Search Results (12,351)

The field of object detection has witnessed significant advancements in recent years, thanks to the remarkable progress in artificial intelligence and deep learning. These breakthroughs have significantly enhanced the accuracy and efficiency of detecting and categorizing objects in digital images. Nonetheless, contemporary object detection technologies have certain limitations, such as their inability to counter white-box attacks, insufficient denoising, suboptimal reconstruction, and gradient confusion. To overcome these hurdles, this study proposes an innovative approach that uses conditional diffusion models to perturb adversarial examples. The process begins with the application of a random chessboard mask to the adversarial example, followed by the addition of a slight noise to fill the masked area during the forward process. The adversarial image is then restored to its original form through a reverse generative process that only considers the masked pixels, not the entire image. Next, we use the complement of the initial mask as the mask for the second stage to reconstruct the image once more. This two-stage masking process allows for the complete removal of global disturbances and aids in image reconstruction. In particular, we employ a conditional diffusion model based on a class-conditional U-Net architecture, with the source image further conditioned through concatenation. Our method outperforms the recently introduced HARP method by 5% and 6.5% in mAP on the COCO2017 and PASCAL VOC datasets, respectively, under non-APT PGD attacks. Comprehensive experimental results confirm that our method can effectively restore adversarial examples, demonstrating its practical utility. Full article

For effective copyright protection of digital images, this paper proposes a zero-watermarking algorithm based on local image feature information. The feature matrix of the algorithm is derived from the keypoint set determined by the Speeded-Up Robust Features (SURF) algorithm, and it calculates both the gradient feature descriptors and the vortex-like texture feature (VTF) descriptors of the keypoint set. Unlike traditional texture feature descriptors, the vortex-like texture feature descriptors proposed in this paper contain richer information and exhibit better stability. The advantage of this algorithm lies in its ability to calculate the keypoints of the digital image and provide a stable vector description of the local features of these keypoints, thereby reducing the amount of erroneous information introduced during attacks. Analysis of experimental data shows that the algorithm has good effectiveness, distinguishability, security, and robustness. Full article

Physical side-channel attacks utilize power, electromagnetic (EM), or timing signatures from cryptographic implementations during operation to retrieve sensitive information from security-critical devices. This paper provides a comprehensive review of these potent attacks against cryptographic hardware implementations, with a particular emphasis on pre-silicon leakage assessment methodologies. We explore the intricacies of cryptographic algorithms, various side-channel attacks, and the latest mitigation techniques. Although leakage assessment techniques are widely adopted in the post-silicon phase, pre-silicon leakage assessment is an emerging field that addresses the inherent limitations of its post-silicon counterpart. We scrutinize established post-silicon techniques and provide a detailed comparative analysis of pre-silicon leakage assessment across different abstraction levels in the hardware design and verification flow. Furthermore, we categorize and discuss existing pre-silicon power and electromagnetic modeling techniques for leakage detection and mitigation that can be integrated with electronic design automation (EDA) tools to automate security assessments. Lastly, we offer insights into the future trajectory of physical side-channel leakage assessment techniques in the pre-silicon stages, highlighting the need for further research and development in this critical area of cybersecurity. Full article

Here, we report on a bifunctional alginate lyase (Vnalg7) expressed in Pichia pastoris, which can degrade natural Undaria pinnatifida into unsaturated guluronic acid di- and trisaccharide without pretreatment. The enzyme activity of Vnalg7 (3620.00 U/mL-culture) was 15.81-fold higher than that of the original alg (228.90 U/mL-culture), following engineering modification. The degradation rate reached 52.75%, and reducing sugar reached 30.30 mg/mL after combining Vnalg7 (200.00 U/mL-culture) and 14% (w/v) U. pinnatifida for 6 h. Analysis of the action mode indicated that Vnalg7 could degrade many substrates to produce a variety of unsaturated alginate oligosaccharides (AOSs), and the minimal substrate was tetrasaccharide. Site-directed mutagenesis showed that Glu²³⁸, Glu²⁴¹, Glu³¹², Arg²³⁶, His³⁰⁷, Lys⁴¹⁴, and Tyr⁴¹⁸ are essential catalytic sites, while Glu³³⁴, Glu³⁴⁴, and Asp³¹¹ play auxiliary roles. Mechanism analysis revealed the enzymatic degradation pattern of Vnalg7, which mainly recognizes and attacks the third glycosidic linkage from the reducing end of oligosaccharide substrate. Our findings provide a novel alginate lyase tool and a sustainable and commercial production strategy for value-added biomolecules using seaweeds. Full article

Pesticide use in crops is a severe problem in some countries. Each country has its legislation for use, but they differ in the degree of tolerance for these broadly toxic products. Several synthetic pesticides can cause air, soil, and water pollution, contaminating the human food chain and other living beings. In addition, some of them can accumulate in the environment for an indeterminate amount of time. The agriculture sector must guarantee healthy food with sustainable production using environmentally friendly methods. In this context, biological biopesticides from microbes and plants are a growing green solution for this segment. Several pests attack crops worldwide, including weeds, insects, nematodes, and microorganisms such as fungi, bacteria, and viruses, causing diseases and economic losses. The use of bioproducts from microorganisms, such as microbial biopesticides (MBPs) or microorganisms alone, is a practice and is growing due to the intense research in the world. Mainly, bacteria, fungi, and baculoviruses have been used as sources of biomolecules and secondary metabolites for biopesticide use. Different methods, such as direct soil application, spraying techniques with microorganisms, endotherapy, and seed treatment, are used. Adjuvants like surfactants, protective agents, and carriers improve the system in different formulations. In addition, microorganisms are a tool for the bioremediation of pesticides in the environment. This review summarizes these topics, focusing on the biopesticides of microbial origin. Full article

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML’s potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment. Full article

Hazardous chemicals are transported on rail and road networks. In the case of accidental spillage or terror attack, civilian and military first responders must approach the scene equipped with appropriate personal protective equipment. The plausible manufacturing of chemical protective polymer material, from photocatalyst anatase titanium dioxide (TiO₂) doped low-density polyethylene (LDPE), for cost-effective durable lightweight protective garments against toxic chemicals such as 2-chloroethyl ethyl sulphide (CEES) was investigated. The photocatalytic effects on the physico-chemical properties, before and after ultraviolet (UV) light exposure were evaluated. TiO₂ (0, 5, 10, 15% wt) doped LDPE films were extruded and characterized by SEM-EDX, TEM, tensile tester, DSC-TGA and permeation studies before and after exposure to UV light, respectively. Results revealed that tensile strength and thermal analysis showed an increasing shift, whilst CEES permeation times responded oppositely with a significant decrease from 127 min to 84 min due to the degradation of the polymer matrix for neat LDPE, before and after UV exposure. The TiO₂-doped films showed an increasing shift in results obtained for physical properties as the doping concentration increased, before and after UV exposure. Relating to chemical properties, the trend was the inverse of the physical properties. The 15% TiO₂-doped film showed improved permeation times only when the photocatalytic TiO₂ was activated. However, 5% TiO₂-doped film exceptionally maintained better permeation times before and after UV exposure demonstrating better resistance against CEES permeation. Full article

This study proposes a general approach to protect graphs using co-secure domination within jump graphs. In the context of graphs, a dominating set is a group of vertices that are either directly linked or connected to all other vertices within the graph. The minimum cardinality of the dominating set in a graph G is called the domination number $\gamma \left(G\right)$. A set $S\subseteq V$ of a graph G is called a co-secure dominating set, if, for all $u\in S,$ there exists a node $v\in N\left(u\right)$ and in $V\setminus S$ so that $(S\setminus \{u\left\}\right)\cup \left\{v\right\}$ dominates the graph G. ${\gamma }_{cs}\left(G\right)$, the co-secure domination number, is the cardinality of a co-secure dominating set with minimum vertices within the graph G. It is a notable protective strategy in which the nodes that are attacked or damaged in an interconnection network can be replaced with alternative nodes to ensure network security. In a jump graph $J\left(G\right)$, the vertices are the edges of G and the adjacency of the vertices of $J\left(G\right)$ are given by the condition that these edges are not adjacent in G. This paper explains how $\gamma \left(G\right)$ and ${\gamma }_{cs}\left(J\left(G\right)\right)$ are related for the jump graph of various graph classes. The study further determines the exact value for ${\gamma }_{cs}\left(J\left(G\right)\right)$ of specific standard graphs. Additionally, the study characterizes ${\gamma }_{cs}\left(J\left(G\right)\right)=2$ and a tight bond is identified for ${\gamma }_{cs}\left(J\left(G\right)\right)$, particularly for G with specific conditions. Full article

This paper investigates robust $H_{\infty }$-based control for autonomous underwater vehicle (AUV) systems under time-varying delay, model uncertainties, and cyber-attacks. Sensor and actuator cyber-attacks can cause faults in the overall AUV system. In addition, the behavior of the system can be affected by the presence of complexities, such as unknown random uncertainties that occur in system modeling. In this paper, the robustness against unpredictable random uncertainties is investigated by considering unknown but norm-bounded (UBB) random uncertainties. By constructing a proper Lyapunov–Krasovskii functional (LKF) and using linear matrix inequality (LMI) techniques, new stability criteria in the form of LMIs are derived such that the AUV system is stable. Moreover, this work is novel in addressing robust $H_{\infty }$ control, which considers time-varying delay, cyber-attacks, and randomly occurring uncertainties for AUV systems. Finally, the effectiveness of the proposed results is demonstrated through two examples and their computer simulations. Full article

For concrete structures in marine or groundwater environments, sulfate attack is a major factor contributing to the degradation of concrete performance. This paper analyzes the existing literature on the chemical reactions and physical crystallization effects of sulfate attack on cement-based materials, summarizing the degradation mechanisms of corroded concrete. Experiments have been conducted to study the performance evolution of concrete under sulfate attack, considering both external environmental factors and internal factors of the cement-based materials. External environmental factors, such as the temperature, humidity, concentration, and type of sulfate solutions, wet-dry cycles, freeze-thaw cycles, chloride coupling effects, and stray currents significantly impact sulfate attack on concrete. Internal factors, including internal sources of corrosion, the chemical composition of the cement, water-cement ratio, and the content of C-S-H gel and Ca(OH)₂, influence the density and sulfate resistance of the cement-based materials. Additionally, five typical methods for enhancing the sulfate resistance of concrete are summarized. Finally, the paper identifies current challenges in the study of corroded concrete and proposes directions for future research. Full article

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints. Full article

The Industrial Internet of Things (IIoT) deals with vast amounts of data that must be safeguarded against tampering or theft. Identifying rare attacks and addressing data imbalances pose significant challenges in the detection of IIoT cyberattacks. Innovative detection methods are important for effective cybersecurity threat mitigation. While many studies employ resampling methods to tackle these issues, they often face drawbacks such as the use of artificially generated data and increased data volume, which limit their effectiveness. In this paper, we introduce a cutting-edge deep binary neural network known as the focal causal temporal convolutional neural network to address imbalanced data when detecting rare attacks in IIoT. The model addresses imbalanced data challenges by transforming the attack detection into a binary classification task, giving priority to minority attacks through a descending order strategy in the tree-like structure. This approach substantially reduces computational complexity, surpassing existing methods in managing imbalanced data challenges in rare attack detection for IoT security. Evaluation of various datasets, including UNSW-NB15, CICIDS-2017, BoT-IoT, NBaIoT-2018, and TON-IIOT, reveals an accuracy of over 99%, demonstrating the effectiveness of FCTCNNs in detecting attacks and handling imbalanced IoT data with efficiency. Full article

The integrity of democratic processes relies on secure and reliable election systems, yet achieving this reliability is challenging. This paper introduces the Post-Quantum Secured Multiparty Computed Hierarchical Authoritative Consensus Blockchain (PQMPCHAC-Bchain), a novel e-voting system designed to overcome the limitations of current Biometric Electronic Voting Machine (EVM) systems, which suffer from trust issues due to closed-source designs, cyber vulnerabilities, and regulatory concerns. Our primary objective is to develop a robust, scalable, and secure e-voting framework that enhances transparency and trust in electoral outcomes. Key contributions include integrating hierarchical authorization and access control with a novel consensus mechanism for proper electoral governance. We implement blockchain sharding techniques to improve scalability and propose a multiparty computed token generation system to prevent fraudulent voting and secure voter privacy. Post-quantum cryptography is incorporated to safeguard against potential quantum computing threats, future-proofing the system. Additionally, we enhance authentication through a deep learning-based face verification model for biometric validation. Our performance analysis indicates that the PQMPCHAC-Bchain e-voting system offers a promising solution for secure elections. By addressing critical aspects of security, scalability, and trust, our proposed system aims to advance the field of electronic voting. This research contributes to ongoing efforts to strengthen the integrity of democratic processes through technological innovation. Full article

Transitive signatures allow any entity to obtain a valid signature of $(i,k)$ by combining signatures of $(i,j)$ and $(j,k)$. However, the traditional transitive signature scheme does not offer fine-grained control over the combiner. To address this issue, we propose a formal definition of the attribute-based designated combiner transitive signature (ABDCTS) and its security model, where only entities whose inherent attributes meet the access policy can combine signatures. By introducing the fine-grained access control structure, control over the combiner is achieved. To demonstrate the feasibility of our primitive, this paper presents the first attribute-based designated combiner transitive signature scheme. Under an adaptive chosen-message attack, we prove its security based on the one-more CDH problem and the co-CDH problem, and that its algorithms have robustness. Full article

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols. Full article