User Station Security Protection Method Based on Random Domain Name Detection and Active Defense

The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense effectiveness.Therefore,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense strategies.the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

Novel 18β-glycyrrhetinic acid amide derivatives show dual-acting capabilities for controlling plant bacterial diseases through ROS-mediated antibacterial efficiency and activating plant defense responses

Natural products have long been a crucial source of,or provided inspiration for new agrochemical discovery.Naturally occurring 18β-glycyrrhetinic acid shows broad-spectrum bioactivities and is a potential skeleton for novel drug discovery.To extend the utility of 18β-glycyrrhetinic acid for agricultural uses,a series of novel 18β-glycyrrhetinic acid amide derivatives were prepared and evaluated for their antibacterial potency.Notably,compound 5k showed good antibacterial activity in vitro against Xanthomonas oryzae pv.oryzae(Xoo,EC50=3.64 mg L–1),and excellent protective activity(54.68%)against Xoo in vivo.Compound 5k induced excessive production and accumulation of reactive oxygen species in the tested pathogens,resulting in damaging the bacterial cell envelope.More interestingly,compound 5k could increase the activities of plant defense enzymes including catalase,superoxide dismutase,peroxidase,and phenylalanine ammonia lyase.Taken together,these enjoyable results suggested that designed compounds derived from 18β-glycyrrhetinic acid showed potential for controlling intractable plant bacterial diseases by disturbing the balance of the phytopathogen’s redox system and activating the plant defense system.

A Security Transfer Model Based on Active Defense Strategy

This paper proposes a security transfer model founded on the active defense strategy. In the unit of security domains of dynamic overlaying routers, this model defines the intelligent agent/ management of network element together with the self-similar, hierarchical and distributed management structure. Furthermore, we use deceptive packets so that the attackers can not trace back to the encrypted data packets. Finally, according to the digested information from data packets, this model is capable of detecting attacks and tracing back to the attackers immediately. In the meantime, the overlaying routers in the security domain are dynamically administered. In summary, this model not only improves the se-curity of data transfer on the web, but also enhances the effectiveness of the network management and switching efficiency of routers as well.

A NEW DYNAMIC DEFENSE MODEL BASED ON ACTIVE DECEPTION

Aiming at the traditional passive deception models,this paper constructs a Decoy Platform based on Intelligent Agent(DPIA) to realize dynamic defense.The paper explores a new dynamic defense model based on active deception,introduces its architecture,and expatiates on communication methods and security guarantee in information transference.Simulation results show that the DPIA can attract hacker agility and activity,lead abnormal traffic into it,distribute a large number of attack data,and ensure real network security.

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.

DLP:towards active defense against backdoor attacks with decoupled learning process

Deep learning models are well known to be susceptible to backdoor attack,where the attacker only needs to provide a tampered dataset on which the triggers are injected.Models trained on the dataset will passively implant the backdoor,and triggers on the input can mislead the models during testing.Our study shows that the model shows different learning behaviors in clean and poisoned subsets during training.Based on this observation,we propose a general training pipeline to defend against backdoor attacks actively.Benign models can be trained from the unreli-able dataset by decoupling the learning process into three stages,i.e.,supervised learning,active unlearning,and active semi-supervised fine-tuning.The effectiveness of our approach has been shown in numerous experiments across various backdoor attacks and datasets.

Effects of Spores and Crude Toxins of Helminthosporium gramineum Rabenh f.sp.echinochloae on the Activity of Defensive Enzymes in Barnyardgrass

[Objective] This study aimed to explore the effects of spores and crude toxins of Helminthosporium gramineum Rabenh f. sp. echinochloae（HGE） on the ac- tivity of defensive enzymes of barnyardgrass [Echinochloa crus-galli （L.） Beauv.]. [Method] The effects of spores and crude toxins of HGE, as well as the mixture of spores and crude toxins on the activity of defensive enzymes in barnyardgrass were determined under laboratory conditions. [Result] Spores and crude toxins of HGE had varying degrees of effects on PAL and POD activity, and no obvious effect on SOD activity in barnyardgrass. In addition, spores and toxins had some similar im- pacts on the defensive enzymes in barnyardgrass. [Conclusion] Since toxins have similar effects on the hosts as spores of fungal pathogen do, they can be a substi- tute for the fungal pathogen in studying the partial pathogenic mechanism of this pathogen due to its complexity in pathogenic process.

Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

Herbicidal activity and biochemical characteristics of the botanical drupacine against Amaranthus retroflexus L.

Botanical herbicide has been a hot topic in the research and development of novel pesticides. The herbicidal activity and biochemical characteristics of the botanical compound drupacine were studied by evaluating its effects on seed germination, seedling growth, morphological and physiological characteristics of Amaranthus retroflexus. Drupacine inhibited seed germination and seedling growth, and had a median inhibition concentration(IC50) value of 38.99 mg L-1against A. retroflexus root. The α-amylase activity and soluble sugar content in treated plants were significantly lower than that of the control. The expression of α-amylase gene was dosage-dependently inhibited compared to the untreated control. This suggested that inhibition of α-amylase activity was a mode of action on seed germination. The root hairs were significantly decreased and part of the root cap fell off after treatment with drupacine. The ultrastructure observation showed that cell damage of root tips increased with the treatment time. Drupacine also increased the relative conductivity and malondialdehyde(MDA) content. Peroxidase(POD), catalase(CAT), and superoxide dismutase(SOD) activities were significantly enhanced in the treatment compared to the control. These findings indicated that the physiological and biochemical reaction changes leading to morphological and membrane injuries were the main effects of drupacine on the inhibition of seedling growth. Drupacine can be developed as a botanical herbicide.

Geographically Distinct Expression Profile of Host Defense Peptides in the Skin of the Chinese Odorous Frog, Odorrana margaretae

Odorrana margaretae （Anura： Ranidae） is widely distributed in the southern provinces of China. Previously, 72 antimicrobial peptides （AMPs） belonging to 21 families were identified from the skin of O. margaretae, which were captured in the Hunan province. In the present study, five O. margaretae frogs were captured from the Guizhou province and a total of 28 cDNAs encoding 17 host defense peptides （HDPs） belonging to 14 families were cloned from the skin cDNA library of O. margaretae. Among the 17 HDPs, only one （brevinin-1-Omar5） had been characterized. The distinct HDP expression profiles for O. margaretae in the previous and present study may be attributed to the environmental differences between the sampling locations and the genetic divergence among O. margaretae populations. Besides, 11 of the 17 HDPs identified in the present study were novel for ranids. In order to understand their roles in host defense reactions, three HDPs （odorranain-H-OM1, odorranain-M-OM and ranatuerin-2-OM）, which possess low sequence similarity with the known amphibian HDPs, were selected for further chemical synthesis and functional analysis. Odorranain-H-OM1 showed direct antimicrobial activity against bacteria and fungi. Odorranain-M-OM exhibited concentration-dependent anti-oxidant activity. Ranatuerin-2-OM showed lectin-like activity and could strongly hemagglu -tinate human intact erythrocytes with or without the presence of Ca2＋. The diverse activities of HDPs implied that they may play different roles in host defense reactions of O. margaretae.

An Exploration of the Mechanism of Educational Scientific Decision-making in View of Bounded Rationality

The scientific decision-making of education policies is not absolutely a complete rational process.The bounded rationality which takes rationality as the judgement standard is the essential connotation of the scientific decision-making of education policies.Therefore,based on this view and through shaping reasonable education policy values,this research gives full play to the driving force of educational scientific research on education policies and designs effective education decision-making information content and running agenda,thus optimizing the weigh principle of education policy proposals,advocating risk assessment and practice of education decisions,constructing institutional rationality of educational scientific decision-making,and finally realizing the rationality and scientificity of education policy decision-making.

Research on Cyberspace Attack and Defense Confrontation Technology

This paper analyzes the characteristics of Interact space and confrontation, discussed on the main technology of network space attack and defense confrontation. The paper presents the realization scheme of network space attack defense confrontation system, and analyzes its feasibility. The technology and the system can provide technical support for the system in the network space of our country development, and safeguard security of network space in China, promote the development of the network space security industry of China, it plays an important role and significance to speed up China＇ s independent controllable security products development.

Active resilient defense control against false data injection attacks in smart grids

The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.

攻防对抗视角下的网络安全主动防御体系研究

为了解决纵深防御体系中防御能力固化、灵活性不足等问题,从攻防对抗视角出发,提出一种网络安全主动防御体系。首先,基于情报收集、监测预警、防御指挥和防御行动等要素的详细描述,设计了用于规划主动防御系统的能力组成框架;其次,选择网络杀伤链、ATT&CK知识库和OODA循环法则,设计了一个以攻助防的防御模型,并分析了引入博弈论进行防御算法设计的优势;最后,以实现为目标,构想了一个典型应用场景,以期为安全能力的体系化设计提供有益借鉴。

复合木霉制剂防治黄连根腐病及其机理研究

为评价复合木霉制剂对黄连根腐病的防治效果,并揭示其防病机理,为黄连根腐病专用微生物农药的研发奠定基础,本试验将深绿木霉Trichoderma atroviride、长枝木霉T.longibrachiatum、钩状木霉T.hamatum、拟康宁木霉T.koningiopsis等4种木霉配制的复合制剂和尖镰孢Fusarium oxysporum以不同的方式分别施用于黄连,统计根腐病发生情况,检测黄连根部防御酶活性,用高通量测序分析根际土壤真菌群落结构。结果表明,复合木霉制剂对尖镰孢导致的根腐病具有明显预防效果;复合木霉制剂和尖镰孢分别接种黄连可提高SOD、POD、CAT、PAL、PPO等防御性酶活性,产生诱导抗性;而它们先后接种黄连可产生强化效应,从另一个途径提高植株抗病性。复合木霉制剂和尖镰孢都会降低真菌的数量、多样性,和某些真菌的相对丰度,而复合木霉制剂的抑菌作用更强烈,尤其能明显抑制尖镰孢、Ilyonectria sp.等病原真菌的生长,且能改善土壤真菌群落结构。木霉和尖镰孢都能在土壤中较长期定殖。可见,复合木霉制剂可以预防尖镰孢导致的黄连根腐病,防病机理包括诱导黄连植株产生抗性,接种后再遭受病原菌侵染产生的强化效应,优化土壤真菌群落结构,抑制土壤中病原菌等,且有效期较长。因此复合木霉制剂具有开发为微生物农药防治黄连根腐病的潜力。

三体对抗中的主动防御鲁棒最优预测制导律研究

考虑防御导弹和我方目标控制系统内部扰动和未准确建模动态特性等因素造成的不确定性影响,设计了鲁棒最优预测制导律(robust optimal predictive guidance law,ROPGL)和鲁棒协同最优预测制导律(robust cooperative optimal predictive guidance law,RCOPGL)拦截来袭寻的导弹.首先,通过对非线性运动学方程线性化和降阶得到简化后的标称系统的状态方程;其次,考虑控制系统的不确定性,建立鲁棒制导问题的模型;再次,基于动态规划方法得到最优制导问题的制导指令关系式,并利用李雅普诺夫稳定性理论证明了最优制导问题的制导指令能够使鲁棒制导问题稳定;然后,引入界值函数得到线性二次型最优制导问题,通过求解黎卡提矩阵微分方程得到制导指令;最后,对设计的制导律的性能进行仿真验证.仿真结果表明,相比于其他制导律,设计的制导律能够抑制不确定性的影响,实现防御导弹对寻的导弹的拦截.

面向人脸属性编辑的三阶段对抗扰动生成主动防御算法

针对恶意人脸属性编辑行为,基于取证的被动防御技术只能对篡改行为进行取证并不能防止其产生,从而难以消除恶意篡改行为已经造成的损失.因此,主动防御技术应运而生,其可以破坏属性编辑的输出从而避免人脸被篡改使用.然而,现有两阶段训练人脸篡改主动防御框架存在迁移性和扰动鲁棒性不足的问题,为此本文通过优化两阶段训练架构及损失函数和引入一个辅助分类器,提出一种三阶段对抗扰动主动防御框架.本文首先修改两阶段训练架构中的代理目标模型并基于此设计了训练扰动生成器的属性编辑损失,以提升代理模型的重建性能和属性约束能力,从而减少对代理模型的过拟合;其次,在训练阶段引入辅助分类器对代理模型提取的编码后特征进行源属性分类并基于此设计训练扰动生成器的辅助分类器损失,从而将原本的两阶段交替训练改为代理目标模型、辅助分类器和扰动生成器的三阶段交替训练,期望通过对抗攻击辅助分类器以促进对篡改模型的主动防御;最后,在扰动生成器的训练中,引入攻击层以促进对抗扰动对滤波和JPEG压缩的鲁棒性.实验结果验证,本文提出的框架能够比现有框架更好地将主动防御从白盒的代理目标模型迁移到黑盒的属性编辑模型,黑盒性能提升16.17%,且生成的对抗扰动较基线算法具有更强的鲁棒性,针对JPEG压缩的性能(PSNR)提升13.91%,针对高斯滤波提升17.76%.

基于演化博弈的拟态防御策略优化

网络空间拟态防御是近些年出现的一种主动防御理论,以异构冗余和动态反馈机制不断调整执行环境来抵抗攻击。然而,面对黑客的多样化攻击手段,仅凭借拟态防御抵抗攻击是不安全的。为了增强系统的安全防御能力,在目前已有的防御系统基础上提出更为合理的防御选取方法。将有限理性的演化博弈引入到拟态防御中,构建了由攻击者、防御者和合法用户组成的三方演化博弈模型,并提出了最优防御策略求解方法。该博弈模型利用复制动态方程得到了演化稳定策略。仿真实验结果表明,系统通过执行推理的演化稳定策略可以降低损失,遏制攻击方的攻击行为,对拟态防御系统中防御策略选取和安全性增强具有一定的借鉴意义。

一种半监督学习潜艇规避防御智能决策方法

潜艇水下作战行动中,受到水下弱可观测环境影响,获取的目标信息呈现稀疏特性。机动规避是潜艇水下防御的重要战术方法,现有机动规避参数仿真与优化方法在建模时不可避免引入观测误差,缺乏对态势演变的应对手段,且由于军事专家的稀缺性,获取军事专家标签的战术对抗样本代价十分昂贵。针对上述困难提出一种基于自编码与主动Q学习策略结合的半监督学习智能决策方法。通过引入对比预测编码自编码器,最大化时序输入与上下文间互信息熵,提高对稀疏时序输入的表征能力。将表征输入与主动强化学习任务相结合,降低智能体的标签需求率,提高规避决策时对环境反馈的能力。基于3 a采集的指挥员战法研练复盘数据构建上帝视角、红方视角数据集。实验结果表明:所提算法与不采用稀疏时序自编码器的算法消融实验,在完全信息、红方视角条件下决策精度分别达到98%、78%,而标签需求率仅为4%、44%;相比于经典的时序分类算法决策精度提高了14%、9%,与有监督算法相比在标签需求率降低为原来的24%~44%条件下,决策精度误差与有监督算法仅差1%,说明所提算法在保证决策精度的同时可大幅降低标签需求量,从而为少量样本条件下的军事智能决策提供一种通用的技术框架。