Existing research on moving target defense (MTD) decision-making methods predominantly relies on game models under the assumption of complete rationality, failing to adequately account for the bounded rationality of attackers and defenders. Additionally, traditional MTD strategies face issues of rigidity and resource inefficiency in dynamic environments, rendering them ineffective against continuously evolving adversarial scenarios. To address these limitations, this paper proposes a dynamically self-adaptive decision-making method based on supermodular games under bounded rationality. First, the adversarial process is modeled using a supermodular game framework that incorporates prospect theory to capture bounded rational behavior. Furthermore, a constrained payoff function that balances MTD security, performance, and affordability is designed. Second, an innovative Prospect-Theoretic Multi-Agent Advantage Actor-Critic (PT-MAA2C) algorithm is proposed to solve the game, leveraging its adaptive and efficient strategy exploration and exploitation capabilities to derive optimal equilibrium solutions. Finally, through experiments simulating MTD methods against DDoS attacks in a network topology deception scenario, the proposed method is demonstrated with significantly enhanced defense efficiency, system robustness, and resource optimization, offering both theoretical and practical value.

Inferring protocol state machines from observable information presents a significant challenge in protocol reverse engineering (PRE), especially when passively collected traffic suffers from message loss, resulting in an incomplete protocol state space. This paper introduces an innovative method for actively inferring protocol state machines using the minimally adequate teacher (MAT) framework. By incorporating session completion and deterministic mutation techniques, this method broadens the range of protocol messages, thereby constructing a more comprehensive input space for the protocol state machine from an incomplete message domain. Additionally, the efficiency of active inference is improved through several optimizations for the LM+ algorithm, including traffic deduplication, the construction of an expanded prefix tree acceptor (EPTA), query optimization based on responses, and random counterexample generation. Experiments on the real-time streaming protocol (RTSP) and simple mail transfer protocol (SMTP), which use Live555 and Exim implementations across multiple versions, demonstrate that this method yields more comprehensive protocol state machines with enhanced execution efficiency. Compared to the LM+ algorithm implemented by AALpy, Act_Infer achieves an average reduction of approximately 40.7% in execution time and significantly reduces the number of connections and interactions by approximately 28.6% and 46.6%, respectively.

Moving Target Defense (MTD) necessitates scientifically effective decision-making methodologies for defensive technology implementation. While most MTD decision studies focus on accurately identifying optimal strategies, the issue of optimal defense timing remains underexplored. Current default approaches-periodic or overly frequent MTD triggers-lead to suboptimal trade-offs among system security, performance, and cost. The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead, yet existing frameworks inadequately address this temporal dimension. To bridge this gap, this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces, thereby capturing the dynamic security state evolution of MTD systems. We introduce a belief factor to quantify information asymmetry during adversarial interactions, enhancing the precision of MTD trigger timing. Leveraging this game-theoretic foundation, we employ Multi-Agent Reinforcement Learning (MARL) to derive adaptive temporal strategies, optimized via a novel four-dimensional reward function that holistically balances security, performance, cost, and timing. Experimental validation using IP address mutation against scanning attacks demonstrates stable strategy convergence and accelerated defense response, significantly improving cybersecurity affordability and effectiveness.

Most of the existing protocol fuzzing work follows a specific predefined probability distributionssss to select mutation operators, resulting in inefficient operators being selected frequently during scheduling, unable to generate enough valuable test cases, and greatly reducing the efficiency of fuzzing. In order to solve the static predefined probability distribution of the mutation operator, this paper proposes a hybrid FA-PSO algorithm, which can improve the speed of global exploration in the early stage, and can avoid falling into local optimization. In different test stages, the weight value of each operator is adjusted adaptively, so as to dynamically adjust the scheduling probability of the mutation operator. The method is evaluated on ProFuzzBench. The experimental results show that the proposed method improves the code coverage by 3.29% and the number of crashes triggered by 10.32% compared with AFLNet.

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.

SignificanceNetwork security vulnerabilities arising from flaws in network protocol implementations cause substantial losses and adverse societal effects, exemplified by the Heartbleed vulnerability in OpenSSL (CVE-2014-0160). This flaw allows attackers to extract encryption keys from public servers, enabling decryption of traffic or unauthorized authentication on less secure systems. Approximately 500,000 internet servers are affected when the vulnerability is publicly disclosed. Against this backdrop of escalating network security risks, ensuring the security of network protocol software becomes a critical research area. Fuzzing, a software testing technique, emerges as one of the most widely used approaches for identifying vulnerabilities in network protocol implementations due to its ease of deployment and high efficiency. The core concept of fuzzing is to improve software security by generating and sending crafted test cases to the target program. Despite significant progress in this field, Network Protocol Fuzzing (NPF) still faces technical challenges. Currently, no systematic and up-to-date review of NPF research exists, limiting researchers’ ability to grasp recent advances. This paper conducts a comprehensive review of NPF techniques, aiming to provide researchers in the field of network protocol security with valuable references for tool selection and optimization.ProgressSince the proposal of AFLNET in 2020, considerable progress occurs in the field of NPF, addressing key challenges such as protocol packet construction, protocol state awareness, and network communication efficiency optimization. Specifically, for protocol packet construction, researchers propose machine learning-based packet generation methods that integrate both generation and mutation strategies. Mutation operators and guidance techniques are designed to target specific protocol characteristics. In terms of protocol state awareness, state tracking capabilities are enhanced through state variable capture and state model construction. Furthermore, state fuzzing has been widely employed to detect state machine bugs. For protocol stack efficiency optimization, researchers improve testing efficiency by refining communication mechanisms and applying snapshot techniques.ConclusionsTo comprehensively summarize the research progress in NPF, this paper first clarifies the unique characteristics of network protocol software compared with other fuzzing targets. These characteristics include strict protocol message formats, asynchronous network interactions, and complex protocol state maintenance. A problem-oriented classification framework for NPF is proposed, structured around three core challenges: protocol packet construction, protocol state awareness, and protocol stack efficiency optimization. Based on this framework, research advancements in NPF over the past eight years are systematically reviewed, with a technical analysis of the capabilities, and limitations of existing approaches. This review highlights several key challenges in the field. For input construction, major limitations include weak validity of generated inputs, input space explosion, and restrictions imposed by encryption and authentication mechanisms. In terms of state awareness, the field faces insufficient protocol state space exploration and low levels of test intelligence and automation. Regarding performance optimization, technical challenges include slow network communication speed, limited scalability across different protocol implementations, and inadequate adaptability to complex network environments. This study provides both theoretical foundations and practical references to guide future research and technological development in the NPF domain.ProspectsFuture research in NPF can integrate emerging technologies such as Artificial Intelligence (AI) to enhance the intelligence and automation of testing processes. For example, combining AI methods with program analysis techniques may enable a deeper understanding of protocol behavior and more efficient generation of test packets. Developing state representations tailored to different protocol characteristics, implementing real-time protocol state mapping, and applying state prediction and reasoning based on LLM can further improve the efficiency and applicability of NPF tools. In addition, introducing technologies such as parallelization, distributed computing, modular test architectures, and integrated network simulation with virtualization can significantly enhance testing scalability and adaptability. Through the integration of emerging technologies and interdisciplinary research, NPF is expected to play an increasingly critical role in network protocol security, providing robust technical support for building secure and reliable network environments.

Enhancing the effectiveness of cybersecurity defense requires not only advanced and practical defensive technologies but also relies on effective decision-making methods. In light of the complex and variable process of cyber offense and defense, the accurate, adaptive and effective selection of optimal strategies is a hot and challenging issue in the current research field of Moving Target Defense (MTD). We believe that existing MTD decision-making methods have significant deficiencies in accurately simulating the network environment, dynamically adapting to environmental changes, and balancing security, performance, and cost. Therefore, we propose a dynamic defense method that combines Markov game theory and improved Multi-Agent Reinforcement Learning (MARL). Firstly, we construct a Markov game model under complete information conditions to abstract the process of cyber offense and defense, considering the strategies of attackers and defenders and their corresponding state transitions to better fit the actual offensive and defensive game model. Secondly, we introduce three-dimensional indicators of security, performance, and affordability to construct the reward function of multi-agent reinforcement learning, thereby multidimensional quantifying the various effects of network defense strategies. On this basis, we design a reinforcement learning algorithm based on the multi-agent Q-learning network, which calculates the optimal network defense strategy measured by these three-dimensional indicators through the learning interaction process and introduces the concept of Probe-with-Penalty to ensure the stability of strategy updates and to achieve effective exploration and rapid convergence of the strategy space. Finally, we verify the effectiveness of the proposed model and algorithm through application examples and demonstrate the performance of the algorithm through result analysis. The study shows that the proposed model and algorithm can effectively improve the adaptability and efficiency of network defense, providing a new method for optimizing network defense strategies.

Provenance graph-based approaches have become a promising solution for detecting advanced persistent threats (APTs). Particularly, analysts can model prior knowledge of acknowledged campaign as query graphs and extract potential APT behaviors through inexact graph matching. However, existing works are shown to be limited for ignoring the importance of extracting cross-graph semantic correlation information between provenance and query graphs, as well as aligning the information level of them. Thus, in this paper, we propose a novel and thorough pipeline for APT detection, including automatic query graph construction, fine-granularity detection through graph matching, and attack behavior reconstruction. Compared to existing works, our method reduces the manual involvement in query graph construction and can detect attack techniques used by attackers. Our experimental results demonstrate that our method can outperform state-of-the-arts by a wide margin.

Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors’ preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.

Protocol reverse engineering is crucial in normative verification, and malware behavior analysis and vulnerability discovery. However, uncovering the structural features of binary protocols concealed within dense data representations remains a significant challenge. Accurately identifying keyword segments associated with message types is a prerequisite for meaningful semantic analysis and protocol state machine reduction. In this work, we introduce a novel approach for inferring keywords from binary protocols based on probabilistic statistics. Our method in terms of Byte employs heuristic rules to filter offset positions that are clearly unrelated to message types. We further filter candidate Byte-offsets utilizing constraint relations and provide the probabilistic ranking of each offset as the keyword segment. To enhance the reliability of keyword segment inference, we utilize the Monte Carlo algorithm to assess the difference between message clustering with candidate Byte-offset and random message clustering, and reorder candidate offsets according to the results. Then we can observe optimal values from both orderings and present the ultimate inference results. Experimental results demonstrate that our method excels in the accuracy of keyword segments identification compared with previous techniques.

Due to the limitations of the existing annotation methods, the prevalence of label noise can be caused in realistic malicious traffic datasets, which has a significant impact on the training and evaluation of deep learning-based intrusion detection models. Recently, various methods have been proposed to deal with noise-containing labeled datasets, and they can be roughly divided into two categories: data cleaning and robust training. However, the different processing ideas lead these two types of methods to ignore the information in different components of the dataset, resulting in a cliff-like drop in performance under high noise conditions. To this end, this study proposes a unified framework for handling noise malicious traffic based on the multidimensional constrained representations named MCRe, which unifies data cleaning and robust training into an ideal representation function approximation. According to the properties of the ideal representation function, information integrity constraints, cluster separability constraints and core proximity constraints are defined to drive MCRe to approximate the ideal representation during iteration. These constraints led MCRe to learn the individual, intra-class, and global levels of distributed knowledge, thus avoiding irrational domain knowledge extraction and ensuring strong label noise robustness of the representation network. We validated MCRe on a dataset that includes 22 types of realistic malicious traffic. Experimental results show that MCRe can outperform the state-of-the-art methods in both data cleaning and robust training downstream tasks, achieving 85% pure sample rate and 82% classification accuracy even under the condition of up to 90% noise labels. In addition, the generalizability of MCRe was verified on several public datasets. Finally, MCRe was also well-extended to enhance other data cleaning and robust training approaches.

Protocol Reverse Engineering (PRE) is of great practical importance in Internet security-related fields such as intrusion detection, vulnerability mining, and protocol fuzzing. For unknown binary protocols having fixed-length fields, and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance. Hence, this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty. Specifically, we design four related constructions between entropy changes and protocol field segmentation, introduce random variables, and construct joint probability distributions with traffic sample observations. Probabilistic inference is then performed to identify the possible protocol segmentation points. Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision.In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets.However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality.Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantly difficult.To address this, we propose a universal packet-level encrypted traffic identification method, Combo Packet.This method utilizes convolutional neural networks to extract deep features of the current packet and its contextual information and employs spatial and channel attention mechanisms to select and locate effective features.Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic service categories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g., BitTorrent and Skype).Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0% and 97.1% for service and application categories, respectively.It also provides shorter training times and higher recognition speeds.The performance and recognition capabilities of Combo Packet are significantly superior to the existing classification methods mentioned.

Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user's browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target model, which is unrealistic. This paper proposes CMAES-WFD, a black-box WF defense based on adversarial samples. The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES) optimization algorithm. Perturbations are injected into the local parts of the original traffic to control bandwidth overhead. According to the experiment results, CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting (DF) and VarCnn to below 8.3% and the bandwidth overhead to a maximum of only 14.6% and 20.5%, respectively. Specially, for Automated Website Fingerprinting (AWF) with simple structure, CMAES-WFD reduced the classification accuracy to only 6.7% and the bandwidth overhead to less than 7.4%. Moreover, it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

Protocol state machine active inference is a technique that relies on active automata learning.However, the abstraction of the alphabet and the construction of the mapper present critical challenges.Due to the diversity of messages of the same type, the response types of the same type are different, causing the method of regarding the message types as the alphabet will result in the loss of states or state transitions.To address the issue, message types were refined into subtypes according to the different responses and a progressive active inference method was proposed.The proposed method extracted the state fields from the existing protocol data to construct the initial alphabet and the mapper, and obtained the initial state machine based on active automata learning.It then mutated the existing messages to explore the response sequences, which were inconsistent with the current state machine.The mutated message was regarded as a protocol subtype and added to the alphabet, and a new state machine was inferred progressively based on the new alphabet.In order to reduce the interactions, a pre-response query algorithm was proposed based on prefix matching for the caching mechanism in the active automata learning.The ProLearner tool was utilized to evaluate the proposed method in the context of the SMTP and RSTP protocols.It is verified that the pre-response query method can effectively reduce the number of actual interactions, with an average reduction rate of about 10%.

2022年9月13日,国务院学位委员会、教育部印发《研究生教育学科专业目录(2022年)》,在交叉学科门类下新设密码硕士专业学位类别.这是继2020年我国新增"密码科学与技术"本科专业后,在密码专业人才培养上又迈进的一大步,正式步入体系化培养密码高层次人才的轨道,对构建密码专业人才队伍具有重要意义.作为高校,瞄准国家密码战略人才的需求,培养一流的密码人才,建设一流的密码学科,职责所系任重道远.

1 Introduction The success of a deep learning-based network intrusion detection systems(NIDS)relies on large-scale,labeled,realistic traffic[1,2].However,automated labeling of realistic traffic,such as by sandbox and rule-based approaches,is prone to errors[3],which in turn affects deep learning-based NIDS.

In recent years, a growing number of breaches targeting cryptocurrency exchanges have damaged the credibility of the entire cryptocurrency ecosystem. To prevent further harm, it’s crucial to detect the anomalous behaviors hidden within cryptocurrency transactions and offer predictive suggestions. However, details of transaction records must be carefully analyzed for effective detection, and this information could be exploited by adversaries to launch attacks such as de-anonymization and model interference. As a result, it is essential to prioritize privacy preservation when designing an anomaly detection system for cryptocurrency transactions. In this paper, we propose a privacy-preserving anomaly detection (PPad) scheme for cryptocurrency transactions based on a decision tree model, which achieves privacy preservation by using additively homomorphic encryption and matrix perturbation techniques. We also design and implement PPad’s underlying protocol in a cloud outsourcing environment. The correctness and privacy properties of PPad have been proven through detailed analysis. Experimental results show that our scheme can offer privacy assurance with desirable detection effectiveness and efficiency, making it suitable for real-world applications.

Represented by reactive security defense mechanisms, cyber defense possesses a static, reactive, and deterministic nature, with overwhelmingly high costs to defend against ever-changing attackers. To change this situation, researchers have proposed moving target defense (MTD), which introduces the concept of an attack surface to define cyber defense in a brand-new manner, aiming to provide a dynamic, continuous, and proactive defense mechanism. With the increasing use of machine learning in networking, researchers have discovered that MTD techniques based on machine learning can provide omni-bearing defense capabilities and reduce defense costs at multiple levels. However, research in this area remains incomplete and fragmented, and significant progress is yet to be made in constructing a defense mechanism that is both robust and available. Therefore, we conducted a comprehensive survey on MTD research, summarizing the background, design mechanisms, and shortcomings of MTD, as well as relevant features of intelligent MTD that are designed to overcome these limitations. We aim to provide researchers seeking the future development of MTD with insight into building an intelligently affordable, optimized, and self-adaptive defense mechanism.