence of masks of “all” areas and not just a single mask as in previous methods. We explore this setting, which we dub Saliency-as-Schedule (SaS), and demonstrate its effectiveness through experiments in a variety of settings, involving multiple datasets and CNN architectures. Further, we also propose a novel attribution task, feature saliency, where we use SaS to rate the influence of image regions on the intermediate feature maps of a CNN, and not just the class logit. Our findings suggest that SaS is a promising direction for the attribution problem. Our code will be available at https://github.com/tumbleweed/SaliencyasSchedule

Abstract

Image Attribution is the task of ascribing importance to regions of the input, for the final decision of a classifier. Many methods for attribution exist, and a recent development has been to use the image at multiple scales to improve the performance of some lightweight attribution methods. These gains have been demonstrated for methods that require a single or multiple but independent forward passes through the network; however, they haven’t been explored in the context of optimization-based attribution (OA) methods. As compared to other techniques, like CAMs or axiomatic methods, OA is attractive as it lends a natural formulation of the task without the need for any heuristic rules. However, the iterative way of solving the optimization problem presents challenges to straightforward utilization of multiple scales. We investigate this scenario and develop a novel 2-step approach that first discovers promising areas across scales and locations from the input and then runs Optimization based Attribution on them. We find that while a naive incorporation of image crops is unsuccessful, this 2-stage pipeline leads to improvements in performance. We provide qualitative and quantitative evidence for this and investigate the reasons for the improvement via multiple ablation experiments.

Abstract

The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly for vulnerable road users like motorcyclists. Hence, we introduce the rider intention prediction (RIP) competition that aims to address challenges in rider safety by proactively predicting maneuvers before they occur, thereby strengthening rider safety. This capability enables the riders to react to the potential incorrect maneuvers flagged by advanced driver assistance systems (ADAS). We collect a new dataset, namely, rider action anticipation dataset (RAAD) for the competition consisting of two tasks: single-view RIP and multi-view RIP. The dataset incorporates a spectrum of traffic conditions and challenging navigational maneuvers on roads with varying lighting conditions. For the competition, we received seventy-five registrations and five team submissions for inference of which we compared the methods of the top three performing teams on both the RIP tasks: one state-space model (Mamba2) and two learning-based approaches (SVM and CNN-LSTM). The results indicate that the state-space model outperformed the other methods across the entire dataset, providing a balanced performance across maneuver classes. The SVM-based RIP method showed the second-best performance when using random sampling and SMOTE. However, the CNN-LSTM method underperformed, primarily due to class imbalance issues, particularly struggling with minority classes. This paper details the proposed RAAD dataset and provides a summary of the submissions for the RIP 2024 competition.

Abstract

The problem of determining geolocation through visual inputs, known as Visual Place Recognition (VPR), has attracted significant attention in recent years owing to its potential applications in autonomous self-driving systems. The rising interest in these applications poses unique challenges, particularly the necessity for datasets encompassing unstructured environmental conditions to facilitate the development of robust VPR methods. In this paper, we address the VPR challenges by proposing an Indian driving VPR dataset that caters to the semantic diversity of unstructured driving environments like occlusions due to dynamic environments, variations in traffic density, viewpoint variability, and variability in lighting conditions. In unstructured driving environments, GPS signals are unreliable often affecting the vehicle to accurately determine location. To address this challenge, we develop an interactive image-to-image tagging annotation tool to annotate large datasets with ground truth annotations for VPR training. Evaluation of the state-of-the-art methods on our dataset shows a significant performance drop of up to 15%, defeating a large number of standard VPR datasets. We also provide an exhaustive quantitative and qualitative experimental analysis of frontal-view, multi-view, and sequence-matching methods. We believe that our dataset will open new challenges for the VPR research community to build robust models.