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This article aims to elucidate the influence of the Chinese elite’s geographic perception on shaping geopolitical codes and its impact on the foreign policy of the People’s Republic of China (PRC). For this purpose, the study focuses on the role of internal and external geopolitical factors in the configuration of Chinese leaders’ spatial vision. Internal geopolitical factors encompass China’s involvement in the global economy, its geographic characteristics, and the geographic distribution of its population. External geopolitical factors include the regional and global distribution of power and China’s territorial disputes with neighboring countries. Regarding methodology, the study draws on a wide range of sources, including geographic and economic statistical data, Chinese leaders’ public statements, PRC’s decision-making bodies documents, and academic studies and reports. The primary findings suggest that geopolitical factors have constrained the strategic choices available to the Chinese elite and favored a seaward-oriented foreign policy, which have been reflected in China’s geopolitical codes. In this context, the China Seas, and to a lesser extent the Indian Ocean, assume pivotal roles in the PRC’s efforts to project power and expand its influence. [ABSTRACT FROM AUTHOR]

Starting with the actual operational requirements of active defense, this paper analyzes the advantages, disadvantages, and usage scenarios of different trajectory prediction methods, also analyzes the differences between the active defense combat scenarios of air platforms and other aircraft trajectory prediction scenarios that are currently mainly studied. A state estimation scheme based on extended Kalman filtering is proposed for the special attack intent and motion law of the trajectory prediction object in this scenario. It simulates the process of the attack missile guided with proportional guidance law attacking aircraft based on simulation software, to establish an observation model under conditions of constant proportional guidance coefficient, and performs linearization simulation using Kalman filter nonlinear model. The motion states of the attack missile at different times are observed and the short-term trajectory prediction is performed. The simulation results show that the model can significantly reduce state estimation errors in the active defense combat scenario.

The harm caused by deepfake face images is increasing. To proactively defend against this threat, this paper innovatively proposes a destructive active defense algorithm for deepfake face images (DADFI). This algorithm adds slight perturbations to the original face images to generate adversarial samples. These perturbations are imperceptible to the human eye but cause significant distortions in the outputs of mainstream deepfake models. Firstly, the algorithm generates adversarial samples that maintain high visual fidelity and authenticity. Secondly, in a black-box scenario, the adversarial samples are used to attack deepfake models to enhance their offensive capabilities. Finally, destructive attack experiments were conducted on the mainstream face datasets CASIA-FaceV5 and CelebA. The results demonstrate that the proposed DADFI algorithm not only improves the generation speed of adversarial samples but also increases the success rate of active defense. This achievement can effectively reduce the harm caused by deepfake face images.

Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network's residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network's resilience against malicious attacks. [ABSTRACT FROM AUTHOR]

As an active defense technology, cyber mimic defense uses heterogeneous redundancy and dynamic feedback mechanism to constantly adjust the execution envinument of defense system to resist attacks. However, in the face of diverse at tack methods of hackers, it is unsafe to resist attacks by only relying on cyber mimic defense. In order to enhance the security defense capability of the system, this paper proposed a more reasonable defense selection method based on the existing defense system. It applied evolutionary game theory of bounded rationality to cyber mimic defense, constructed a three-party evolutionary game model consisting of attackers, defenders and legitimate users, and proposed a solution method for the optimal defense strat- egy. This game model used the replication dynamic equation to obtain evolutionary stable strategy. The simulation results show that the system can reduce the loss and restrain the attack behavior by implementing the evolutionary stable strategy of reasoning, which has certain reference significance for the selection of defense strategy and security enhancement in cyber minie defense [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to software and hardware differentiation, the problem of false positives mistakenly identified as network attack behavior caused by inconsistent mimic decision results frequently occurs. Therefore, a mimic decision method based on deep learning was proposed. By constructing an unsupervised autoencoder-decoder deep learning model, the deep semantic features of diverse normal response data were explored from different executions and its statistical rules were analyzed and summarized. Additionally, the offline learning-online decision-making mechanism and the feedback optimization mechanism were designed to solve false positive problem, thereby accurately detecting network attacks and improving target system security resilience. Since statistical rules of normal response data was understood and mastered by deep learning model, the mimic decision results among different executions could remain consistent, indicating that the target system was in a secure state. However, once the target system was subjected to a network attacks, the response data outputted by the different executions was deviated from statistical distribution of deep learning model. Therefore, inconsistent mimic decision results were presented, indicating that the affected execution was under attack and the target system was exposed to potential security threats. The experiments show that the performance of the proposed method is significantly superior to the popular mimic decision methods, and the average prediction accuracy is improved by 14.89%, which is conducive to integrating the method into the mimic transformation of real application to enhance the system's defensive capability. [ABSTRACT FROM AUTHOR]

The rapid development of network attack and defense technologies has posed various challenges to current cybersecurity assurance systems. Therefore, studying a new cybersecurity assurance system has become an urgent need to promote the development of information technologies and is of strategic significance for strengthening the network security and availability in China. This study summarizes the operation status of and major security challenges faced by China’s current cybersecurity guarantee system that features a self-defense mode. A cybersecurity guarantee system based on a guard mode and its key technical tasks are proposed. Specifically, the tasks include honey point technology based on deep threat perception, honey court technology based on attack observation and discrimination, honey matrix technology based on collaborative linkage, and honey hole technology based on attack deterrence and mapping. Furthermore, we propose the following suggestions: (1) exploring the cybersecurity assurance mechanisms based on the guard mode to comprehensively improving the cybersecurity protection level of China; (2) exploring the research and application of security protection technologies based on the guard mode and achieving the integration of existing and new security protection technologies; (3) exploring a new talent-training model to cultivate innovative and practical professionals in the cybersecurity field.

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. • The method identifies backdoor-poisoned samples in multidomain datasets. • The method presents an MI generation module that does not depend on clean datasets. • ALL-to-ALL decoupling training to decouple association between features and labels. • Extensive experiments demonstrate our effectiveness across multidomain datasets. [ABSTRACT FROM AUTHOR]

The existing active defense technology based on game theory is mainly based on static games. This method can not adapt to the dynamic change of the attacker’s attacking intent and tactics. Based on the non-cooperative and non-zero-sum dynamic game theory, a complete Bayesian system of emotional game bodies is established. Use the “virtual node” to transform the attack and defense chart into a tree structure of the attack and defense game. An attack and defense strategy suitable for integrity and incompleteness is given. The experimental simulation results show that the seven comprehensibilities and usability of Nash equilibrium solutions optimized by Bayesian subgames are well obtained

Abstract 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 unreliable 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.

One of the most common cyber-attacks that users encounter on the internet are phishing websites. In the attacks that are performed on phishing websites, real websites are duplicated and published on different domain names, and users are directed to these fake websites through various social engineering techniques. Through to the website to which users are directed, they transmit some personal and confidential data such as credit card, username-password details to attackers. In this study, the establishment of the infrastructure and content of phishing internet sites has been explained, a tool named PhisherHunter created, and four different methods have been developed so as to detect such websites. Through the examination of newly registered websites, which is the main detection method, a successful detection rate of 95.4% has been achieved. Three different methods have been used in the active defense part of the study. Firstly, the hosting company has been automatically determined to stop the publication of the phishing website and a notification has been sent with a success rate of 98%. As the second active defense method, the active honeypot technique has been developed. The active honeypot method aims to enter a marked information on the phishing website and to track this information on the real website. And as the last active defense method, the method of poisoning phishing websites by using fake data has been developed. It has been observed that poisoning methods by using the techniques of active honeypot and fake data have achieved a success of 92%. [ABSTRACT FROM AUTHOR]

Air platform is the key equipment that affects the victory or defeat of modern war. In the high antagonistic environment of the future air combat, the "hard kill" active defense system becomes the key factor improving the survivability of air platforms. In order to study the key problems on the construction of air platform active defense system, the development status and current situation of foreign active defense system and equipments of air platforms are analyzed. According to the characteristics and operational requirements of the air platform active defense system, which must have all-direction situation awareness and attack capability, and achieve high-precision guidance and head-on interception under the fast rendezvous conditions, the key technologies of the air platform active defense system, such as high precision early warning, detection and tracking technology, all-direction attack technology, counter-parallel interception and guidance technology, interception and damage technology, and lightweight defense missile technology, are compared and analyzed. These studies can provide technical support for the research on active defense system and weapon equipment of air platform.

Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network’s residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network’s resilience against malicious attacks.

Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asymmetric disadvantage for the attacker by using deception and randomization techniques to create a dynamic attack surface. Moving Target Defense (MTD) typically relies on system randomization and diversification, while Cyber Deception is based on decoy nodes and fake systems to deceive attackers. However, current Moving Target Defense techniques are complex to manage and can introduce high overheads, while Cyber Deception nodes are easily recognized and avoided by adversaries. This paper presents DOLOS, a novel architecture that unifies Cyber Deception and Moving Target Defense approaches. DOLOS is motivated by the insight that deceptive techniques are much more powerful when integrated into production systems rather than deployed alongside them. DOLOS combines typical Moving Target Defense techniques, such as randomization, diversity, and redundancy, with cyber deception and seamlessly integrates them into production systems through multiple layers of isolation. We extensively evaluate DOLOS against a wide range of attackers, ranging from automated malware to professional penetration testers, and show that DOLOS is effective in slowing down attacks and protecting the integrity of production systems. We also provide valuable insights and considerations for the future development of MTD techniques based on our findings. [ABSTRACT FROM AUTHOR]

Cross-site scripting vulnerability (XSS) is one of the most frequently exploited and harmful vulnerabilities among web vulnerabilities. In recent years, many researchers have used different machine learning methods to detect network attacks, but these methods have not achieved high accuracy and recall rates and cannot effectively combat XSS attacks. Designing a model that can achieve high accuracy and truly proactive defense against reflected XSS vulnerabilities has become a top priority for maintaining user network security at this stage. In this paper, we propose a detection model for reflected XSS vulnerabilities based on the paths-attention method (PATS model). Firstly, the model converts vulnerability data into an intermediate representation of abstract syntax trees, then traverses the abstract syntax tree to generate multiple sets of syntactic paths, and then converts them into vector representations through word embedding matrices. The model extracts semantic features using attention mechanisms to improve training effectiveness by assigning appropriate weights to different sets of syntactic paths as it learns with neural networks, which realizes the transformation from passive defense to active defense. Additionally, in the dataset processing section, we point out the shortcomings of current research datasets and construct a reliable dataset composed of 1000 malicious samples from NIST and 10,000 benign samples from GitHub for experimentation purposes. Experimental results show that compared with other machine learning models, the paths-attention method can achieve an accuracy rate of 90.25% and F1-score of 81.62%, while reducing the training time by half to 30 h. [ABSTRACT FROM AUTHOR]

Doctrines are considered a key component of military power, shaping the ways in which armed forces organize and operate. This study critically examines the assumption that armed forces can change their practices by writing formal doctrine. The study addresses the research problem of why some formal doctrines are implemented and others are rejected. It does so by developing and testing a novel theoretical framework on doctrinal implementation through a comparative case study on rejection of the US Army 1976 Active Defense doctrine and successful implementation of the 1982 AirLand Battle doctrine. The study shows that contrary to popular beliefs, the actual concepts within a formal doctrine do not seem crucial for whether it is implemented or rejected. Rather, cultural coherence and inclusive creation seem crucial in this regard. [ABSTRACT FROM AUTHOR]

MVX is one of the most popular active defense technologies. Ideally, when not under attack, the MVX architecture provides normal program functions. Unfortunately, when a multi-threaded program runs in MVX architecture, because different program variants have inconsistent threads execution orders of shared resource operations, different variants will produce state inconsistency and trigger false attack alarm. This problem makes it difficult for multi-threaded programs to run in MVX architecture. This paper proposed a compiler-supported synchronous compilation model for shared resource operations in multi-threaded programs. This model took the shared resource operations as synchronization points, analyzed the timing and mode of shared resource operations in multi-threaded programs. The model ensured the consistency of multiple threads operating on shared resource in different variants at runtime, eliminating the false attack alarms. Based on LLVM 12. 0 compilation framework, this paper designed and implemented a prototype system based on the synchronous compilation model, and conducted simulation experiments on the prototype. The experimental results show that, processed by prototype system, multi-threaded programs have significantly lower false positive rates in MVX architecture. This indicates that,as a universal method, the synchronous compilation model can effectively eliminate the false attack alarms of multi-threaded programs running in MVX architecture, and improve the availability of MVX. [ABSTRACT FROM AUTHOR]

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 unreliable 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. [ABSTRACT FROM AUTHOR]

The power cyber–physical system (CPS) realizes the wide-area interconnection of new energy sources and multiple loads and the dynamic interaction of information and energy flows; however it faces increasingly severe security threats of false data injection attacks (FDIAs). In this context, the active defense methods against power CPS FDIAs from the multiple spatiotemporal perspective are summarized and analyzed in this paper. First, the architecture of a power CPS is abstracted and its multiple spatiotemporal coordination characteristic and FDIAs security risk are expounded upon. Then, the active defense system against FDIAs is conceived in the spatiotemporal coordinate system, and based on different spatiotemporal intersections, the six active defense methods and application examples – namely network deception, data encryption protection, vulnerability policy protection, false identification correction, traffic anomaly detection, and fault prevention control – are summarized. Finally, the limitations of existing active defense methods against FDIAs are discussed, and the linked defense methods with multiple spatiotemporal coordination are prospected.

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existence of cyber aggression is a constant and increasingly present threat to the computer systems used in contemporary life, requiring a sustained mobilization of the national and European legislators, especially in the context in which they become complementary to traditional armed conflicts. The latest conflict of this kind has brought back to the present more than ever the need to unify the legal framework on cyber attacks, by dismantling existing conventional regulations at European and international level, not enough appeal to the law of classical war. Equally, given the varied nature of the technologies used and the generally occult nature of such attacks, there is a need to strengthen the accountability of the states in whose territory they are launched, which is essential for an effective and timely response. The present study focuses on these needs, the conceptual and legal framework regarding the assimilation of cyber aggression to the armed forces themselves, but also useful practical ways in which states could counteract this type of attacks. [ABSTRACT FROM AUTHOR]

Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on the classical three-body adversarial research, the reinforcement learning algorithm is introduced to improve the purposefulness of the algorithm training. The action spaces the reward and punishment conditions of both attack and defense confrontation are considered in the reward function design. Through the analysis of the sign of the action space and design of the reward function in the adversarial form, the combat requirements can be satisfied in both the missile and target/defender training. Then, a curriculum-based deep reinforcement learning algorithm is applied to train the agents and a convergent game strategy is obtained. The simulation results show that the attack strategy of the missile can maneuver according to the battlefield situation and can successfully hit the target after avoiding the defender. The active defense strategy enables the less capable target/defender to achieve the effect similar to a network adversarial attack on the missile agent, shielding targets from attack against missiles with superior maneuverability on the battlefield. [ABSTRACT FROM AUTHOR]

Summary: DDoS attack as an attempt to make an online service inaccessible by suppressing it with massive traffic from various sources is a problem that is getting worse by the day, and when customers need to implement DDoS mitigation solutions in their network, they often have to make major changes to the existing network in terms of establishing new protocols or the need to completely redesign the existing network. Software evolution in networks under the name of software‐defined networks is developing and advancing in parallel with traditional networks, and the tendency and migration towards this generation of networks may be considered more and more seriously. Using SDN to encounter DDoS attacks, given its reactive capabilities, brings strategies such as dynamic cyber deception and active defense to minds, against attackers. In this article, we show how to obfuscate the network route to the destination by playing the address in the path and destination of the attacker's request, without understanding them from the heart of the issue by using the capabilities of software‐defined networks and lead DDoS attack flows, and kept the rest of network safe with least effect on topology from this destruction. [ABSTRACT FROM AUTHOR]

Cyber security systems generally have the phenomena of passive defense and low-efficiency early warnings. Aiming at the above problems, this study proposes a real-time warning and plans an AI defense strategy for a cyber security system aided by a security ontology. First, we design a security defense ontology that integrates attack graphs, general purpose and domain-specific knowledge bases, and on this basis, we (1) develop an ontology-driven method of early warnings of real-time attacks, which supports non-intrusive scanning attack detection and (2) combine artificial intelligence planning and bounded rationality to recommend and automatically execute defense strategies in conventional defense scenarios. A case study has been performed, and the results indicate that: (1) the proposed method can quickly analyze network traffic data for real-time warnings, (2) the proposed method is highly feasible and has the ability to implement defense strategies autonomously, and (3) the proposed method performs the best, with a 5.4–11.4% increase in defense effectiveness against the state-of-the-art counterparts considering the APT29 attack. Overall, the proposed method holds the potential to increase the defense effectiveness against cyberattacks under high computing resource constraints. [ABSTRACT FROM AUTHOR]

With the wide application of lithium‐ion battery in various fields, the security and reliability of lithium‐ion battery have attracted great attention. Under the mode of continuous development of Internet of vehicles technology, vehicles will be connected with each other in the future, and the hackers will attack the energy system of the vehicle. However, health assessment of lithium‐ion battery can timely grasp the running state and health of the power battery system, so as to realize active defense against hacker security attacks. This paper proposes a health assessment method for lithium‐ion batteries using incremental capacity analysis and weighted Kalman filter algorithm. In view of the problem that ordinary Kalman filtering algorithm produces poor filtering results when the actual measurement noise error is large, this paper proposes a weighted Kalman filtering algorithm based on ordinary Kalman filtering. Incremental capacity analysis was performed on the charge and discharge data of lithium‐ion batteries, and health characteristics were extracted to construct a Gaussian nonlinear feature association mapping model for the health characteristics of lithium‐ion batteries. Combined with the battery SOH double‐exponential decay model, the weighted Kalman filter algorithm was used to evaluate the health of lithium‐ion batteries. Four lithium‐ion battery data sets provided by NASA were used to simulate and verify the health assessment method proposed in this paper. The verification results show that the health assessment method based on weighted Kalman filter proposed in this paper has better assessment accuracy than the common Kalman filter method with an average percentage error of 0.61%. The average percentage error of the assessment results for different types of batteries was less than 0.9%. The health assessment method has high accuracy and is suitable for different types of batteries. [ABSTRACT FROM AUTHOR]

The terminal equipment interconnection and the network communication environment are complex in power cyber–physical systems (CPS), and the frequent interaction between the information and energy flows aggravates the risk of false data injection attacks (FDIAs) in the power grid. This paper proposes an active defense framework against FDIAs of power CPS based on data-driven algorithms in order to ensure that FDIAs can be efficiently detected and processed in real-time during power grid operation. First, the data transmission scenario and false data injection forms of power CPS were analyzed, and the FDIA mathematical model was expounded. Then, from a data-driven perspective, the algorithm improvement and process design were carried out for the three key links of data enhancement, attack detection, and data reconstruction. Finally, an active defense framework against FDIAs was proposed. The example analysis verified that the method proposed in this paper could effectively detect FDIAs and perform data reconstruction, providing a new idea for the active defense against FDIAs of power CPS. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The post-quantum security of electronic voting protocols such as lattice-based ones are based on the assumption of certain difficult computational problems, which cannot be solved by classical computers and have also not been solved by effective quantum algorithms until now. However, these voting protocols are still at risk of compromise with the development of quantum computing, and we call them passive defense voting protocols. By making use of the encrypted three-pass protocol configured by taking physical limits of quantum computing into account and the message authentication code with information theory security, we propose a new post-quantum voting (NPQV) protocol. The proposed protocol exhibits the following advantages: (1) The post-quantum security of NPQV protocol depends on the physical limits that are inherent to quantum computers, so NPQV remains secure with the development of quantum computing and thus we call it active defense voting protocol. NPQV is the first voting protocol with active defense capability. (2) NPQV protocol is suitable for voting with numerous candidates and voters and accommodates the situation in which multiple voters vote simultaneously. (3) NPQV satisfies all the seven security requirements for an electronic voting protocol, i.e. privacy, verifiability, completeness, robustness, eligibility, unreusability and fairness. Moreover, it remains secure under internal and external attacks. [ABSTRACT FROM AUTHOR]

Cross-site scripting vulnerability (XSS) is one of the most frequently exploited and harmful vulnerabilities among web vulnerabilities. In recent years, many researchers have used different machine learning methods to detect network attacks, but these methods have not achieved high accuracy and recall rates and cannot effectively combat XSS attacks. Designing a model that can achieve high accuracy and truly proactive defense against reflected XSS vulnerabilities has become a top priority for maintaining user network security at this stage. In this paper, we propose a detection model for reflected XSS vulnerabilities based on the paths-attention method (PATS model). Firstly, the model converts vulnerability data into an intermediate representation of abstract syntax trees, then traverses the abstract syntax tree to generate multiple sets of syntactic paths, and then converts them into vector representations through word embedding matrices. The model extracts semantic features using attention mechanisms to improve training effectiveness by assigning appropriate weights to different sets of syntactic paths as it learns with neural networks, which realizes the transformation from passive defense to active defense. Additionally, in the dataset processing section, we point out the shortcomings of current research datasets and construct a reliable dataset composed of 1000 malicious samples from NIST and 10,000 benign samples from GitHub for experimentation purposes. Experimental results show that compared with other machine learning models, the paths-attention method can achieve an accuracy rate of 90.25% and F1-score of 81.62%, while reducing the training time by half to 30 h.

Summary: With the development and popularization of Internet technology, network security has become the focus of attention. Vulnerabilities and back doors are regarded as two of the main reasons of network security problems. Dynamic heterogeneous redundant (DHR) architecture is a typical framework of cyberspace mimic defense. It can make use of untrusted hardware and software components to construct a high reliable and high security information system. In this article, a mathematical model is set up for the DHR architecture, and the system security is characterized by vulnerability consistency rate and success rate of system attack. The antiattack ability of the DHR system is analyzed using the mathematical model. [ABSTRACT FROM AUTHOR]

Aimed at the poor performance of guidance algorithms designed based on a linearized model in active defense under large leading angle deviation, both-way and one-way cooperative sliding mode guidance algorithms based on the smooth nonsingular terminal sliding mode for the defense missile are proposed. The relative kinematics and linearized models of the target, the active defense missile, and the attacking missile are established. In the design process, two smooth nonsingular terminal sliding mode surfaces are constructed based on zero-effort miss distance and zero-effort velocity, as well as their integral values. A tracking differentiator is introduced for excessive initial command deviation to meet the overload constraints of the active missile and the target. The sensitivity of guidance law to the estimated time-to-go error is reduced, and the target is allowed to perform an independent evasive maneuver. The effectiveness of the proposed guidance strategy is verified by numerical simulation and compared to the existing guidance strategies, the high accuracy, anti-chattering, and strong robustness of the proposed guidance algorithm are verified. [ABSTRACT FROM AUTHOR]

Due to the tight coupling between the cyber and physical sides of a cyber-physical power system (CPPS), the safe and reliable operation of CPPSs is being increasingly impacted by cyber security. This situation poses a challenge to traditional security defense systems, which considers the threat from only one side, i.e., cyber or physical. To cope with cyber-attacks, this paper reaches beyond the traditional one-side security defense systems and proposes the concept of cyber-physical coordinated situation awareness and active defense to improve the ability of CPPSs. An example of a regional frequency control system is used to show the validness and potential of this concept. Then, the research framework is presented for studying and implementing this concept. Finally, key technologies for cyber-physical coordinated situation awareness and active defense against cyber-attacks are introduced.

Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on the classical three-body adversarial research, the reinforcement learning algorithm is introduced to improve the purposefulness of the algorithm training. The action spaces the reward and punishment conditions of both attack and defense confrontation are considered in the reward function design. Through the analysis of the sign of the action space and design of the reward function in the adversarial form, the combat requirements can be satisfied in both the missile and target/defender training. Then, a curriculum-based deep reinforcement learning algorithm is applied to train the agents and a convergent game strategy is obtained. The simulation results show that the attack strategy of the missile can maneuver according to the battlefield situation and can successfully hit the target after avoiding the defender. The active defense strategy enables the less capable target/defender to achieve the effect similar to a network adversarial attack on the missile agent, shielding targets from attack against missiles with superior maneuverability on the battlefield.