Active Defense Research Articles

Intelligent Optimization Method and Network Security Analysis for Power System Active Control

Abstract Optimizing the active control of the power system and improving the stability of the system in the face of cyber-attacks are necessary to secure the power supply and achieve energy saving and emission reduction. The article proposes an improved granular computing method applied to power system active control, which includes chaotic initialization, dynamic parameter adjustment, and a fast search strategy. And also provides two strategies for defense against FDIA and DDoS attacks. The minimum cost of the IGrC algorithm in 5, 10 and 30 unit systems are 43115.67$, 1017569.51$ and 10170321$, and it has good convergence and robustness. The F1 value of the vertical and horizontal prediction algorithm used is more than 90% in all environments, and the optimal marginal cost of this paper’s algorithm is 44.8357 regardless of whether or not it is facing DDoS attacks. Therefore, the active control optimization method and attack defense strategy proposed in this paper have practical application effects.

Active Security Defense Model for New Power Systems

This article proposes a C-PDR active dynamic security model based on the WPDRRC network dynamic security model, combined with the new power system security characteristics, starting from pre judgment, in-process detection, and post response. This model constructs a comprehensive defense in depth through boundary defense, network security protection, host intrusion prevention, application and data intrusion prevention, and can fundamentally address the network threats faced by the power system.

Arabidopsis thaliana protein NSL1 interacts with Pseudomonas syringae pv. tomato DC3000 effector HopM1 in a yeast 2-hybrid assay.

Arabidopsis thaliana proteins NECROTIC SPOTTED LESIONS 1 ( NSL1 ) and CONSTITUTIVE ACTIVE DEFENSE 1 ( CAD1 ) have previously been linked to immunity against phytopathogens such as Pseudomonas syringae pv. tomato ( Pst ) DC3000 (Noutoshi et al. 2006; Tsutsui et al. 2008; Asada et al. 2011; Fukunaga et al. 2017; Holmes et al. 2021). Here, we used a yeast 2-hybrid (Y2H) approach to explore their potential to interact with Pst DC3000 effectors. We found that NSL1 , but not CAD1 , interacted with the Pst DC3000 effector HopM1. Although further experiments are needed to validate this interaction, our results suggest that NSL1 may be a host target of HopM1.

Secure Consensus Control of Multiagent Systems Under DoS Attacks: A Switching-Scheme-Based Active Defense Method.

This article studies the secure consensus control problem of multiagent systems (MASs) with a nonzero input leader subject to denial-of-service (DoS) attacks. The introduction of backup topologies makes it possible for MASs to actively defend against DoS attacks. Subsequently, a novel active defense method consisting of two types of state observers, an adaptive topology switching mechanism, and switching controllers is proposed, which can ensure the leader-follower bound consensus even if DoS attacks hinder the interaction between agents. Within such a defense framework, the switching mechanism, driven by the predefined performance index and designed monitoring function, can automatically search for a healthy communication graph among backup topologies. Concurrently, the observer-based switching control strategy will be modified to match the corresponding topology, in which the universal observer and controller parameters in different topologies are obtained by solving linear matrix inequalities. It should be highlighted that the developed defense scheme not only removes the limitations of existing results on the duration and frequency of DoS attacks but also ensures the same upper bound of consensus error before and after DoS attacks. Finally, several simulation examples for different systems illustrate the efficiency and superiority of the theoretical results.

Active defense strategies for invasive plants may alter the distribution pattern of pests in the invaded area.

In the invaded areas, it is believed that invasive species reduce their investment in defense due to the absence of natural enemies. By field investigation and a series of laboratory assays, This study explored the defense strategies of invasive plants. Field investigation indicated that invasive plants have a antifeedant effect on herbivorous pests, and the distribution frequency of wormholes of native plants shows a peak at a distance of 2-3 m from the invasive species. The feeding preference experiment conducted with two generalist herbivorous insects (native insect Spodoptera litura and invasive insect Spodoptera frugiperda) showed that the invasive plants have a stronger antifeedant effect than native plants. By analyzing the content of secondary metabolites in the leaves of three invasive plants (Sphagneticola trilobata, Mikania micrantha, Ipomoea cairica) and three native plants (Ipomoea nil, Paederia foetida, Polygonum chinense), the leaves of invasive plants had higher concentrations of substances associated with defenses, including total phenols, flavonoids, jasmonic acid, tannin, H2O2, and total antioxidant capacity (TAC), and lower soluble protein concentrations than native plants. After leaf damage, compared to native plants, the leaves of invasive plants showed an overall increase in substances associated with defense, except for soluble sugar. These results suggest that invasive plants maintain active defense strategies in invaded areas, leading to changes in the distribution patterns of herbivorous insects in a manner that facilitates invasion.

Impact Angle Constraint Guidance Against Active Defense Target Based on Partial Differential Games

For intercepting active defense target, an impact angle constraint guidance law is proposed based on partial differential games. First, the relative motion models of three‐player games between interceptor, target, and defense missile are established. The problem of intercepting active defense target is separated into two partial games. Then, two partial coupled game guidance laws are obtained, respectively. Impact angle constraint is taken into account in the game between attacker and defender (D). Finally, a combined guidance law is calculated by the two partial differential games. The performance of the strategy can be adjusted by the weight coefficient related to the miss distance between the attacker and the D. Simulations show that the guidance law can intercept the target with impact angle constraint and evade D’s interception at the same time.

Measuring network security in the cloud : A roadmap for proactive defense

An exhaustive examination and evaluation of hierarchy network security measuring and optimal active defense in the context of cloud computing is provided in this paper. After collecting all the cloud platform’s security-related data obtained via evaluation, the pertinent security data is summarized and analyzed to create the cloud platform’s safety record index. This index serves as a benchmark for the cloud platform administrators to evaluate the security vulnerabilities associated with their cloud platform. It offers cloud platform administrators a standard against which to assess the security hazards of cloud platforms. We primarily investigate the building of the cloud platform, the building process of the security situational awareness system, and the estimation of the safety circumstance value and use via the cloud platform’s security circumstance understanding system, thereby significantly enhancing the cloud platform’s stability, security, and dependability. By implementing the approach, the limitations of conventional network security management are circumvented, as the latter is predicated solely on historical data and is incapable of perceiving real-time alterations in the security condition of the system. Concurrently, the anticipated outcomes are incorporated into the imprecise decision-making system’s input, enhancing the evaluation’s precision. The approach improves the efficiency and efficacy of predicting the network safety situation in real time, augments the algorithm’s rate of convergence and prediction precision, and prevents overfitting from occurring. Simulation experiments conducted using the internet network safety posture dataset demonstrate that this research approach exhibits superior learning efficiency and lower prediction error compared to conventional machine learning and other deep learning techniques.

Two-Stage Game Guidance Strategy With Impact Point for Active Defense Aircraft in Two-on-Two Engagement

Two-Stage Game Guidance Strategy With Impact Point for Active Defense Aircraft in Two-on-Two Engagement

DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models

Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.

Effects of nitric oxide, spermidine, and salicylic acid signaling and their crosstalk with each other in the production of commercially important stevioside content and drought stress responses in Stevia rebaudiana bertoni

The leaves of Stevia rebaudiana Bertoni contain steviol glycosides (SGs), which provide the sweet taste of stevia. However, drought can have a negative impact on the plant's growth and development. To address this issue, signaling molecules such as sodium nitroprusside (SNP), spermidine (SPD), and salicylic acid (SA) are often applied to increase plant tolerance. However, the combined effects of these molecules have not been extensively studied. This research aimed to investigate the effects of controlled elicitation with SA, SNP, SPD, and their combinations on plant performance, SG content, and drought stress mitigation in Stevia rebaudiana under drought stress. The elicitor treatments were found to result in a significant increase in SG content, with 0.1 mM SA being the most effective treatment. Additionally, the treatments were able to reduce the stress effects on growth parameters to non-stress levels. The use of SPD, SA+SNP, and SPD+SNP on stressed plants significantly increased CAT and SOD activity, resulting in a more active antioxidant defense system that lowered MDA contents and H2O2 generation. These findings suggest that stevia cultivation with controlled elicitation could be used to improve plant growth, tolerance, and SG production under drought stress conditions.

Identifikasi Bahaya Keamanan Siber pada Perizinan Bahan Nuklir Online di Indonesia

A study has identified Cybersecurity Hazards in Indonesia's Online Nuclear Material Licensing process. This study was conducted based on Government Regulation 5 of 2021, which concerns Implementing Risk-Based Business Licensing. The online Nuclear Material Licensing system is still in development and is integrated with the Online Single Submission (OSS) system, which the Ministry of Investment coordinates. Data integration poses a severe risk to cybersecurity, especially regarding personal data security. Hackers can enter the system and wipe out all the data. The system has already been tested with permit holders, and there is a lot of feedback regarding Cybersecurity Hazards. This paper discusses some recommendations to protect cybersecurity in the Online licensing system. These include appointing institutions authorized in cybersecurity, implementing active defense mechanisms to reduce bureaucratic complexity, and improving the quality and capacity of human resources, technology, budget, business processes, work programs, and so on. The study results related to public involvement in online system features include personal data leakage, Hacking Hazards, email bombing hazards, spam, password crackers, botnets, Distributed Denial of Service (DDoS) incident hazards, Spoofing, Phishing, Malicious Software (code), and Storage Media. BAPETEN needs to screen Nuclear Material Information at Balis L Nino to mitigate these risks and add data security facilities. The information in the license must be general and include quotas for the total quantity, maximum volume, and maximum weight, which is the maximum nuclear material capacity. Keywords: cybersecurity, business licensing, nuclear material licensing, online single submission

Rice resistance against Bipolaris oryzae infection is mediated by lower foliar potassium concentration

AbstractBrown spot, caused by Bipolaris oryzae, is a very important disease of rice. This study investigated the effect of potassium (K) on rice resistance to brown spot. The working hypothesis tested was that higher foliar K concentration could allow plants to respond more efficiently against fungal infection. Plants were grown in nutrient solution amended with three K rates (0.5, 1.0 and 2.5 mM) and noninoculated or inoculated with B. oryzae. The photosynthetic performance of plants, activities of defence and antioxidant enzymes and the concentrations of reactive oxygen species, phenolics and lignin were determined. Foliar K concentration was significantly higher by 38% and 91% for plants supplied with 1.0 and 2.5 mM K, respectively, compared to plants supplied with 0.5 mM K. Brown spot severity was significantly higher (≥20%) for plants supplied with 1.0 and 2.5 mM K than those supplied with 0.5 mM K (≤15%). Higher brown spot severity for plants supplied with 2.5 mM K resulted in changes in the photosynthetic apparatus, reduced chlorophyll a + b and carotenoids concentrations, and higher production of hydrogen peroxide and superoxide anion radical. In contrast, higher activities of defence and antioxidant enzymes and more production of phenolics for plants supplied with 0.5 mM K helped them to cope with B. oryzae infection more efficiently. In conclusion, rice resistance against brown spot was achieved by keeping a lower foliar K concentration linked to more active defence reactions, a robust antioxidative system and less damage to the photosynthetic apparatus.

Changing turn-over rates regulate abundance of tryptophan, GS biosynthesis, IAA transport and photosynthesis proteins in Arabidopsis growth defense transitions

BackgroundShifts in dynamic equilibria of the abundance of cellular molecules in plant-pathogen interactions need further exploration. We induced PTI in optimally growing Arabidopsis thaliana seedlings for 16 h, returning them to growth conditions for another 16 h.MethodsTurn-over and abundance of 99 flg22 responding proteins were measured chronologically using a stable heavy nitrogen isotope partial labeling strategy and targeted liquid chromatography coupled to mass spectrometry (PRM LC–MS). These experiments were complemented by measurements of mRNA and phytohormone levels.ResultsChanges in synthesis and degradation rate constants (Ks and Kd) regulated tryptophane and glucosinolate, IAA transport, and photosynthesis-associated protein (PAP) homeostasis in growth/PTI transitions independently of mRNA levels. Ks values increased after elicitation while protein and mRNA levels became uncorrelated. mRNA returned to pre-elicitation levels, yet protein abundance remained at PTI levels even 16 h after media exchange, indicating protein levels were robust and unresponsive to transition back to growth. The abundance of 23 PAPs including FERREDOXIN-NADP( +)-OXIDOREDUCTASE (FNR1) decreased 16 h after PAMP exposure, their depletion was nearly abolished in the myc234 mutant. FNR1 Kd increased as mRNA levels decreased early in PTI, its Ks decreased in prolonged PTI. FNR1 Kd was lower in myc234, mRNA levels decreased as in wild type.ConclusionsProtein Kd and Ks values change in response to flg22 exposure and constitute an additional layer of protein abundance regulation in growth defense transitions next to changes in mRNA levels. Our results suggest photosystem remodeling in PTI to direct electron flow away from the photosynthetic carbon reaction towards ROS production as an active defense mechanism controlled post-transcriptionally and by MYC2 and homologs. Target proteins accumulated later and PAP and auxin/IAA depletion was repressed in myc234 indicating a positive effect of the transcription factors in the establishment of PTI.

The Meddling Professor: Bernard Crick's Active Defence of Politics

It's now fifteen years since the death of Bernard Crick. His contribution to modern scholarship and political writing continues to be recognised largely with reference to ‘the book’ and ‘the biography’, namely the ‘mere essay’ In Defence of Politics and his major work on George Orwell. The central contention of this article, however, is that Crick's corpus of work continues to have a wide contemporary relevance that reaches far beyond these two central reference points. Crick's arguments about ideology and political cultures remain hugely significant to anyone seeking to understand or contest the emergence of populism. Populism has, in recent years, reaffirmed its status as a highly effective mode of political galvanisation. Crick saw populism as the most urgent threat to politics and his concerns are widely echoed in academia and beyond. This article uses some of Crick's political ‘meddling’ in his final years as a lens through which to examine Crick's active defence of politics. These include Crick's deep engagement with Scottish political life, and manifestations of his truculent moderation and intellectual pluralism. It argues that Crick embodied a positive faith that, though powerful and serious, threats to politics could be disarmed and repelled.

Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense

Moving target defenses (MTDs) are widely used as an active defense strategy for thwarting cyberattacks on cyber-physical systems by increasing diversity of software and network paths. Recently, machine Learning (ML) and deep Learning (DL) models have been demonstrated to defeat some of the cyber defenses by learning attack detection patterns and defense strategies. It raises concerns about the susceptibility of MTD to ML and DL methods. In this paper, we analyze the effectiveness of ML and DL models when it comes to deciphering MTD methods and ultimately evade MTD-based protections in real-time systems. Specifically, we consider a MTD algorithm that periodically randomizes address assignments within the MIL-STD-1553 protocol—a military standard serial data bus. Two ML and DL-based tasks are performed on MIL-STD-1553 protocol to measure the effectiveness of the learning models in deciphering the MTD algorithm: 1) determining whether there is an address assignments change i.e. whether the given system employs a MTD protocol and if it does 2) predicting the future address assignments. The supervised learning models (random forest and k-nearest neighbors) effectively detected the address assignment changes and classified whether the given system is equipped with a specified MTD protocol. On the other hand, the unsupervised learning model (K-means) was significantly less effective. The DL model (long short-term memory) was able to predict the future addresses with varied effectiveness based on MTD algorithm's settings.

Cytological and transcriptomic analysis to unveil the mechanism of web blotch resistance in Peanut

BackgroundPeanut is an important oil crop worldwide. Peanut web blotch is a fungal disease that often occurs at the same time as other leaf spot diseases, resulting in substantial leaf drop, which seriously affects the peanut yield and quality. However, the molecular mechanism underlying peanut resistance to web blotch is unknown.ResultsThe cytological examination revealed no differences in the conidium germination rate between the web blotch-resistant variety ZH and the web blotch-susceptible variety PI at 12–48 hpi. The appressorium formation rate was significantly higher for PI than for ZH at 24 hpi. The papilla formation rate at 36 hpi and the hypersensitive response rate at 60 and 84 hpi were significantly higher for ZH than for PI. We also compared the transcriptional profiles of web blotch-infected ZH and PI plants at 0, 12, 24, 36, 48, 60, and 84 hpi using an RNA-seq technique. There were more differentially expressed genes (DEGs) in ZH and PI at 12, 36, 60, and 84 hpi than at 24 and 48 hpi. Moreover, there were more DEGs in PI than in ZH at each time-point. The analysis of metabolic pathways indicated that pantothenate and CoA biosynthesis; monobactam biosynthesis; cutin, suberine and wax biosynthesis; and ether lipid metabolism are specific to the active defense of ZH against YY187, whereas porphyrin metabolism as well as taurine and hypotaurine metabolism are pathways specifically involved in the passive defense of ZH against YY187. In the protein-protein interaction (PPI) network, most of the interacting proteins were serine acetyltransferases and cysteine synthases, which are involved in the cysteine synthesis pathway. The qRT-PCR data confirmed the reliability of the transcriptome analysis.ConclusionOn the basis of the PPI network for the significantly enriched genes in the pathways which were specifically enriched at different time points in ZH, we hypothesize that serine acetyltransferases and cysteine synthases are crucial for the cysteine-related resistance of peanut to web blotch. The study results provide reference material for future research on the mechanism mediating peanut web blotch resistance.

Cooperative Guidance Strategy for Active Spacecraft Protection from a Homing Interceptor via Deep Reinforcement Learning

The cooperative active defense guidance problem for a spacecraft with active defense is investigated in this paper. An engagement between a spacecraft, an active defense vehicle, and an interceptor is considered, where the target spacecraft with active defense will attempt to evade the interceptor. Prior knowledge uncertainty and observation noise are taken into account simultaneously, which are vital for traditional guidance strategies such as the differential-game-based guidance method. In this set, we propose an intelligent cooperative active defense (ICAAI) guidance strategy based on deep reinforcement learning. ICAAI effectively coordinates defender and target maneuvers to achieve successful evasion with less prior knowledge and observational noise. Furthermore, we introduce an efficient and stable convergence (ESC) training approach employing reward shaping and curriculum learning to tackle the sparse reward problem in ICAAI training. Numerical experiments are included to demonstrate ICAAI’s real-time performance, convergence, adaptiveness, and robustness through the learning process and Monte Carlo simulations. The learning process showcases improved convergence efficiency with ESC, while simulation results illustrate ICAAI’s enhanced robustness and adaptiveness compared to optimal guidance laws.

The role of signaling compounds in enhancing rice allelochemicals for sustainable agriculture: an overview.

Plant phytotoxin synthesis is influenced by intricate signaling networks like jasmonic acid (JA) and salicylic acid (SA). These compounds not only induce allelochemical production but also aid weed suppression and plant immunity. (-)-Loliolide, JA, SA, and their derivatives trigger rice allelochemical synthesis and gene expression. Enhancing allelochemical synthesis in crops offers an alternative, reducing reliance on traditional herbicides for effective weed management. Rice (Oryza sativa L.) serves as a crucial staple food crop, nourishing over half of the global population, particularly in South Asia. Within rice plants, various secondary metabolites are produced, contributing to its nutritional value and providing energy to consumers. Over the last 5 decades, researchers have investigated 276 distinct types of secondary metabolites found in rice plants. These metabolites predominantly include phenolic acids, flavonoids, steroids, alkaloids, terpenoids, and their derivatives. The role of these secondary metabolites is to regulate the growth and development of the rice plant. In this research paper, we have focused on the allelopathic potential of rice, which involves its active defense strategy to suppress other species in its vicinity. This defense mechanism is regulated by plant signaling compounds. These signaling compounds enable rice plants to recognize and detect competitors, pathogens, and herbivores in their environment. As a response, the rice plants elevate the production of defensive secondary metabolites. One crucial aspect of rice allelopathy is the phenomenon of neighbor detection. Rice plants can sense the presence of neighboring plants and respond accordingly to establish their competitive advantage and ensure their survival. This paper specifically highlights the impact of exogenously applied signaling compounds, namely Methyl salicylate (MeSA) and Methyl Jasmonate (MeJA), on paddy rice. The aim is to provide deeper insights into the signaling mechanisms involved in rice allelopathy and how the exogenous application of signaling compounds influence the induction and regulation of defensive secondary metabolites in rice plants. Comprehensive analysis of various researchers' studies clearly reveals that the application of these elicitor compounds noticeably augments the allelopathic potential of rice, resulting in heightened accumulation of phenolic acid compounds. Expansion in more enlistment of phenolics may be because of expansion in the activities of enzymes, such as cinnamate 4-hydroxylase (C4H) and phenylalanine ammonia-lyase (PAL), the two main enzymes of the phenylpropanoid pathway, which are associated with allelopathic crop plants, and along this, they recognize the presence of weeds and react by expanding allelochemical focuses. Consequently, substantial endeavors have been dedicated in recent times to discover and characterize plant-derived signaling molecules. In bioassays conducted by Patni et al. in 2019, both competitive and non-competitive rice genotypes exhibited elevated phytotoxicity against Echino colona following treatment with MeSA. MeSA-treated rice plants displayed accelerated growth, increased yield, and concurrently demonstrated weed-suppressing properties. Published studies from 1976 to 2021 are reviewed in this paper. The study indicates that signaling compounds induce allelochemical concentrations, enhancing allelopathic activity. This insight may lead to development of novel herbicides for effective sustainable weed management.

Multidomain active defense: Detecting multidomain backdoor poisoned samples via ALL-to-ALL decoupling training without clean datasets

Deep learning is vulnerable to backdoor poisoning attacks in which an attacker can easily embed a hidden backdoor into a trained model by injecting poisoned samples into the training set. Many prior state-of-the-art techniques for detecting backdoor poisoning attacks are based on a potential separability assumption. However, current adaptive poisoning strategies can significantly reduce ’distinguishable behavior’, making most prior state-of-the-art techniques less effective. In addition, we note that existing detection methods are not practical for multidomain datasets and may leak user privacy because they require and collect clean samples. To address the above issues, we propose a multidomain active defense approach that does not use clean datasets. The proposed approach can generate diverse clean samples from different domains and decouple neural networks round by round using clean samples to disassociate features and labels, making backdoor poisoned samples easier to detect without fitting clean samples. We demonstrate the advantage of our approach through an extensive evaluation of CIFAR10, CelebA, MNIST & MNIST-M, MNIST & USPS & MNIST-M, MNIST & USPS & SVHN and CIFAR10 & Tiny-ImageNet.

Enabling cybersecurity incident response agility through dynamic capabilities: the role of real-time analytics

ABSTRACT We explore how organisations enable agility in their cybersecurity incident response (IR) process by developing dynamic capabilities using real-time analytics (RTA). Drawing on RTA practices in the IR process at three large financial organisations, we develop a framework to explain how IR teams respond to the rapidly evolving cyber threat environment by developing RTA-based microfoundations that underpin the building of sensing, seizing, and transforming dynamic IR capabilities. These dynamic IR capabilities in turn help organisations to enable agility in their IR processes by leveraging swift, flexible, and innovative IR strategies, including active threat reconnaissance, active threat defence, and pervasive learning. Our findings have implications for the discourse on cybersecurity because we demystify the black box of IR agility, for our understanding of the use of RTA to enable agility in IR, and for the discourse on dynamic capabilities.