Attack Behavior Research Articles

AECR: Automatic attack technique intelligence extraction based on fine-tuned large language model

Cyber Threat Intelligence (CTI) reports contain resourceful intelligence on cyber-attack campaigns, which provides great help for security analysts to infer attack trends and enhance their defenses. However, due to the diversity of report content and writing styles, current intelligence extraction is mostly based on time-consuming manual efforts. Moreover, existing automatic methods generally neglect the importance of background knowledge and produce inexact extraction results. These problems prevent the effective utilization and sharing of intelligence from CTI reports. In this paper, we primarily focus on the automatic extraction of attack technique (AT) intelligence, which reveals patterns of attack behaviors and hardly changes over time. We propose a novel automatic AT extraction pipeline for CTI reports (AECR). AECR explores the feasibility of extracting AT intelligence based on a fined-tuned large language model (LLM). Particularly, we endow the selected LLM with enhanced domain-specific knowledge to improve its comprehension of AT-relevant content and alleviate the hallucination problem. Experimental results demonstrate that AECR outperforms state-of-the-art methods by a wide margin with a reasonable time cost. Specifically, we improve the accuracy, precision, recall, and F1-score by 108%, 37.2%, 22.4%, and 67.5% respectively. To the best of our knowledge, AECR is the first to perform AT extraction based on fine-tuned LLM.

Cutting the sap: First molecular phylogeny of twig‐girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviours contributed to morphological evolution

AbstractUnderstanding how novel adaptive traits arise, evolve and impact other aspects of an organism's phenotype is a foundational question in evolutionary biology. We explore this by focusing on Onciderini (Coleoptera: Cerambycidae: Lamiinae), a tribe of longhorn beetles commonly referred to as twig girdlers because the females of some species girdle live trees to deposit their eggs. We reconstructed the first time‐calibrated phylogeny of the Onciderini, based on three genetic markers (cytochrome c oxidase subunit 1 ‘cox1’, Wingless ‘Wg’ and carbamoyl‐phosphate synthase domain of the CAD locus ‘CPS’), and used morphometric data, employing multiple models of trait evolution and phylogenetic regressions, to assess whether girdling behaviour predicts head size or head size sexual dimorphism. Our results indicate that onciderines originated c. 49 million years ago ‘Ma’ (95% highest posterior densities ‘HPD’: 44.1–54.57 Ma) and consist of two major lineages, which we formalize as subtribes Hypsiomina and Onciderina. Additionally, our analyses revealed several taxonomic inconsistencies within the tribe, which we rectify by proposing new synonymies, including Jamesia Jekel as a synonym of Hypselomus Perty, Cipriscola Dillon & Dillon of Hypsioma Audinet‐Serville and Psyllotoxus Thomson and Taricanus Thomson as a synonym of Oncideres Lacordaire, and a new combination for Periergates kenjii Nearns & Swift as Oncideres kenjii (Nearns & Swift) comb. nov. Using this new phylogenetic framework, we identified five independent unidirectional origins of girdling behaviour within the tribe. Overall, our results suggest that girdling influences head size evolution in Onciderini, as it is associated with an increase in both male and female head size of girdler species. Despite this, girdling and non‐girdling lineages do not consistently differ in head size sexual dimorphism. This study refines the classification of Onciderini and marks a significant step in understanding the evolutionary dynamics shaping the diversity of twig‐girdler beetles, a group with notable ecological and economic importance.

String stable control design for automated vehicles under cyberattacks

Emerging automated vehicle (AV) technologies open a door for cyberattacks, where a select number of AVs are compromised to drive in an adversarial manner, degrading the performance of transportation systems. Hence, designing stable controls for AVs that can mitigate the impact of attacks on traffic flow is important and necessary as AVs gradually become a reality. In this study, we first present a general framework for describing mixed-autonomy traffic involving AVs and human-driven vehicles (HVs) based on car-following dynamics. Under this framework, a class of malicious attacks on AV control commands (vehicle acceleration) is mathematically characterized. To deal with the lack of knowledge of the attacks, we model attacks as an unstructured process, allowing for the inclusion of both deterministic and stochastic attack behaviors, bounded by a known bound (to remain stealthy) without being subject to any specific statistical distribution. Moreover, we analytically derive a set of sufficient conditions for string stable control design of individual AVs which can help mitigate the disturbances to traffic flow caused by unknown attacks on AVs. Furthermore, for any given market penetration rate of AVs, a set of conditions is also derived for selecting appropriate feedback gains of AVs to ensure string stability of heterogeneous traffic, reducing the undesired impact of attacks on traffic flow. These sufficient conditions provide important criteria for string stable control design of AVs without requiring much knowledge of the attacks. A series of numerical results is presented to show effectiveness of the proposed approach on mitigating attack disruption.

Mitigation measures for pinniped-fisheries interactions based on knowledge of animal behavior

Abstract A rise in pinniped-fisheries interactions has been observed due to the global decline in fish populations primarily attributed to overfishing, and/or due to the recent population recovery of several pinniped species. Although many studies have quantified these interactions, a limited number delve into their behavioral aspects. We consulted 374 studies on pinniped-fisheries interactions from 1980 to 2020 and analyzed which of them addressed the interaction from a behavioral perspective. Additionally, we examined the role of learning in pinnipeds and their ability to capture prey from fishing gear. The stimulus-reward relationship in operational interactions was addressed in 51 (14%) studies, involving 10 (19%) of the world’s 54 pinniped species or subspecies. Depredation behavior (43%) and attack behavior (35%) were frequently reported during fishing interactions. Understanding associative and non-associative learning mechanisms has the potential to reduce economic losses from fishing activity, advance efficient mitigation measures to minimize conflicts, and establish pragmatic conservation priorities based on a better understanding of the behavioral aspects of these interactions. More research is needed to explore the effectiveness of deterrents in different species, improve methods of reducing habituation, and evaluate long-term behavioral effects, fostering effective strategies for minimizing conflicts and promoting coexistence between humans and pinnipeds.

H∞$$ {\boldsymbol{H}}_{\mathbf{\infty}} $$ Control With Event‐Triggered Mechanism for T‐S Fuzzy System Under Multiple Cyber‐Attacks

ABSTRACTThe problem of event‐triggered control for networked Takagi–Sugeno (T‐S) fuzzy system under aperiodic DoS attacks and deception attacks is investigated. First, an event generator is introduced in the Sensor‐to‐Controller channel to determine the transmission of data. At the same time, the Sensor‐to‐Controller channel is assumed to be subjected to deception attacks that are randomly distributed but not Bernoulli distributed. Next, the impact of aperiodic DoS attacks on the Controller‐to‐Actuator channel is further considered, and the DoS attack behavior is described in terms of attack period and frequency. The article designs an adaptive resilience event‐triggered mechanism (ARETM), which is aimed at circumventing the ineffective data updating in the “active” phase of the DoS attacks, thereby realizing the effective saving of communication resources and mitigating the adverse effects of DoS attacks. Then, the switched fuzzy system is established to cope with the different states of the DoS attackers. Using the piecewise Lyapunov function, a design method for the controller gains and the ARETM matrix is obtained, which allows the system to be stabilizable and obtain performance under the control action. Finally, the effectiveness of the ARETM‐based control strategy is confirmed by simulation experiment.

Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack.

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.

A robust federated learning algorithm for partially trusted environments

Due to the distributed nature of federated learning, it is vulnerable to poisoning attacks during the training process. The model’s resistance to poisoning attacks can be improved using robust aggregation algorithms. Current research on federated learning to resist poisoning attacks is mainly based on two settings: No trust or Byzantine robustness. However, both settings are not close enough to reality in practical scenarios. In many practical applications, some participants in federated learning are trustworthy. For example, participants who have participated in the training of this model before and performed very well, or participants with strong compliance and credibility such as governments and some national agencies participate in the training. In existing research, these trusted participants still have to accept the judgment of the aggregation node, which generates unnecessary computation, increases overhead, and does not take advantage of a trusted environment. Since there is no attack behavior on the trusted client, its training results are used to classify the trustworthiness of other untrusted clients and identify attack nodes with higher accuracy. Therefore, this paper proposes a robust federated learning algorithm for partially trusted environments. The proposed scheme uses the experimental results of trusted clients to judge the behavior of untrustworthy clients by the cosine similarity and the Local Outlier Factor and further identify and detect malicious clients. Experiments are performed on MNIST and CIFAR datasets. Comparison with other six aggregation algorithms under 30% attack scenario. And compared with the other four aggregation algorithms under 70% attack conditions. Our algorithm is more accurate than almost all of the other aggregation algorithms. The paper is the first to conduct robust research on federated learning in a partially trusted environment, and the proposed algorithm can more effectively resist poisoning attacks.

Adaptive learning-based hybrid recommender system for deception in Internet of Thing

In the rapidly evolving Internet of Things (IoT) security domain, device vulnerabilities pose significant risks, frequently exploited by cyberattackers. Traditional reactive security measures like patching often fall short against advanced threats. This paper introduces a proactive deception system enhanced by an innovative Adaptive Learning-based Hybrid Recommender System (AL-HRS), utilizing the vulnerability and attack repository for IoT (VARIoT) database. This advanced system identifies existing vulnerabilities and dynamically recommends additional deceptive vulnerabilities based on real-time analysis of attacker behavior and historical exploit data. These recommended vulnerabilities mislead attackers into engaging with controlled environments such as honeypots, effectively neutralizing potential threats. The AL-HRS combines the predictive strengths of content-based filtering (CBF) and collaborative filtering (CF) with an adaptive learning mechanism that adjusts recommendations based on ongoing attacker interactions, ensuring the system’s efficacy amidst changing attack patterns. Our approach innovatively combines these methodologies to provide a continuously evolving security strategy, significantly enhancing the deception capability of IoT systems. Initial evaluations demonstrate a potential reduction in device compromise, highlighting the effectiveness and strategic relevance of this adaptive deception framework in IoT cybersecurity.

TIMFuser: A multi-granular fusion framework for cyber threat intelligence

Cyber attack campaigns with multiple technical variants are becoming increasingly sophisticated and diverse, posing great threats to institutions and every individual. Cyber Threat Intelligence (CTI) offers a novel technical solution to transition from passive to active defense against cyber attacks. To counter these attacks, security practitioners need to condense CTIs from extensive CTI sources, primarily in the form of unstructured CTI reports. Unstructured CTI reports provide detailed threat information and describe multi-step attack behaviors, which are essential for uncovering complete attack scenarios. Nevertheless, automatic analysis of unstructured CTI reports is challenging. Furthermore, manual analysis is often limited to a few CTI sources. In this paper, we propose a multi-granular fusion framework for CTIs from massive CTI sources, comprising a comprehensive pipeline with six subtasks. Many current CTI extraction systems are limited by mining intelligence from a single source, thereby leading to challenges such as producing a fragmented view of attack campaigns and lower value density. We fuse the attack behaviors and attack techniques of the attack campaigns using innovative and improved multi-granular fusion methods and offer a comprehensive view of the attack. TIMFuser fills a critical gap in the automated analysis and fusion of multi-source CTIs, especially in the multi-granularity aspect. In our evaluation of 739 real-world CTI reports from 542 sources, experimental results demonstrate that TIMFuser can enable security analysts to obtain a complete view of real-world attack campaigns, in terms of fused attack behaviors and attack techniques.

Neuroethology: A Review of Studies on Combat Behavior in Mice

Optogenetics has revolutionized neuroscience, enabling precise control and monitoring of neural circuits. This study focuses on the neural mechanisms underlying combat behaviors in mice, particularly the roles of the ventromedial hypothalamus (VMH) in freezing, jumping escape, and defensive attack behaviors. The research employed a combination of optogenetic tools, behavioral assays, and high-resolution imaging techniques. Genetically modified mice expressing channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR) were used. Viral vectors were injected into specific brain regions, and optogenetic stimulation was performed to activate or inhibit neuronal populations. Behavioral responses were documented and analyzed using specialized software, and brain tissues were prepared for histological examination. Activation of VMH neurons increased jumping escape behavior, while inhibition reduced defensive attack behaviors, highlighting the VMH's critical role in these responses. High-resolution video analysis provided detailed metrics on behavior, and histological examination confirmed precise targeting and expression of optogenetic proteins in the brain. The results demonstrated the direct influence of specific neural circuits on combat behaviors. The study elucidated the roles of VMH and associated neural circuits in mediating combat behaviors in mice. Optogenetic techniques provided precise control over neuronal activity, revealing critical insights into the neural mechanisms underlying these behaviors. The findings support the VMH's central role in regulating instinctual defensive actions and offer potential applications for developing therapeutic strategies for anxiety and pain disorders.

A novel approach for APT attack detection based on an advanced computing

To enhance the effectiveness of the Advanced Persistent Threat (APT) detection process, this research proposes a new approach to build and analyze the behavior profiles of APT attacks in network traffic. To achieve this goal, this study carries out two main objectives, including (i) building the behavior profile of APT IP in network traffic using a new intelligent computation method; (ii) analyzing and evaluating the behavior profile of APT IP based on a deep graph network. Specifically, to build the behavior profile of APT IP, this article describes using a combination of two different data mining methods: Bidirectional Long Short-Term Memory (Bi) and Attention (A). Based on the obtained behavior profile, the Dynamic Graph Convolutional Neural Network (DGCNN) is proposed to extract the characteristics of APT IP and classify them. With the flexible combination of different components in the model, the important information and behavior of APT attacks are demonstrated, not only enhancing the accuracy of detecting attack campaigns but also reducing false predictions. The experimental results in the paper show that the method proposed in this study has brought better results than other approaches on all measurements. In particular, the accuracy of APT attack prediction results (Precision) reached from 84 to 91%, higher than other studies of over 7%. These experimental results have proven that the proposed BiADG model for detecting APT attacks in this study is proper and reasonable. In addition, those experimental results have not only proven the effectiveness and superiority of the proposed method in detecting APT attacks but have also opened up a new approach for other cyber-attack detections such as distributed denial of service, botnets, malware, phishing, etc.

An optimized multi-layer ensemble model for airborne networks intrusion detection

In view of the characteristics of airborne networks, such as the traffic data of 1553B data bus and public networks, numerous redundant and irrelevant data, and fewer but more types of attack behaviors, a highly representative balanced data subset is generated by combining K-means with synthetic minority oversampling technique (SMOTE). A feature selection (FS) method of fast correlation-based filter combined with information gain (IG-FCBF) is proposed to filter the key characteristics with 90 % cumulative importance. Aiming at the problem that it is not easy for a single classifier to accurately classify various types of attacks, as well as the difficulty in obtaining the best prediction results by adjusting default and manual parameters, a multi-layer ensemble model based on Bayesian optimization tree-structured Parzen estimator (BO-TPE) is proposed for airborne networks intrusion detection, combining the advantages of supervised learning, stacking ensemble method and hyperparameter optimization (HPO). The experimental results show the superiority and effectiveness of the model, as well as its universality in Integrated Avionics Systems (IAS) and onboard public networks, which provides a new approach for airborne networks intrusion identification and protection.

Corrosion resistance of multicomponent disilicate (Ho0.2Er0.2Tm0.2Yb0.2Lu0.2)2Si2O7 against CMAS and volcanic ash

With the increasing operating temperature of gas turbine engines, calcium-magnesium-aluminosilicate (CMAS) poses a serious threat on environmental barrier coatings (EBCs) applied on hot-sections of aero-engines. Here, we have synthesized a novel multicomponent disilicate—(Ho0.2Er0.2Tm0.2Yb0.2Lu0.2)2Si2O7 (brief to (5RE0.2)2Si2O7), and comparatively studied its performance in the presence of synthesized CMAS and natural volcanic ash at 1400ºC. In comparison with Yb2Si2O7, (5RE0.2)2Si2O7 has a shorter Si-O bond length and a larger RE-O bond length because of the larger average RE3+ radius. After CMAS corrosion, some apatite grains precipitate at the CMAS/(5RE0.2)2Si2O7 interface to develop a loose reaction layer, exhibiting a higher corrosion resistance than Yb2Si2O7. Meanwhile, the consumption of CaO and release of SiO2 during the chemical reaction process increase the viscosity of CMAS to some extent and thus weaken its infiltration propensity. For the volcanic ash case, it directly infiltrates into the interior of (5RE0.2)2Si2O7 along grain boundaries without any reaction due to the relatively low CaO content, exhibiting a more serious attacking behavior. In addition, (5RE0.2)2Si2O7 effectively increases the contact angle of molten volcanic ash due to its lower surface energy. These finds here provide a better understanding for the design and application of next-generation EBC material.

TTPXHunter: Actionable Threat Intelligence Extraction as TTPs from Finished Cyber Threat Reports

Understanding the modus operandi of adversaries aids organizations to employ efficient defensive strategies and share intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and convert it into a structured format. This research introduces a methodology named TTPXHunter for automated extraction of threat intelligence in terms of Tactics, Techniques, and Procedures (TTPs) from finished cyber threat reports. It leverages cyber domain-specific state-of-the-art natural language model to augment sentences for minority class TTPs and refine pinpointing the TTPs in threat analysis reports significantly. We create two datasets: an augmented sentence-TTP dataset of \(39,296\) sentence samples and a \(149\) real-world cyber threat intelligence report-to-TTP dataset. Further, we evaluate TTPXHunter on the augmented sentence and report datasets. The TTPXHunter achieves the highest performance of \(92.42\%\) f1-score on the augmented dataset, and it also outperforms existing state-of-the-art TTP extraction method by achieving an f1-score of \(97.09\%\) when evaluated over the report dataset. TTPXHunter significantly improves cybersecurity threat intelligence by offering quick, actionable insights into attacker behaviors. This advancement automates threat intelligence analysis and provides a crucial tool for cybersecurity professionals to combat cyber threats.

Efficient degradation of tetracycline by cobalt ferrite modified alkaline solution nanofibrous Ti3C2Tx MXene activated peroxymonosulfate system: Mechanism analysis and pathway

Magnetic CoFe2O4 nanoparticles have received considerable attention as an activator for peroxymonosulfate (PMS) in the degradation of tetracycline. However, it is still challenging to construct CoFe2O4 composites with good dispersion and highly exposed reactive sites. Herein, the KOH fiber-functionalized MXene matrix decorated by cobalt ferrite nanoparticles (denoted as CoFe2O4@Alk-MXene) was developed by electrostatic self-assembly method. The layered fibrotic structure effectively disperses and fixes cobalt ferrite nanoparticles, increasing the exposure of reactive sites. The as-prepared CoFe2O4@Alk-MXene material exhibited rapid removal of 100% tetracycline hydrochloride (TC-HCl) within 10 minutes by activating PMS, leveraging the excellent conductivity of alkalized MXene and the efficient activation of transition metal atoms. The experimental and theoretical analysis revealed that the electron transfer process of Ti2+/Ti4+, Co2+/Co3+ and Fe3+/Fe2+, as well as the free radical and non-free radical attack behaviors produced by the adsorption of PMS by the composite, are the primary mechanisms for achieving rapid removal of pollutants. A comprehensive degradation mechanism and potential pathways were proposed based on these findings. Overall, CoFe2O4@Alk-MXene/PMS emerges as a promising catalytic system for TC removal.

Sex-specific manipulation of sexually cannibalistic mantid mating behavior by hairworms

Abstract Changes in the morphology, physiology, and behavior of parasitized organisms provide an ideal opportunity to examine the extended phenotype of parasites. Because the quality of the host directly affects the fitness of the parasite, parasites may increase their fitness by manipulating phenotypes of low-quality hosts. Males are usually preyed on by females in sexual cannibalism. Thus, the males of sexually cannibalistic species are unsafe and low-quality hosts for parasites, whereas females may be beneficial hosts because of the chance of nutrient intake from cannibalized males. Under passive modes of transmission, parasites cannot choose the host sex. Such parasites exploiting sexually cannibalistic organisms are subjected to contrasting fitness effects and may evolve to manipulate host mating behavior in a sex-specific manner: decreasing male mating to avoid cannibalism and promoting female mating to engage in cannibalism. We examined this hypothesis by a behavioral experiment using a mantid–hairworm system. Parasitized male mantids (Tenodera angustipennis) changed their behavior as expected, exhibiting increased escapes and decreased courtships and mountings, potentially avoiding encounters with the female. Interestingly, male attack behavior was promoted, possibly decreasing contact with the encountered female. However, contrary to our prediction, parasitized females also exhibited decreased propensities of mating, suggesting costs or little benefits of host mating for parasites in the female host. This study provides novel insights into the evolution of parasite strategies in response to sexual differences in host quality.

Enhancing robustness of community structure in networks against attacks with gray information

In recent years, network science has received significant attention and application. As an important mesoscale structure of networks, community structure reveals the inherent modular structure and potential functionality of networks. Therefore, maintaining the community structure during attacks is crucial. Existing studies on community robustness primarily focus on two attack behaviors: malicious attacks and random failures. However, little is known on the community robustness in the more practical situations where the attackers have limited ability to access precise network information, leading to gray information attack behaviors. Hence, in this paper, we investigate the robustness of network communities under gray information attacks and the enhancing algorithms. Following the community robustness measure for the sequential attacks, we establish a unified evaluation framework for attacks with gray information by introducing a gray attack coefficient, which treats the usual random failures and malicious attacks as the two special situations in the proposed framework. Several enhancing algorithms with local search strategy are exquisitely devised. The efficacy of our framework and algorithms which significantly improve the community robustness against gray attacks is demonstrated by the experimental results on a variety of real-world networks. Our findings have important implications not only in enhancing the community robustness of existing networks but also in designing robust ones from the scratch, which paves the way to further understand and seek strategies and solutions for network community robustness with gray information.

Decoupling of Phase and Amplitude Channels with a Terahertz Metasurface Toward High‐Security Image Hiding

AbstractSteganography technology which conceals a message in a carrier to make it invisible is critical for information security. Conventional optical image steganography using diffractive optical components or spatial light modulators suffers from less encoding channel and bulky volume. The emergence of multifunctional metasurface that can manipulate abundant physical dimensions of optical fields allows the multi‐channel image steganography in a compact volume. Here, the image hiding in a metasurface is demonstrated by modulating the amplitude, phase, and polarization states of terahertz (THz) waves completely. Especially, the phase channel can decouple from the amplitude channel based on a Fresnel‐diffraction‐based algorithm. By directly measuring the phase distribution using the homemade THz focal plane imaging system, the number of transmission channels can expand from N to 2N. As a proof of concept, it is shown that the secret images can encode in the phase channel and subsequently extract by using different keys, such as polarization states, detection distances, and its combinations. Moreover, different hiding strategies with different attacker behaviors are also demonstrated. The decoupling of the phase and amplitude channels with a single metasurface may open an avenue toward innovative optoelectronic devices for optical image steganography, data storage, and terahertz communication.

The dorsal raphe-to-ventral hippocampal projection modulates reactive aggression through 5-HT1B receptors

Maladaptive reactive aggression is a core symptom of neuropsychiatric disorders such as schizophrenia. While uncontrolled aggression dampens societal safety, there is a limited understanding of the neural regulation involved in reactive aggression and its treatment. High levels of aggression have been linked to low serotonin (5-HT) levels. Additionally, post-weaning socially isolated (SI) mice exhibit outbursts of aggression following encountering acute stress, and hyperactivated ventral hippocampus (vHip) involves this stress-provoked escalated aggression. Here, we investigated the potential role of the raphe nucleus projecting to the vHip in modulating aggressive behavior. Chemogenetically activating the dorsal raphe nucleus (DRN) soma projecting the vHip or DRN nerve terminals in the vHip reduced reactive aggression. The reduction of attack behavior was abolished by the pretreatment of 5-HT1B receptor antagonist SB-224289. However, activating the median raphe nucleus (MRN)-to-vHip pathway ameliorated depression-like behavior but did not affect reactive aggression. DRN→vHip activation suppressed the vHip downstream area, the ventromedial hypothalamus (VMH), which is a core aggression area. Intra-vHip infusion of 5-HT1B receptor agonists (anpirtoline, CP-93129) suppressed reactive aggression and decreased c-Fos levels in the vHip neurons projecting to the VMH, suggesting an inhibition mechanism. Our findings indicate that activating the DRN projecting to the vHip is sufficient to inhibit reactive aggression in a 5-HT1B receptor-dependent manner. Thus, targeting 5-HT1B receptor could serve as a promising therapeutic approach to ameliorate symptoms of reactive aggression.

Optimal control for linear cyber physical systems under replay attacks via time delay estimation approach

We mainly study the optimal control problem for linear cyber physical systems under replay attacks in this paper. The methodological research mainly relies on the following two starting points: (1) Promptly detect and estimate the malicious replay attack behaviours hampering normal system procedures; (2) Design a resilient optimal control strategy to ensure the system regularly works as much as possible. Specifically, the linear cyber physical system becomes a time delay system after the replay attack, and then an algorithm is designed to transform the system into a delay-free system and ensure its stability. Finally, a numerical simulation is employed to demonstrate the practicality and efficacy of the proposed approach.