Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.

Molecule aging induced by electron attacking

Here we propose a new concept of"molecule aging":with some special treatment,a molecule could be"aged"by losing some unknown tiny particles or pieces from atoms in the molecule,Such"aging"or loss of unknown tiny particles does not change apparently its molecular structure or chemical composition,but some physicochemical properties could be changed irreversibly.We further confirm such"molecule aging"via a long-term electron attacking to age water(H_(2)O)molecules.The IR spectra show no structural difference between the fresh water and the aged one,while the NMR spectra show that the electron attacking can decrease the size of water clusters.Such facts indicate that the electron attacking indeed can"affect"the structure of water molecule slightly but without damaging to its basic molecule frame.Further exploration reveals that the hydrogen evolution reaction(HER)activity of the aged water molecule is lower than the fresh water on the same Pt/C electrocatalyst.The density functional theory calculations indicate that the shortened O-H bond in H_(2)O indeed can present lower HER activity,so the observed size decrease of water clusters from NMR probably could be attributed to the shortening of O-H bond in water molecules.Such results indicate significantly that the molecule aging can produce materials with new functions for new possible applications.

Fast-solving method for air-to-surface guided bombs' allowable attack area

A new method called local accurate search is put forward to calculate the allowable area for air-to-surface missiles based on the conventional methods. Comparing with traditional methods, the local accurate search method can search the area online and reduce the time of search with the required precision. Combining the traditional flight range table with a model calculation method, the new method employs the local search to fred an accurate result, which will meet the fast-calculation requirements for attacking moving targets. In this way, the missiles are adapted for the complex warfare situation.

Diabetic foot attack:Managing severe sepsis in the diabetic patient

Diabetic foot attack(DFA)is the most severe presentation of diabetic foot disease,with the patient commonly displaying severe sepsis,which can be limb or life threatening.DFA can be classified into two main categories:Typical and atypical.A typical DFA is secondary to a severe infection in the foot,often initiated by minor breaches in skin integrity that allow pathogens to enter and proliferate.This form often progresses rapidly due to the underlying diabetic pathophysiology of neuropathy,microvascular disease,and hyperglycemia,which facilitate infection spread and tissue necrosis.This form of DFA can present as one of a number of severe infective pathologies including pyomyositis,necrotizing fasciitis,and myonecrosis,all of which can lead to systemic sepsis and multiorgan failure.An atypical DFA,however,is not primarily infection-driven.It can occur secondary to either ischemia or Charcot arthropathy.Management of the typical DFA involves prompt diagnosis,aggressive infection control,and a multidisciplinary approach.Treatment can be guided by the current International Working Group on the Diabetic Foot/Infectious Diseases Society of America guidelines on diabetic foot infections,and the combined British Orthopaedic Foot and Ankle Society-Vascular Society guidelines.This article highlights the importance of early recognition,comprehensive management strategies,and the need for further research to establish standardized protocols and improve clinical outcomes for patients with DFA.

Variable structure guidance law for attacking surface maneuver targets

The characteristics of surface maneuver targets are analyzed and a 3-D relative motion model for missiles and targets is established. A variable structure guidance law is designed considering the characteristics of targets. In the guidance law, the distance between missiles and targets as well as the missile-target relative velocity are all substituted by estimation values. The estimation errors, the target＇s velocity, and the maneuver acceleration are all treated as bounded disturbance. The guidance law proposed can be implemented conveniently in engineering with little target information. The performance of the guidance system is analyzed theoretically and the numerical simulation result shows the effectiveness of the guidance law.

Effects of Rebixiao Granules (热痹消颗粒剂) on Blood Uric Acid in Patients with Repeatedly Attacking Acute Gouty Arthritis

Objective: To observe the clinical effect of Rebixiao granule (热痹消颗粒剂, RBXG) in treating repeatedly attacking acute gouty arthritis and through experimental study on blood uric acid to explore RBXG's therapeutic mechanism. Methods: Ninety repeatedly attacking acute gouty arthritis patients were divided into the treated group ( n =60) and control group ( n =30). The treated group was treated with RBXG, and the control group was treated with Futalin tablets (diclofenac sodium). The baseline treatment including good rest, low purine diet, sufficient water drinking and urine alkalization, etc. was then given to both groups. Hypoxanthine 600 mg/kg and niacin 100 mg/kg was applied to hyperuricemic mice by gastrogavage to establish the animal models. Results: The clinical effective rate of the treated group was 95.0% and that of the control 90.0%. Good therapeutic effects were won, insignificant difference ( P >0.05)was shown between the two groups. However, the cure rate of the treated group was 26.7% while that of the control group was 10.0%, with significant difference ( P <0.01) shown between them. The treated group had its blood uric acid lowered, which was significantly different ( P <0.05) from that of the control group. The animal experiment indicated that all the three groups treated with different dosages of RBXG, as well as the Ash bark and Smilax glabra rhizome groups had their blood uric acid content reduced in the hyperuricemic mice. Conclusion: RBXG has a quicker initiation and better treatment effects than sole anti-inflammatory and analgesic agents on the treatment of repeatedly attacking acute gouty arthritis, showing no obvious toxic or adverse reactions and therefore good for long-term administration and likely to be a safe TCM preparation to control the symptoms and reduce the onsets of repeatedly attacking of acute gouty arthritis. The animal experiment shows that both the compound preparation and part of the single ingredients in the recipe have the function of reducing blood uric acid. However, the compound recipe has better therapeutic effects, proving to be superior to single drugs.

A Fast Two-Stage Black-Box Deep Learning Network Attacking Method Based on Cross-Correlation

Deep learning networks are widely used in various systems that require classification.However,deep learning networks are vulnerable to adversarial attacks.The study on adversarial attacks plays an important role in defense.Black-box attacks require less knowledge about target models than white-box attacks do,which means black-box attacks are easier to launch and more valuable.However,the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images.This paper proposes a kind of fast black-box attack based on the cross-correlation(FBACC)method.The attack is carried out in two stages.In the first stage,an adversarial image,which would be missclassified as the target label,is generated by using gradient descending learning.By far the image may look a lot different than the original one.Then,in the second stage,visual quality keeps getting improved on the condition that the label keeps being missclassified.By using the cross-correlation method,the error of the smooth region is ignored,and the number of iterations is reduced.Compared with the proposed black-box adversarial attack methods,FBACC achieves a better fooling rate and fewer iterations.When attacking LeNet5 and AlexNet respectively,the fooling rates are 100%and 89.56%.When attacking them at the same time,the fooling rate is 69.78%.FBACC method also provides a new adversarial attack method for the study of defense against adversarial attacks.

Attacking a high-dimensional quantum key distribution system with wavelength-dependent beam splitter

The unconditional security of quantum key distribution(QKD) can be guaranteed by the nature of quantum physics.Compared with the traditional two-dimensional BB84 QKD protocol, high-dimensional quantum key distribution(HDQKD) can be applied to generate much more secret key.Nonetheless, practical imperfections in realistic systems can be exploited by the third party to eavesdrop the secret key.The practical beam splitter has a correlation with wavelength,where different wavelengths have different coupling ratios.Using this property, we propose a wavelength-dependent attack towards time-bin high-dimensional QKD system.What is more, we demonstrate that this attacking protocol can be applied to arbitrary d-dimensional QKD system, and higher-dimensional QKD system is more vulnerable to this attacking strategy.

XMAM:X-raying models with a matrix to reveal backdoor attacks for federated learning

Federated Learning(FL),a burgeoning technology,has received increasing attention due to its privacy protection capability.However,the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks.Former researchers proposed several robust aggregation methods.Unfortunately,due to the hidden characteristic of backdoor attacks,many of these aggregation methods are unable to defend against backdoor attacks.What's more,the attackers recently have proposed some hiding methods that further improve backdoor attacks'stealthiness,making all the existing robust aggregation methods fail.To tackle the threat of backdoor attacks,we propose a new aggregation method,X-raying Models with A Matrix(XMAM),to reveal the malicious local model updates submitted by the backdoor attackers.Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates,unlike the existing aggregation algorithms,we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior.Specifically,like medical X-ray examinations,we investigate the collected local model updates by using a matrix as an input to get their Softmax layer's outputs.Then,we preclude updates whose outputs are abnormal by clustering.Without any training dataset in the server,the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones.For instance,when other methods fail to defend against the backdoor attacks at no more than 20%malicious clients,our method can tolerate 45%malicious clients in the black-box mode and about 30%in Projected Gradient Descent(PGD)mode.Besides,under adaptive attacks,the results demonstrate that XMAM can still complete the global model training task even when there are 40%malicious clients.Finally,we analyze our method's screening complexity and compare the real screening time with other methods.The results show that XMAM is about 10–10000 times faster than the existing methods.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Effect of Polymer-based Grinding Aid on Sulfate Attacking Resistance of Concrete

Influences of polymer-based grinding aid（PGA） on the damage process of concrete exposed to sulfate attack under dry-wet cycles were investigated. The mass loss, dynamic modulus of elasticity（Erd）, and S and Ca element contents of concrete specimens were measured. Scanning electron microscopy（SEM）, mercury intrusion porosimetry（MIP）, and X-ray diffractometry（XRD） were used to investigate the changing of microstructure of interior concrete. The results indicated that PGA was capable of reducing the mass loss and improving the sulfate attack resistance of concrete. X-ray fluorescence（XRF） analysis revealed that PGA delayed the transport process of sulfate ions and Ca ions. In addition, MIP analysis disclosed that the micropores of concrete with PGA increased in the fraction of 20-100 nm and decreased in the residues of 200 nm. Compared with the blank sample, concrete with PGA had more slender and well-organized hydration products, and no changes in hydration products ratio or type were observed.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach

Existing web-based security applications have failed in many situations due to the great intelligence of attackers.Among web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while modifying an organization's or user's information.To avoid these security challenges,this article proposes a novel,all-encompassing combination of machine learning(NB,SVM,k-NN)and deep learning(RNN,CNN,LSTM)frameworks for detecting and defending against XSS attacks with high accuracy and efficiency.Based on the representation,a novel idea for merging stacking ensemble with web applications,termed“hybrid stacking”,is proposed.In order to implement the aforementioned methods,four distinct datasets,each of which contains both safe and unsafe content,are considered.The hybrid detection method can adaptively identify the attacks from the URL,and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy,accelerate the training process,and effectively remove the unsafe JScript/JavaScript keywords from the URL.The simulation results show that the proposed hybrid model is more efficient than the existing detection methods.It produces more than 99.5%accurate XSS attack classification results(accuracy,precision,recall,f1_score,and Receiver Operating Characteristic(ROC))and is highly resistant to XSS attacks.In order to ensure the security of the server's information,the proposed hybrid approach is demonstrated in a real-time environment.

Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based institutions.Data indicates a persistent rise in phishing attacks.Moreover,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to identify.Hence,it is imperative to utilize sophisticated algorithms to address this issue.Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors.Machine learning(ML)approaches can identify common characteristics in most phishing assaults.In this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets.After that,we used the normalization technique on the dataset to transform the range of all the features into the same range.The findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best results.While in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction process.Also,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

EBCache:A Novel Cache-Based Mechanism for Mitigating the Spectre Attacks for RISC-V Processor

The cache-based covert channel is one of the common vulnerabilities exploited in the Spectre attacks.Current mitigation strategies focus on blocking the eviction-based channel by using a random/encrypted mapping function to translate memory address to the cache address,while the updated-based channel is still vulnerable.In addition,some mitigation strategies are also costly as it needs software and hardware modifications.In this paper,our objective is to devise low-cost,comprehensive-protection techniques for mitigating the Spectre attacks.We proposed a novel cache structure,named EBCache,which focuses on the RISC-V processor and applies the address encryption and blacklist to resist the Spectre attacks.The addresses encryption mechanism increases the difficulty of pruning a minimal eviction set.The blacklist mechanism makes the updated cache lines loaded by the malicious updates invisible.Our experiments demonstrated that the EBCache can prevent malicious modifications.The EBCache,however,reduces the processor’s performance by about 23%but involves only a low-cost modification in the hardware.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

On Zero Dynamics and Controllable Cyber-Attacks in Cyber-Physical Systems and Dynamic Coding Schemes as Their Countermeasures

In this paper, we study stealthy cyber-attacks on actuators of cyber-physical systems(CPS), namely zero dynamics and controllable attacks. In particular, under certain assumptions, we investigate and propose conditions under which one can execute zero dynamics and controllable attacks in the CPS. The above conditions are derived based on the Markov parameters of the CPS and elements of the system observability matrix. Consequently, in addition to outlining the number of required actuators to be attacked, these conditions provide one with the minimum system knowledge needed to perform zero dynamics and controllable cyber-attacks. As a countermeasure against the above stealthy cyber-attacks, we develop a dynamic coding scheme that increases the minimum number of the CPS required actuators to carry out zero dynamics and controllable cyber-attacks to its maximum possible value. It is shown that if at least one secure input channel exists, the proposed dynamic coding scheme can prevent adversaries from executing the zero dynamics and controllable attacks even if they have complete knowledge of the coding system. Finally, two illustrative numerical case studies are provided to demonstrate the effectiveness and capabilities of our derived conditions and proposed methodologies.