An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Plant Chemical Defenses against Insect Herbivores—Using theWild Tobacco as a Model

The Nicotiana genus, commonly known as tobacco, holds significant importance as a crucial economic crop. Confrontedwith an abundance of herbivorous insects that pose a substantial threat to yield, tobacco has developed adiverse and sophisticated array of mechanisms, establishing itself as a model of plant ecological defense. Thisreview provides a concise overview of the current understanding of tobacco’s defense strategies against herbivores.Direct defenses, exemplified by its well-known tactic of secreting the alkaloid nicotine, serve as a potent toxinagainst a broad spectrum of herbivorous pests. Moreover, in response to herbivore attacks, tobacco enhancesthe discharge of volatile compounds, harnessing an indirect strategy that attracts the predators of the herbivores.The delicate balance between defense and growth leads to the initiation of most defense strategies only after aherbivore attack. Among plant hormones, notably jasmonic acid (JA), play central roles in coordinating thesedefense processes. JA signaling interacts with other plant hormone signaling pathways to facilitate the extensivetranscriptional and metabolic adjustments in plants following herbivore assault. By shedding light on these ecologicaldefense strategies, this review emphasizes not only tobacco’s remarkable adaptability in its natural habitatbut also offers insights beneficial for enhancing the resilience of current crops.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

A shield of defense:Developing ballistic composite panels with effective electromagnetic interference shielding absorption

The primary goal of this study is to develop cost-effective shield materials that offer effective protection against high-velocity ballistic impact and electromagnetic interference(EMI)shielding capabilities through absorption.Six fiber-reinforced epoxy composite panels,each with a different fabric material and stacking sequence,have been fabricated using a hand-layup vacuum bagging process.Two panels made of Kevlar and glass fibers,referred to as(K-NIJ)and(G-NIJ),have been tested according to the National Institute of Justice ballistic resistance protective materials test NIJ 0108.01 Standard-Level IIIA(9 mm×19 mm FMJ 124 g)test.Three panels,namely,a hybrid of Kevlar and glass(H-S),glass with ceramic particles(C-S),and glass with recycled rubber(R-S)have been impacted by the bullet at the center,while the fourth panel made of glass fiber(G-S)has been impacted at the side.EMI shielding properties have been measured in the X-band frequency range via the reflection-transmission method.Results indicate that four panels(K-NIJ,G-NIJ,H-S,and G-S)are capable of withstanding high-velocity impact by stopping the bullet from penetrating through the panels while maintaining their structural integrity.However,under such conditions,these panels may experience localized delamination with variable severity.The EMI measurements reveal that the highest absorptivity observed is 88% for the KNIJ panel at 10.8 GHz,while all panels maintain an average absorptivity above 65%.All panels act as a lossy medium with a peak absorptivity at different frequencies,with K-NIJ and H-S panels demonstrating the highest absorptivity.In summary,the study results in the development of a novel,costeffective,multifunctional glass fiber epoxy composite that combines ballistic and electromagnetic interference shielding properties.The material has been developed using a simple manufacturing method and exhibits remarkable ballistic protection that outperforms Kevlar in terms of shielding efficiency;no bullet penetration or back face signature is observed,and it also demonstrates high EMI shielding absorption.Overall,the materials developed show great promise for various applications,including the military and defense.

Seasonal increase in nest defense,but not egg rejection,in a cuckoo host

The interactions between avian brood parasites and their hosts provide an informative and easy-to-handle system for studying coevolution.Avian brood parasitism reduces the reproductive success of hosts,and thus,hosts have evolved anti-parasitic strategies,such as rejecting parasitic eggs and adopting aggressive nest defense strategies,to avoid the cost brought on by brood parasitism.To test whether host anti-parasitic strategies are adjusted with the risk of being parasitized when the breeding seasons of brood parasites and hosts are not synchronous,we conducted a field experiment assessing nest defense and egg recognition behaviors of the Isabelline Shrike(Lanius isabellinus),a host of the Common Cuckoo(Cuculus canorus).In the local area,the host Isabelline Shrike begins to breed in April,whereas the summer migratory Common Cuckoo migrates to the local area in May and begins to lay parasitic eggs.Results showed that nest defense behaviors of the Isabelline Shrike increases significantly after cuckoo arrival,showing higher aggressiveness to cuckoo dummies,with no significant difference in attack rates among cuckoo,sparrowhawk and dove dummies,but their egg rejection did not change significantly.These results imply that Isabelline Shrikes may adjust their nest defense behavior,but not egg rejection behavior,with seasonality.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Two intestinal microbiota-derived metabolites, deoxycholic acid and butyrate, synergize to enhance host defense peptide synthesis and alleviate necrotic enteritis

Background Necrotic enteritis(NE)is a major enteric disease in poultry,yet effective mitigation strategies remain elusive.Deoxycholic acid(DCA)and butyrate,two major metabolites derived from the intestinal microbiota,have independently been shown to induce host defense peptide(HDP)synthesis.However,the potential synergy between these two compounds remains unexplored.Methods To investigate the possible synergistic effect between DCA and butyrate in regulating HDP synthesis and barrier function,we treated chicken HD11 macrophage cells and jejunal explants with DCA and sodium butyrate(NaB),either individually or in combination,for 24 h.Subsequently,we performed RNA isolation and reverse transcrip-tion-quantitative PCR to analyze HDP genes as well as the major genes associated with barrier function.To further determine the synergy between DCA and NaB in enhancing NE resistance,we conducted two independent trials with Cobb broiler chicks.In each trial,the diet was supplemented with DCA or NaB on the day-of-hatch,followed by NE induction through sequential challenges with Eimeria maxima and Clostridium perfringens on d 10 and 14,respectively.We recorded animal mortality after infection and assessed intestinal lesions on d 17.The impact of DCA and NaB on the microbiota in the ileum and cecum was evaluated through bacterial 16S rRNA gene sequencing.Results We found that the combination of DCA and NaB synergistically induced multiple HDP genes in both chicken HD11 cells and jejunal explants.Additionally,the gene for claudin-1,a major tight junction protein,also exhibited synergistic induction in response to DCA and NaB.Furthermore,dietary supplementation with a combination of 0.75 g/kg DCA and 1 g/kg NaB led to a significant improvement in animal survival and a reduction in intestinal lesions compared to either compound alone in a chicken model of NE.Notably,the cecal microbiota of NE-infected chickens showed a marked decrease in SCFA-producing bacteria such as Bacteroides,Faecalibacterium,and Cuneatibacter,with lactobacilli becoming the most dominant species.However,supplementation with DCA and NaB largely restored the intestinal microbiota to healthy levels.Conclusions DCA synergizes with NaB to induce HDP and claudin-1 expression and enhance NE resistance,with potential for further development as cost-effective antibiotic alternatives.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Effective Utilization of Government-Provided CTI by Small Businesses within the Defense Industrial Base

There are two broad objectives of the research reported in this paper. First, we assess whether government-provided cyber threat intelligence (CTI) is helpful in preventing, or responding to, cyber-attacks among small businesses within the U.S. Defense Industrial Base (DIB). Second, we identify ways of improving the effectiveness of government-provided CTI to small businesses within the DIB. Based on a questionnaire-based survey, our findings suggest that government-provided CTI helps businesses within the DIB in preventing, or responding to, cyber-attacks providing a firm is familiar with the CTI. Unfortunately, a large percentage of small firms are not familiar with the government-provided CTI feeds and consequently are not utilizing the CTI. This latter situation is largely due to financial constraints confronting small businesses that prevent firms from having the wherewithal necessary to effectively utilize the government-provided CTI. However, we found a significant positive association between a firm’s familiarity with the government-provided CTI and whether a firm is being periodically reviewed by the Defense Counterintelligence and Security Agency (DCSA) or is compliant with the Cybersecurity Maturity Model Certification (CMMC) program. The findings from our study also show that the participating firms believe that external cyber threats are more likely to be the cause of a future cybersecurity breach than internal cybersecurity threats. Finally, our study found that the portion of the IT budget that small businesses within the DIB spend on cybersecurity-related activities is dependent on the perception that a firm would be the target of an external cyber-attack.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

GUARDIAN: A Multi-Tiered Defense Architecture for Thwarting Prompt Injection Attacks on LLMs

This paper introduces a novel multi-tiered defense architecture to protect language models from adversarial prompt attacks. We construct adversarial prompts using strategies like role emulation and manipulative assistance to simulate real threats. We introduce a comprehensive, multi-tiered defense framework named GUARDIAN (Guardrails for Upholding Ethics in Language Models) comprising a system prompt filter, pre-processing filter leveraging a toxic classifier and ethical prompt generator, and pre-display filter using the model itself for output screening. Extensive testing on Meta’s Llama-2 model demonstrates the capability to block 100% of attack prompts. The approach also auto-suggests safer prompt alternatives, thereby bolstering language model security. Quantitatively evaluated defense layers and an ethical substitution mechanism represent key innovations to counter sophisticated attacks. The integrated methodology not only fortifies smaller LLMs against emerging cyber threats but also guides the broader application of LLMs in a secure and ethical manner.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.