Multiple Attacks Research Articles

Robust coverless image steganography based on human pose estimation

Coverless image steganography has garnered significant attention in the field of information hiding due to its robust anti-stego analysis security, as it eliminates the need for modifying the carrier image. With the proliferation of social platforms, a plethora of images containing abundant human information have emerged on the Internet, rendering them an ideal natural image carrier database. In this paper, we propose a pose estimation network model and devise a coverless image steganography algorithm based on pose estimation to conceal secret information by mapping it onto human pose information. Since all selected carrier images are human-centered and statistically insensitive, and the high-level semantic information shows excellent stability against attacks, the concealment and robustness are enhanced. Experimental results on three public datasets demonstrate that the algorithm achieves an average robustness of up to 92% against multiple attacks.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Distributed Fuzzy Resilient Control for Vehicular Platooning System With Multiple Nodes Attacks in Prescribed Performance

Distributed Fuzzy Resilient Control for Vehicular Platooning System With Multiple Nodes Attacks in Prescribed Performance

Understanding the SARS-CoV-2–Human Liver Interactome Using a Comprehensive Analysis of the Individual Virus–Host Interactions

Many metabolic processes at the molecular level support both viral attack strategies and human defenses during COVID-19. This knowledge is of vital importance in the design of antiviral drugs. In this study, we extracted 18 articles (2021–2023) from PubMed reporting the discovery of hub nodes specific for the liver during COVID-19, identifying 142 hub nodes. They are highly connected proteins from which to obtain deep functional information on viral strategies when used as functional seeds. Therefore, we evaluated the functional and structural significance of each of them to endorse their reliable use as seeds. After filtering, the remaining 111 hubs were used to obtain by STRING an enriched interactome of 1111 nodes (13,494 interactions). It shows the viral strategy in the liver is to attack the entire cytoplasmic translational system, including ribosomes, to take control of protein biosynthesis. We used the SARS2-Human Proteome Interaction Database (33,791 interactions), designed by us with BioGRID data to implement a reverse engineering process that identified human proteins actively interacting with viral proteins. The results show 57% of human liver proteins are directly involved in COVID-19, a strong impairment of the ribosome and spliceosome, an antiviral defense mechanism against cellular stress of the p53 system, and, surprisingly, a viral capacity for multiple protein attacks against single human proteins that reveal underlying evolutionary–topological molecular mechanisms. Viral behavior over time suggests different molecular strategies for different organs.

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS)

Distributed intrusion detection systems (DIDS) are a specialized subset of conventional IDSs designed for implementation in distributed environments. Each IDS is integrated into distinct entities within a monitored network, potentially distributed across various locations. These participating IDSs can be configured to detect either a particular or multiple attack types. Although DIDS has found extensive application in diverse IoT systems, its utilization in unmanned aerial vehicles (UAVs) still needs to be explored. Consequently, it is imperative to devise a comprehensive framework tailored explicitly for UAVs. It combines multiple detection units to enhance security. Based on the insights gained from previous studies, we propose an exhaustive DIDS for UAVs security enforcement in this paper. Our proposed solution offers a robust and scalable security approach. Through distributing the workload across interconnected IDSs deployed on the UAV, our solution was optimized for UAVs attacks detection to achieve high detection performance while reducing the complexity. To the best of our insight, there is no recorded DIDS for UAVs security, and attack detection has been proposed and evaluated. Furthermore, our paper provides a detailed analysis, outlining the development basis and the achieved results. We performed multiple experiments over different cases using different datasets. The achieved experimental results demonstrate that the proposed IDS has significantly high accuracy detection and low loss rates. Our proposed E-DIDS efficiently detects multiple attacks on different UAVs subsets with good global accuracy that reached 98.6% and low resource consumption.

A multi-step attack identification and correlation method based on multi-information fusion

With the continuous development of Internet technology, network security issues are becoming more and more complex. In order to obtain or damage user information, attackers usually need to use multiple attack steps to attack different network nodes to achieve the final goal. Currently, traditional intrusion detection mainly focuses on classifying attacks on a single piece of traffic information, and lacks the use of correlation and timing information between multi-step attack traffic to carry out multi-step attack identification and correlation research. To this end, we introduce an edge-based graph attention network to aggregate the neighbor information of flows and use a long short-term memory neural network to obtain time series information. By considering neighbor traffic and time information during the classification process, we are able to achieve accurate classification in multi-step attack scenarios. Then, based on the classification results, the sum of the number of attacks and the number of attacks is counted for each host node. Then standardize the statistical counts of internal network host nodes and external network host nodes and set thresholds to exclude low-risk nodes. Finally, a time-based depth-first traversal algorithm is used to obtain the key attack chain. However, this method may face some challenges, such as high computational complexity, the ability to handle large-scale data, and the generalization ability of the model. Experimental results show that the method we proposed is significantly better than the traditional method in terms of accuracy and recall rate for multi-step attacks, and it can effectively correlate attack steps and obtain multiple key attack chains.

Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy

SummaryWireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

Output Formation Containment for Multiagent Systems Under Multipoint Multipattern FDI Attacks: A Resilient Impulsive Compensation Control Approach.

The increasing number of devices and frequent interactions of agents from networked multiagent systems (MASs) exacerbate the risks of potential cyber attacks, especially the different point attacks and multiple pattern attacks. This article considers the output formation-containment problem for MASs under multipoint multipattern false data injection (FDI) attacks. The multipoint describes the attacks simultaneously occurring on the sensors, actuators, and communication channels; the multipattern captures that sensor and actuator attack signals are both continuous deterministic variables, and the communication channel attack signals are intermittent random variables, obeying the Bernoulli distribution. For such compromised MASs, a novel hybrid protocol is proposed, which integrates a state observer, an attack estimator, an impulsive interactor and a compensation controller. Thereinto, the state observer and the attack estimator are constructed to recover the unmeasured system states and the unknown FDI attack signals, respectively; the impulsive interactor is designed to guarantee that the neighbor's signals are transmitted only at impulsive instants, and meanwhile the channel attacks are randomly launched; using the recovered signals, the compensation controller is devised to alleviate the effect of attacks. A sufficient condition is identified, under which the output formation containment is achieved with cooperative uniform ultimate boundedness (UUB). Finally, simulation results are carried out to validate the effectiveness and advantages of the proposed approach.

Laparoscopic Colecystectomy in a Patient with Situs Inversus Totalis

Situs inversus totalis (SIT) is a rare clinical condition with autosomal recessive inheritance in which visceral organs are replaced as a mirror image. In this paper, we present the case of a 44-year-old female with SIT who underwent laparoscopic cholecystectomy (LC) due to multiple attacks of cholecystitis and gallbladder stones. Since the patient had SIT, the operation was performed by modifying the placement of the operating team and the operating table. No adverse events were encountered during the operation. The patient was discharged the next day without any complications in the postoperative clinic follow-ups. Due to organ displacement, LC in individuals with SIT is susceptible to complications. These operations should be performed with the utmost care by surgeons with sufficient experience.

JUST-IN-TIME VULNERABILITY DETECTION AND LOCALIZATION

Software vulnerabilities have increased dramatically, and multiple severe attacks have occurred in recent years. This poses a critical challenge for early detection and prevention of vulnerabilities in Software Quality Assurance. This paper introduces a novel framework, JULY, which serves the dual purpose of detecting vulnerable commits and localizing the root causes of the vulnerabilities. The fundamental concept of JULY is that the determinant of the vulnerability of a commit is the inherent meaning embedded in its changed code. For just-in-time vulnerability detection (JIT-VD ), JULY represents each commit by a Code Transformation Graph and employs a Graph Neural Network model to capture their meanings and distinguish between vulnerable and non-vulnerable commits. Once a commit is detected as vulnerable, it is passed to the just-in-time vulnerability localization (JIT-VL ) model to localize the root causes, which are vulnerable changed statements. In JIT-VL , JULY encodes each statement by the following features: operation, context, and topic. Then, JULY measures the suspiciousness score of each changed statement and ranks them based on their scores. To evaluate the effectiveness of JULY, we conducted several experiments using a dataset consisting of 20,274 commits in 506 C/C++ projects. JULY achieves a remarkable improvement of 95% in Top-1 ACC and 63% in MRR compared to the state-of-the-art approaches. Furthermore, when examining the same portion (i.e., 20%) of modified statements in each commit, JULY can find twice as many vulnerable statements within a given commit as the state-of-the-art approaches.

Data Transmission Security on Cloud Computing using Algorithm

Cloud computing enables businesses to efficiently store vast amounts of data in the cloud, which can be accessed seamlessly over the Internet without hardware compatibility constraints. However, the transmission of data over the Internet is susceptible to various security threats such as man-in-the-middle attacks, known plaintext attacks, chosen ciphertext attacks, related-key attacks, and pollution attacks. Uploading data to a single cloud service may heighten the risk of compromising confidential information. Prior research has explored several cryptography techniques like SA-EDS, RFDA, and EST to secure data storage across multiple cloud platforms. However, these existing methods exhibit vulnerabilities to various attacks. This article focuses on addressing data security challenges in multi-cloud environments and proposes an advanced approach called Proficient Security over Distributed Storage. PSDS has undergone rigorous testing against multiple attacks, demonstrating its resilience against related-key attacks, pollution attacks, chosen ciphertext attacks, and known plaintext attacks. Additionally, PSDS exhibits reduced computational time compared to existing encryption methods such as STTN and RFD.

Rethinking Mesh Watermark: Towards Highly Robust and Adaptable Deep 3D Mesh Watermarking

The goal of 3D mesh watermarking is to embed the message in 3D meshes that can withstand various attacks imperceptibly and reconstruct the message accurately from watermarked meshes. The watermarking algorithm is supposed to withstand multiple attacks, and the complexity should not grow significantly with the mesh size. Unfortunately, previous methods are less robust against attacks and lack of adaptability. In this paper, we propose a robust and adaptable deep 3D mesh watermarking Deep3DMark that leverages attention-based convolutions in watermarking tasks to embed binary messages in vertex distributions without texture assistance. Furthermore, our Deep3DMark exploits the property that simplified meshes inherit similar relations from the original ones, where the relation is the offset vector directed from one vertex to its neighbor. By doing so, our method can be trained on simplified meshes but remains effective on large size meshes (size adaptable) and unseen categories of meshes (geometry adaptable). Extensive experiments demonstrate our method remains efficient and effective even if the mesh size is 190× increased. Under mesh attacks, Deep3DMark achieves 10%∼50% higher accuracy than traditional methods, and 2× higher SNR and 8% higher accuracy than previous DNN-based methods.

Energy-Management Strategy of Battery Energy Storage Systems in DC Microgrids: A Distributed Fuzzy Output Consensus Control Considering Multiple Cyber Attacks

Distributed renewable sources are one of the most promising contributors for DC microgrids to reduce carbon emission and fuel consumption. Although the battery energy storage system (BESS) is widely applied to compensate the power imbalance between distributed generators (DGs) and loads, the impacts of disturbances, DGs, constant power loads (CPLs) and cyber attacks on this system are not simultaneously considered. Based on this, a distributed fuzzy output consensus control strategy is proposed to realize accurate current sharing and operate normally in the presence of denial of service (DoS) attacks and false data injection (FDI) attacks. Firstly, the whole model of the BESS in DC microgrids embedded into disturbance items, DGs, CPLs and resistive loads, is firstly built. This model could be further transformed into standard linear heterogeneous multi-agent systems with disturbance, which lays the foundation for the following control strategy. Then the model of FDI and DoS attacks are built. Meanwhile, the fuzzy logic controller (FLC) is applied to reduce the burden of communication among batteries. Based on these, a distributed output consensus fuzzy control is proposed to realize accurate current sharing among batteries. Moreover, the system under the proposed control in different cases is analyzed. Finally, the feasibility of the proposed control strategy is verified by numerical simulation results and experiment results.

Poison Attack and Poison Detection on Deep Source Code Processing Models

In the software engineering (SE) community, deep learning (DL) has recently been applied to many source code processing tasks, achieving state-of-the-art results. Due to the poor interpretability of DL models, their security vulnerabilities require scrutiny. Recently, researchers have identified an emergent security threat to DL models, namely, poison attacks . The attackers aim to inject insidious backdoors into DL models by poisoning the training data with poison samples. The backdoors mean that poisoned models work normally with clean inputs but produce targeted erroneous results with inputs embedded with specific triggers. By using triggers to activate backdoors, attackers can manipulate poisoned models in security-related scenarios (e.g., defect detection) and lead to severe consequences. To verify the vulnerability of deep source code processing models to poison attacks, we present a poison attack approach for source code named CodePoisoner as a strong imaginary enemy. CodePoisoner can produce compilable and functionality-preserving poison samples and effectively attack deep source code processing models by poisoning the training data with poison samples. To defend against poison attacks, we further propose an effective poison detection approach named CodeDetector . CodeDetector can automatically identify poison samples in the training data. We apply CodePoisoner and CodeDetector to six deep source code processing models, including defect detection, clone detection, and code repair models. The results show that ❶ CodePoisoner conducts successful poison attacks with a high attack success rate (average: 98.3%, maximum: 100%). It validates that existing deep source code processing models have a strong vulnerability to poison attacks. ❷ CodeDetector effectively defends against multiple poison attack approaches by detecting (maximum: 100%) poison samples in the training data. We hope this work can help SE researchers and practitioners notice poison attacks and inspire the design of more advanced defense techniques.

Improving adversarial robustness using knowledge distillation guided by attention information bottleneck

Deep neural networks (DNNs) have recently been found to be vulnerable to adversarial examples, which raises concerns about their reliability and poses potential security threats. Adversarial training has been extensively studied to counter adversarial attacks. However, the limited attack types incorporated during the training phase will restrict the defense performance of models against unknown attacks and impact their standard accuracies. Furthermore, we discover that adversarial training models tend to overfit redundant noisy features, which hinders their generalization. To alleviate these issues, this paper proposes the attention information bottleneck-guided knowledge distillation (AIB-KD) method to enhance models' adversarial robustness. We integrate adversarial training with attention information bottleneck as the defense framework to achieve an optimal trade-off between information compression and classification performance. Simultaneously, we specifically employ knowledge distillation to guide the adversarial training models in learning both the standard attention information and valuable deep feature distributions to enhance their defense generalization capability. Experimental results demonstrate that AIB-KD can effectively classify adversarial examples in multiple attack settings. The average white-box and black-box classification accuracies for the WideResNet-28-10 model on the CIFAR-10 dataset are 56.59% and 85.49%, respectively, and the average accuracies on the SVHN dataset are 61.71% and 88.96%. When applied to unknown attack scenarios, AIB-KD is more effective and interpretable than state-of-the-art methods.

Does fungal infection increase the palatability of oilseed rape to insects?

Multiple and simultaneous attacks by pathogens and insect pests frequently occur in nature. Plants respond to biotic stresses by activating distinct defense mechanisms, but little is known about how plants cope with multiple stresses. The focus of this study was the combined interaction of fungal infection caused by Leptosphaeria maculans (synonym Plenodomus lingam) and arthropod infestation by the diamondback moth (Plutella xylostella) in oilseed rape (Brassica napus). We hypothesized that infection by the fungal pathogen L. maculans could alter oilseed rape palatability to P. xylostella-chewing caterpillars. Feeding preference tests were complemented with analyses of defense gene transcription, and levels of glucosinolates (GLSs) and volatile organic compounds (VOCs) in L. maculans-inoculated and non-inoculated (control) leaves to determine possible causes of larval choice. Caterpillars preferred true leaves to cotyledons, hence true leaves were used for further experiments. True leaves inoculated with L. maculans were more palatable to caterpillars over control leaves during the early stage of infection at 3 days post inoculation (dpi), but this preference disappeared in the later stages of infection at 7 dpi. In parallel, genes involved in the salicylic acid and ethylene pathways were up-regulated in L. maculans-inoculated leaves at 3 and 7 dpi; L. maculans increased the level of total aliphatic GLSs, specifically glucobrassicanapin, and decreased the level of glucoiberin at 3 dpi and altered the content of specific VOCs. A group of 55 VOCs with the highest variability between treatments was identified. We suggest that the P. xylostella preference for L. maculans-inoculated leaves in the early stage of disease development could be caused by the underlying mechanisms leading to changes in metabolic composition. Further research should pinpoint the compounds responsible for driving larval preference and evaluate whether the behavior of the adult moths, i.e. the stage that makes the first choice regarding host plant selection in field conditions, correlates with our results on larval host acceptance. © 2024 Society of Chemical Industry.

Clinical characteristics of adult cases with urolithiasis from Türkiye: A regional epidemiological study.

To evaluate the metabolic and clinical characteristics of adult cases with stone disease from a regional part of Türkiye. The study included 2348 adult patients with sonography and/or computed tomography-proven urinary stones. All cases were given a questionnaire about the epidemiological features of urolithiasis. Aside from the type and severity of stoneforming risk factors, both patient (age, gender, BMI, associated comorbidities, first onset of stone disease, positive family history, educational level) and stone-related (size, number, location, chemical composition, previous stone attacks) factors have been thoroughly assessed. The data were evaluated in multiple aspects to outline the epidemiological features. The overall mean age value of the cases was 43.3 years, and the M/F ratio was 1.34. The first onset of the disease was found to vary between 15-57 years, with a mean value of 32.4 years. While most of the stones were located in kidney and ureter, calcium-containing stones constituted the most common type (CaOx 69%, CaOxPO4 7%). More than 42% of the cases suffered from multiple stone attacks; positive family history waspresent in 31.6%. Among the associated comorbidities, hypertension was the most common pathology (45.8%), and the BMI index value was > 30 in 31.3% of the cases. 57.7% of the patients had just one stone attack, and 42.2% had recurrent stone formation. Our findings clearly show that important implications may be extracted from epidemiologic data acquired from local scale research to implement an effective preventative program and closely monitor the patients.

Output feedback pinning control for complex dynamical networks subjected to multiple attacks

This paper is concentrated on the secure synchronization control issue for complex dynamical networks (CDNs) subjected to multiple attacks. For the sake of conserving resources and being closer to reality, a new output feedback controller based on pinning nodes is firstly designed. Secondly, in contrast with the existent researches on secure network circumstances, we establish a novel multiple attacks model considering the influence of random deception attacks and aperiodic Denial-of-Service (DoS) attacks. Based on the piecewise Lyapunov-Krasovskii function and decoupling method, a new secure synchronization criterion is proposed to realize the mixed H∞ and passive performance of CDNs against multiple attacks, and the control gain is gained. Lastly, the availability of the raised consequence is testified by two concrete examples.