Advanced Persistent Threat Attacks Research Articles

Missing Data Imputation Based on Causal Inference to Enhance Advanced Persistent Threat Attack Prediction

With the continuous development of network security situations, the types of attacks increase sharply, but can be divided into symmetric attacks and asymmetric attacks. Symmetric attacks such as phishing and DDoS attacks exploit fixed patterns, resulting in system crashes and data breaches that cause losses to businesses. Asymmetric attacks such as Advanced Persistent Threat (APT), a highly sophisticated and organized form of cyber attack, because of its concealment and complexity, realize data theft through long-term latency and pose a greater threat to organization security. In addition, there are challenges in the processing of missing data, especially in the application of symmetric and asymmetric data filling, the former is simple but not flexible, and the latter is complex and more suitable for highly complex attack scenarios. Since asymmetric attack research is particularly important, this paper proposes a method that combines causal discovery with graph autoencoder to solve missing data, classify potentially malicious nodes, and reveal causal relationships. The core is to use graphic autoencoders to learn the underlying causal structure of APT attacks, with a special focus on the complex causal relationships in asymmetric attacks. This causal knowledge is then applied to enhance the robustness of the model by compensating for data gaps. In the final phase, it also reveals causality, predicts and classifies potential APT attack nodes, and provides a comprehensive framework that not only predicts potential threats, but also provides insight into the logical sequence of the attacker’s actions.

Detection of advanced persistent threat: A genetic programming approach

Advanced Persistent Threats (APTs) are an intimidating class of cyberattacks known for their persistence, sophistication, and targeted nature. These attacks, coordinated by highly motivated adversaries, pose a grave risk to organizations and individuals, often operating stealthily and evading detection. While existing research primarily focuses on applying Machine Learning (ML) methods to analyze network traffic data for APT detection, this article introduces a novel approach that utilizes Genetic Programming (GP). The proposed method not only detects APT attacks but also identifies their specific life cycle stages through the evolutionary capabilities of GP. Its effectiveness lies in its ability to excel in detecting intricate patterns, even within classes with a limited number of instances, a feat that is often challenging for traditional ML techniques. The method involves evolving and optimizing its models to effectively learn and adapt to complex APT behaviors. Experimentation with a publicly available dataset showcases the efficacy of the proposed method across diverse APT stages. The results demonstrate that the proposed method, GPC, achieves a 3.71% improvement in balanced accuracy compared to the best-performing model from related works. Moreover, a thorough analysis of the best-evolved GP model uncovers valuable insights about identified features and significant patterns. This research advances the APT detection paradigm by leveraging GP’s capabilities, providing a fresh and effective perspective on countering these persistent threats.

A Survey on Advanced Persistent Threat Detection: A Unified Framework, Challenges, and Countermeasures

In recent years, frequent Advanced Persistent Threat (APT) attacks have caused disastrous damage to critical facilities, leading to severe information leakages, economic losses, and even social disruptions. Via sophisticated, long-term, and stealthy network intrusions, APT attacks are often beyond the capabilities of traditional intrusion detection methods. Existing methods employ various techniques to enhance APT detection at different stages, but this makes it difficult to fairly and objectively evaluate the capability, value, and orthogonality of available techniques. Overly focusing on hardening specific APT detection stages cannot address some essential challenges from a global perspective, which would result in severe consequences. To holistically tackle this problem and explore effective solutions, we abstract a unified framework that covers the complete process of APT attack detection, with standardized summaries of state-of-the-art solutions and analysis of feasible techniques. Further, we provide an in-depth discussion of the challenges and countermeasures faced by each component of the detection framework. In addition, we comparatively analyze public datasets and outline the capability criteria to provide a reference for standardized evaluations. Finally, we discuss insights into potential areas for future research.

Design and implementation of anti-mapping security access technology based on illegal scanning

Abstract In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system’s recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.

A dynamic provenance graph-based detector for advanced persistent threats

Advanced Persistent Threats (APTs) pose a major cyber threat due to their stealthy, long-term nature and intricate complexity, making them particularly challenging to detect. Provenance graphs map interactions between system entities as directed, heterogeneous networks, offering rich semantic information valuable for threat identification. However, most existing approaches rely on static graph analysis, overlooking the critical dynamics of evolving threats. Dynamic graph analysis offers the potential to capture both temporal and structural insights, but current methods focus on single-perspective learning, failing to fully exploit the inherent relationships and evolving patterns within the data. To address this gap, we propose CGL-AD, a Contextualized Graph Learning APT Detector. CGL-AD leverages temporal graph learning to capture subtle temporal changes and overall structural transformations over time. It then integrates hash-based data stream frequency estimation techniques to identify local topological alterations, subsequently feeding these rich embeddings into a powerful sequence learning model. Experimental results on three widely used datasets: Streamspot, Camflow-apt and Shellshock datasets demonstrate that CGL-AD significantly outperforms existing methods. Specifically, CGL-AD outperforms the best baseline FLASH on these datasets by 1.5%, 3.7%, and 5.6% respectively in terms of Receiver Operating Characteristic -Area Under the Curve (ROC-AUC), effectively revealing hidden APT attack patterns in dynamic provenance graphs.

RT-APT: A real-time APT anomaly detection method for large-scale provenance graph

Advanced Persistent Threats (APTs) are prevalent in the field of cyber attacks, where attackers employ advanced techniques to control targets and exfiltrate data without being detected by the system. Existing APT detection methods heavily rely on expert rules or specific training scenarios, resulting in the lack of both generality and reliability. Therefore, this paper proposes a novel real-time APT attack anomaly detection system for large-scale provenance graphs, named RT-APT. Firstly, a provenance graph is constructed with kernel logs, and the WL subtree kernel algorithm is utilized to aggregate contextual information of nodes in the provenance graph. In this way we obtain vector representations. Secondly, the FlexSketch algorithm transforms the streaming provenance graph into a sequence of feature vectors. Finally, the K-means clustering algorithm is performed on benign feature vector sequences, where each cluster represents a different system state. Thus, we can identify abnormal behaviors during system execution. Therefore RT-APT enables to detect unknown attacks and extract long-term system behaviors. Experiments have been carried out to explore the optimal parameter settings under which RT-APT can perform best. In addition, we compare RT-APT and the state-of-the-art approaches on three datasets, Laboratory, StreamSpot and Unicorn. Results demonstrate that our proposed method outperforms the state-of-the-art approaches from the perspective of runtime performance, memory overhead and CPU usage.

Development of Detection and Mitigation of Advanced Persistent Threats Using Artificial Intelligence and Multi-Layer Security on Cloud Computing Infrastructure

This research proposes a novel approach for detecting and mitigating Advanced Persistent Threats (APTs) in cloud computing infrastruc ture, offering more comprehensive protection compared to previous methods. By integrating detection and mitigation, this study addresses the shortcomings of prior research that focused solely on detection. Based on the conducted research, Artificial Intelligence (AI) detected Cross-Site Scripting (XSS) attacks with an accuracy of 0.9951, SQL Injection (SQLI) at 0.9964, and Remote Code Execution (RCE) at 0.9876. In trials against new attacks, the detection success rates reached 70% for XSS, 98% for SQLI, and 100% for RCE. During the deployment phase, the system successfully identified 23.040 out of 108.394 requests as XSS attacks, 2.684 out of 128.750 as SQLI attacks, and 1.135 out of 46.450 as RCE attacks. The detection and mitigation methods were directly tested on cloud server experiencing APT attacks. The daily attacks on the server reached 1.980, with 663.000 requests. Additionally, the number of attacks directed at authentication or sensitive pages reached 17.913.701. Attack mitigation was tested through seven layers of security, including DNS Protection, Config Server Firewall (CSF), OWASP ModSecurity, HTTP middleware, data filter or sanitizer, template engine, and manual mitigation successfully blocking million of persistent attacks. The DNS protection layer successfully mitigated 59,000 out of a total of 19 million requests. The CSF layer mitigated 173 sources IP of DDoS attacks. The ModSecurity layer mitigated 17,916,204 attacks. All attacks were successfully mitigated before reaching the HTTP Middleware stage or next layer. The use of NIST 2.0 standards helps manage security risks through identification, protection, detection, response, and recovery. Test results indicate that this multi-layered system is more efficient and effective in detecting and mitigating attacks compared to traditional methods. However, the complexity of implementation and maintenance poses challenges that must be addressed. This research significantly contributes to a more adaptive and sustainable cybersecurity strategy.

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.

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and Trends

Advanced persistent threat (APT) attacks present a significant challenge for any organization, as they are difficult to detect due to their elusive nature and characteristics. In this paper, we conduct a comprehensive literature review to investigate the various APT attack detection systems and approaches and classify them based on their threat model and detection method. Our findings reveal common obstacles in APT attack detection, such as correctly attributing anomalous behavior to APT attack activities, limited availability of public datasets and inadequate evaluation methods, challenges with detection procedures, and misinterpretation of requirements. Based on our findings, we propose a reference architecture to enhance the comparability of existing systems and provide a framework for classifying detection systems. In addition, we look in detail at the problems encountered in current evaluations and other scientific gaps, such as a neglected consideration of integrating the systems into existing security architectures and their adaptability and durability. While no one-size-fits-all solution exists for APT attack detection, this review shows that graph-based approaches hold promising potential. However, further research is required for real-world usability, considering the systems’ adaptability and explainability.

ProcSAGE: an efficient host threat detection method based on graph representation learning

Advanced Persistent Threats (APTs) achieves internal networks penetration through multiple methods, making it difficult to detect attack clues solely through boundary defense measures. To address this challenge, some research has proposed threat detection methods based on provenance graphs, which leverage entity relationships such as processes, files, and sockets found in host audit logs. However, these methods are generally inefficient, especially when faced with massive audit logs and the computational resource-intensive nature of graph algorithms. Effectively and economically extracting APT attack clues from massive system audit logs remains a significant challenge. To tackle this problem, this paper introduces the ProcSAGE method, which detects threats based on abnormal behavior patterns, offering high accuracy, low cost, and independence from expert knowledge. ProcSAGE focuses on processes or threads in host audit logs during the graph construction phase to effectively control the scale of provenance graphs and reduce performance overhead. Additionally, in the feature extraction phase, ProcSAGE considers information about the processes or threads themselves and their neighboring nodes to accurately characterize them and enhance model accuracy. In order to verify the effectiveness of the ProcSAGE method, this study conducted a comprehensive evaluation on the StreamSpot dataset. The experimental results show that the ProcSAGE method can significantly reduce the time and memory consumption in the threat detection process while improving the accuracy, and the optimization effect becomes more significant as the data size expands.

Data Exfiltration Anomaly Detection on Enterprise Networks using Deep Packet Inspection

Advanced persistent threats (APT) are threat actors with the advanced Technique, Tactic and Procedure (TTP) to gain covert control of the computer network for a long period of time. These threat actors are the highest cyber attack risk factor for enterprise companies and governments. A successful attack by the APT threat Actors has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. The final goal for the APT Attack is to exfiltrate victims data or sabotage system. This aim of this research is to exercise multiple Machine Learning Approach such as k-Nearest Neighbors and H20 Deep Learning Model and also employ Deep Packet Inspection on enterprise network traffic dataset in order to identify suitable approaches to detect data exfiltration by APT threat Actors. This study shows that combining machine learning techniques with Deep Packet Inspection significantly improves the detection of data exfiltration attempts by Advanced Persistent Threat (APT) actors. The findings suggest that this approach can enhance anomaly detection systems, bolstering the cybersecurity defenses of enterprises. Consequently, the research implications could lead to developing more robust strategies against sophisticated and covert cyber threats posed by APTs.

APT-scope: A novel framework to predict advanced persistent threat groups from enriched heterogeneous information network of cyber threat intelligence

Addressing the expanding Advanced Persistent Threat (APT) landscape is crucial for governments, enterprises and threat intelligence research groups. While defenders often rely on tabular formats for assets like logs, alerts, firewall rules; attackers leverage a graph-based mindset. In this work, we propose a novel multistage framework named APT-Scope which employs a comprehensive approach to Cyber Threat Intelligence (CTI) analysis on qualified real-world data. APT-Scope workflow consists of data gathering, enrichment, and analysis stages, where relationships between entities are used to construct a Heterogeneous Information Network (HIN). We applied CTI enrichment using additional active data collection techniques like DNS and Whois lookups, port scans, SSL footprinting, named entity recognition via SpaCy, and constructed a machine learning pipeline to predict relationships between entities using FastRP and Logistic Regression. By analyzing the resulting HIN, we discovered aliases for APT groups and predicted threat actors of APT attacks with unknown perpetrators. We observed AUCPR metrics as train score = 96.57% and test score = 92.36%. Our work is beneficial to oversee the entire APT landscape, steer ongoing and future CTI operations and make strategic decisions.

Complexity of Contemporary Indicators of Compromise

The cybersecurity landscape has undergone substantial transformation, especially in the sphere of Advanced Persistent Threats (APT). These evolving threats, marked by increased sophistication, scale, and impact, require the critical revaluation of traditional security models and the development of more advanced defensive strategies. This study offers a comprehensive analysis of the progress in APT attack methodologies over the past 30 years, focused on the evolving nature of compromise (IoCs) and their role in shaping future predictive and defensive mechanisms. Using a rigorous methodological approach, this survey systematically reviewed 21 significant APT incidents that span three decades. This includes integrating data from various sources such as academic journals, specialised cybersecurity blogs, and media reports. Using comparative and analytical methods, this study dissects each incident to provide an intricate understanding of the APT landscape and the evolution of IoCs. Our findings indicate a notable change in thinking from isolated hacker activities to organised state-sponsored APT operations driven by complex motives such as political espionage, economic disruption, and national security interests. Advancements in APTs are characterised by sophisticated persistence mechanisms, innovative attack vectors, advanced lateral movement within networks, and more covert data exfiltration and evasion methods.This study emphasises the difficulties in detecting advanced persistent threat (APT) activities due to their sophisticated and secretive nature. This stresses the importance of thoroughly investigating the evidence of such activities and highlights the need for a dynamic and initiative-cybersecurity approach. This study also highlights the crucial role of integrating IoC understanding into AI-driven predictive models and frameworks to predict potential APT. This integration is essential for the development of pre-emptive defence strategies. This study provides valuable information on the evolving dynamics of cyber threats and emphasises the urgent need for forward-thinking adaptive cybersecurity strategies. It offers a framework for understanding the complexities of modern APTs and guides the development of more effective AI-enhanced defence mechanisms against emerging cyber threats.

The Homology Determination System for APT Samples Based on Gene Maps

At present, there are fewer types of homology determination methods for advanced persistent threat (APT) samples detection, and most existing determination schemes have problems such as high cost, low accuracy, and difficulty in identifying unknown APT samples. Therefore, we proposed a homology determination system for APT samples based on gene maps by integrating deep learning and gene maps. Firstly, we extract the software gene features from the samples uploaded by the user and apply the TF-IDF algorithm to clean the extracted software genes. The Word2Vec algorithm is used to vectorize all the genes to construct the gene sample vectors. And we use a LSTM-based classifier to detect APT attack samples. Finally, the K-nearest neighbor algorithm is used to determine the homology of gene-sharing APT samples. The detailed construction process of the scheme is given in this paper, including APT sample gene extraction, cleaning, clustering, sample detection, and homology determination. Experimental validation showcases our model outperforming existing methodologies with an accuracy of 95%, precision of 94%, and recall of 95%. When compared to previous models, the superiority of our approach is evident. These results underscore our model’s high efficiency and accuracy, confirming its potential for significant application in the field of cybersecurity.

Machine learning-enabled hybrid intrusion detection system with host data transformation and an advanced two-stage classifier

Network Intrusion Detection Systems (NIDS) have been extensively investigated by monitoring real network traffic and analyzing suspicious activities. However, there are limitations in detecting specific types of attacks with NIDS, such as Advanced Persistent Threats (APT). Additionally, NIDS is restricted in observing complete traffic information due to encrypted traffic or a lack of authority. To address these limitations, a Host-based Intrusion Detection system (HIDS) evaluates resources in the host, including logs, files, and folders, to identify APT attacks that routinely inject malicious files into victimized nodes. In this study, a hybrid network intrusion detection system that combines NIDS and HIDS is proposed to improve intrusion detection performance. The host data undergoes a Language Processing (NLP)-based Bidirectional Encoder Representations from Transformers (BERT) model from textual representation to a numerical one in order to process host data in a similar way to the network flow data through machine learning models. The feature flattening technique is applied to flatten two-dimensional host-based features that is provided by BERT into one-dimensional vectors so that host-based and network flow-based features can be processed by advanced Machine Learning (ML) models. In order to enhance HIDS effectiveness, a two-stage collaborative classifier is utilized, which applies two tiers of machine learning algorithms, binary and multi-class classifiers, to detect network intrusions. Once a binary classifier is used to detect benign samples to reduce the complexity of the original problem, the attack data are classified by a multi-class supervised learner to identify attack types. Hence, the overall performance of the two-stage collaborative model outperforms the baseline classifier, XGBoost. The proposed method is shown to generalize across two well-known datasets, CICIDS 2018 and NDSec-1. The performance of XGBoost, which represents conventional ML, is evaluated. Combining host and network features enhances attack detection performance (macro average F1 score) by 8.1% under the CICIDS 2018 dataset and 3.7% under the NDSec-1 dataset. Meanwhile, the two-stage collaborative classifier improves detection performance for most single classes, especially for DoS-LOIC-UDP and DoS-SlowHTTPTest, with improvements of 30.7% and 84.3%, respectively, when compared with the traditional ML models.

APT Attack Detection Method Based on Traceability Graph

This article proposes an Advanced Persistent Threat (APT) attack detection method based on traceability graphs, aimed at addressing the complexity and concealment of APT attacks. This method describes system behavior by constructing a traceability graph, optimizing it to reduce redundant information, converting the traceability graph sequence into a feature vector sequence, and using an encoder decoder model to train the GRU (Gate Recurrent Unit) model to extract long-term features of the sequence. Finally, a normal behavior model is established through clustering to detect APT attacks.

Two-stage advanced persistent threat (APT) attack on an IEC 61850 power grid substation

Advanced Persistent Threats (APTs) are stealthy, multi-step attacks tailored to a specific target. Often described as ’low and slow’, APTs remain undetected until the consequences of the cyber-attack become evident, usually in the form of damage to the physical world, as seen with the Stuxnet attack, or manipulation of an industrial process, as was the case in the Ukraine Power Grid attacks. Given the increasing sophistication and targeted nature of cyber-attacks, especially APTs, this paper delves into the substantial threats APTs pose to critical infrastructures, focusing on power grid substations. Through a detailed case study, we present and explore a 2-stage APT attack on an IEC 61850 power grid substation, employing a Hardware-in-the-Loop (HIL) testbed to simulate real-world conditions. More specifically, this paper discusses two significant experiments conducted to assess vulnerabilities in the control protocols used in IEC 61850 substations: IEC 60870-5-104 and IEC 61850. The integration of findings from these experiments revealed a number of previously undiscussed potential threats to power grid infrastructure that could arise from attacking one or more substations. To better address these potential threats, the paper proposes an extension to the Industrial Control System (ICS) kill chain that explicitly accounts for the consequences of attacks on the physical aspects of Cyber-Physical Systems (CPSs).

ATT&CK-based Advanced Persistent Threat attacks risk propagation assessment model for zero trust networks

In recent years, the growing frequency and intensity of Advanced Persistent Threats (APTs) have significantly undermined the legitimacy and financial stability of government agencies, enterprises, and other entities. Moreover, these attacks have shown the inherent vulnerabilities in conventional border defense strategies. The emergence of the zero trust network architecture can be attributed to the increasing complexity of the cyber threat landscape. With the application of risk assessment, this paper effectively tackles the challenges posed by conventional network defense limitations and enhances the efficiency of the access control decision-making process. Nevertheless, the existing risk assessment approaches primarily focus on conventional security assessment objectives, which exhibits a deficiency in the ability to dynamically assess APT attacks. The Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) Framework introduced in this paper is a novel approach to mitigating APT attacks. This paper aims to mine and analyze the frequent item set and correlation of cyber threat penetration attack techniques. The paper also intends to construct an attack technique relationship diagram and develop a tactical prediction model for cyber threat penetration attacks using the Markov chain model. Finally, our study aims to establish a risk propagation model for APT threats based on the aforementioned model. The approach presented in this paper significantly enhances the capacity of zero trust networks in addressing sophisticated cyber threats.

Intelligent Security Detection and Defense in Operating Systems Based on Deep Learning

With the development of network technology, APT(advanced persistentthreat) attacks are increasing, and research on the security of enterprise assets requires effective detection of digital assets in the network space and effective management of assets through screening and combing, which is the key to real-time monitoring of the safe operation of the system. However, the accompanying malware also poses a threat to the user's property and privacy, so an effective method of detecting Android malware is necessary. In this research direction, although the feature processing capability of traditional machine learning has been improved, there are problems that feature extraction relies on expert experience and the accuracy is low. Therefore, this paper combines deep learning reinforcement technology with the operating system to detect and defend the system in advance, so as to achieve network security.