Network Security Defense Research Articles

Vulnerability extraction and prediction method based on improved information gain algorithm.

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened.

Design of a cybersecurity defense system based on big data and artificial intelligence

With the development of big data and artificial intelligence technologies, hacker attack techniques and capabilities have continuously improved, and the methods of network attacks have diversified. Without cybersecurity, there is no national security. Therefore, cybersecurity has become a focal point of attention. To comprehensively enhance the network security defense capabilities of computer operating systems, aligning with the rapid development trends of big data and artificial intelligence technologies, this study focuses on constructing an efficient, stable, and practical cybersecurity defense system. This system deeply integrates advanced technologies of big data analysis and artificial intelligence, thoroughly analyzing the current status of network information security in computer operating systems and closely aligning with the practical needs of design and production. The aim is to provide highly valuable reference solutions for the field of cybersecurity defense.

Communication security of intelligent information service platform combining AES and ECC algorithms

ABSTRACT The rapid progress of information technology has made the status of information service platforms increasingly prominent in social operations. However, their communication security is also facing unprecedented challenges. In response to the issues of insufficient security and efficiency in traditional encryption algorithms, this study combines advanced encryption standard algorithms with elliptic curve cryptography algorithms to generate encryption key pairs through elliptic curve cryptography algorithms. It utilizes advanced encryption standard algorithms to generate symmetric keys, and then encrypts the keys of the advanced encryption standard using the public key of the elliptic curve cryptography algorithm. Based on this, it implements advanced encryption standard encryption on data and designs a hybrid encryption algorithm. The results showed that the throughput of this method was 4000bit/s, with a delay of 550 m, which is higher than other algorithms, proving its high efficiency in processing data. The average bit change of the ciphertext in the hybrid encryption algorithm was 55.8 bits, with a range of bit changes of [69.3, 82.7], which is significantly higher than other algorithms, proving its high security. The designed algorithm has significant benefits in communication security of information service platforms and plays an important role in promoting the development of future network security defense technology.

Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning

Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning

Verification and Optimization of Network Security Defense System from the Perspective of Blue Army in Actual Offensive and Defensive Exercises

From the point of view of signal camouflage, this paper proposes an active network defense system. Then, the optimal camouflage target selection scheme is proposed. Its goal is to solve the problems existing in the information constructionof Chinese military equipment support. This method establishes a multistage game model based on weapon support informationnetwork attack and defense. The attack and defense benefits are quantitatively calculated based on the cost of signal concealment. The solution to the refined Bayesian balance problem is given. Then, a multistage optimization algorithm for camouflage signal selection is proposed. Finally, experimental research proves the proposed algorithm to be reasonable and practical.

Construction of Power System Network Security Defense Behavior Decision-making Model based on Artificial Intelligence Technology

According to the needs of power grid monitoring architecture and information security cooperation protection, this project builds a multi-level, deeply distributed active security cooperation defense mode. A complete implementation method is proposed from the perspective of model architecture and function mechanism. The optimal defense strategy based on grey correlation is studied according to the characteristics of cooperation between regions. In this way, the coordination between the equipment is realized to achieve multi-level protection from the host layer to the security equipment layer and then to the network layer. Multiple detection mechanisms are used to realize the comprehensive detection and integrated judgment of abnormal documents in the cloud environment. This provides maximum protection for cloud users. Experiments show that this method can effectively suppress the malicious attacks of malicious users and reduce the damage caused by viruses. In this way, both the cloud and the customer are protected.

PatchesNet: PatchTST-based multi-scale network security situation prediction

Internet of Things (IoT) technology increases connectivity between devices; however, IoT device networks are less secure, making them highly susceptible to attack. Most of the network security defense techniques are passive defense with inevitable delays. For instance, in the case of intrusion detection systems, during the time interval between detecting an attack and implementing defenses, the attacker may inflict damage on the target. Therefore, the adoption of active defense techniques becomes particularly important. Network Security Situation Prediction (NSSP) is an active defense technology capable of predicting future states of network security. Currently, most methods in the NSSP field focus on single-step prediction, while research related to multi-step prediction is scarce. Hence, this paper presents an NSSP method based on the PatchTST framework(PatchesNet), which exhibits good multi-step predictive performance. The method uses Variational Mode Decomposition (VMD) to deal with sequence instability. Multi-scale Patch delivery (MSP), Multi-scale Second Decomposition (MSD), and Period Embedding (PeE) are used to obtain more sequence features. Correlation Fusion (CF) can reduce the noise effect on prediction. Experiments are conducted on three datasets. The results demonstrate that PatchesNet exhibits favorable performance, relatively reducing the MSE and MAE by 39.243% and 26.033%, respectively, compared to the previous state-of-the-art method.

Construction a Portrait of Network Security Defense Capabilities Driven by Risk Monitoring Data

Construction a Portrait of Network Security Defense Capabilities Driven by Risk Monitoring Data

Network Security Defense Strategy of Deep Reinforcement Learning Oriented to Game Battle

With the popularity of online games and the increasing frequency of game battles, game platforms are facing more and more network security threats and attacks. In order to effectively deal with these threats, this study proposes a network security defense strategy of Deep Reinforcement Learning (DRL) for game warfare. Through the modeling of game battle environment and the application of DRL algorithm, this strategy can monitor and identify all kinds of network attacks in game battle in real time, and take corresponding measures to defend and deal with them. In this study, the corresponding experimental environment is designed, and the network security defense strategy based on DRL is evaluated and analyzed. The experimental results show that the strategy has obvious advantages in network attack detection rate and false alarm rate, and has good stability and reliability. In addition, the strategy also shows strong generalization ability and adaptability, and can effectively deal with different types of network attack threats. The results of this study are of great significance for strengthening the network security defense of the game platform and improving the user experience and the stability of the game environment.

USE OF ARTIFICIAL INTELLIGENCE METHODS AND MODELS FOR IMPROVING EXPERT SYSTEMS OF INTRUSION DETECTION

In the domain of cyber security, the efficacy of Intrusion Detection Systems (IDS) is critical for the proactive identification and mitigation of cyber threats. This research delineates a novel paradigm for enhancing IDS accuracy through the integration of advanced Artificial Intelligence (AI) methodologies, thereby setting a new benchmark in network security defense mechanisms. Utilizing a synergistic approach that combines both descriptive and inferential statistical analyses, this study introduces an expert system endowed with the capability to detect network intrusions with an unparalleled accuracy rate of 99.98%. By incorporating Extreme Gradient Boosting (XGBoost) for the classification of predefined attack vectors and a Variational Autoencoder (VAE) for anomaly detection, the research extends the boundaries of current cyber threat detection frameworks. These methodologies not only enhance the precision of threat categorization but also introduce a mechanism for the system to adapt to novel, previously unidentified cyber threats through real-time learning and adaptation to emerging data patterns. Critically, the expert system is engineered to facilitate high-speed data processing and supports online learning, making it optimally suited for application in high-traffic network environments. The scientific novelty of this research is encapsulated in the formulation of advanced AI-driven models for the dual purposes of traffic anomaly detection and the classification of cyber-attack types based on distinctive behavioral characteristics. These models are meticulously designed to evolve, learning from new data in real time, thereby continuously enhancing the system's efficacy. In practical terms, the system provides a robust solution for the protection of digital ecosystems against intrusions, enabling the automatic filtration of malicious network traffic. Beyond its immediate applicability, the study contributes to the field of cyber security by laying down a foundational framework for the future exploration of AI-based security solutions. It invites further scientific inquiry into the development of adaptive, intelligent IDS mechanisms, potentially revolutionizing the approach to cyber defense strategies.

Design and implementation of computer network security detection and control system

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the systems design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.

Research on the application of network security defence in database security services based on deep learning integrated with big data analytics

Every day, more people use the internet to send and receive sensitive information. A lot of confidential data is being transmitted electronically between people and businesses. Cyber-attacks, which are the inevitable result of our growing reliance on digital technology, are a reality that we must face today. This paper aims to investigate the impact of Big Data Analytics (BDA) on information security and vice versa. Additionally, an Artificial Neural Network (ANN)-based Deep Learning (DL) method for Anomaly Detection (AD) is presented in this work. To improve AD, the proposed method uses a DL-based detection method, which is used to parse through many collected security events to develop individual event profiles. The paper also investigated how BDA can be used to address Information Security (IS) issues and how existing Big Data technologies can be adapted to improve BDA's security. This study developed a DL-based Security Information System (DL-SIS) using a combination of event identification for data preprocessing and different Artificial Neural Network (ANN) methods. The feasibility and impact of implementing a Big Data Analytics (BDA) system for AD are investigated and addressed in this study. From this study, we learn that BDA systems are highly effective in securing Critical Information Setup from several discrete cyberattacks and that they are currently the best method available. By analyzing the False Positive Rate (FPR), the system facilitates quick action by security analysts in response to cyber threats. DL-SIS had the highest AD accuracy of 99.40% but performed poorly in the high-dimensional dataset.

Deep Learning-based Network Security Protection for Scheduling Data in Power Plant Systems

Abstract Scheduling data of power plant systems plays a pivotal role in grid security. In this paper, the intrusion detection model IDP-TSW is constructed by using deep learning technology, feature extraction of raw traffic data based on density peak clustering algorithm and control variable method, and the final classification is realized by softmax. After completing the intrusion detection, for the network malicious intrusion situation further proposed the security protection strategy selection model HMS-BAG based on the Bayesian attack graph, formalized the description of the protection strategy selection problem, and proposed the optimal security protection strategy selection algorithm based on PSO. In the network intrusion detection performance experiments, the performance of the IDP-TSW model proposed in this paper outperforms the Bi-LSTM and CNN+Bi-LSTM models in terms of accuracy, recall, and F1 value. The F1 values for determining normal and abnormal data are 96.57% and 95.75%, respectively, and the precision and recall are also higher than 94%. Detecting Generic and Reconnaissance attacks is more than 90% accurate, but Dos attacks and others are relatively absent. In the network security defense performance experiment, the proposed HMS-BAG model achieved a defense success rate of 94.2% and a defense gain of 170.68.

An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems

<abstract> <p>Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques.</p> </abstract>

Wireless sensor network security defense strategy based on Bayesian reputation evaluation model

AbstractIn order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed.

Application of artificial intelligence in network security defense

With the development of today's social network, people use the Internet more frequently and become more reliant on it. However, as a result of the development of the network, network attacks are becoming more frequent, and people have an imperative need for a secure and dependable computer network. Meanwhile, the technology of artificial intelligence is progressively maturing, so the application of artificial intelligence in network security defense will be a more effective means of addressing network security's hidden dangers. This paper focuses primarily on the benefits of the application of artificial intelligence to network security defense, as well as summarizing and analyzing the application of artificial intelligence to network security defense. This paper concludes, by analyzing the most recent literature and evaluations, that AI plays a crucial role in resolving network security issues and can effectively withstand certain network attack threats.

Research on the design of computer network security defense system applying cloud computing technology

Abstract In this paper, we first study cloud computing technology and build a computer network security defense system based on cloud computing technology. Secondly, to better analyze the network generated by cloud computing, the corresponding network topology is established according to the characteristics of the cloud computing network, and the complete network topology is generated through four steps. Then, the avalanche effect under the cloud computing network attack and its propagation are studied. Simulated tests are used to evaluate the performance of the network security defense system security. According to the results, the test group's network security defense rate is 89.2% when the intensity of network intrusion behavior is the highest. The defense effectiveness of all eight regions in the security domain test is above 90%, which is excellent.

Design of Computer Network Security Defense System Based on Artificial Intelligence and Neural Network

Design of Computer Network Security Defense System Based on Artificial Intelligence and Neural Network

NIDS-FLGDP: Network Intrusion Detection Algorithm Based on Gaussian Differential Privacy Federated Learning

As a widely used network security defense technology, network intrusion detection has more deep learning methods used to improve the performance of intrusion detection. However, this method requires a large-scale network traffic data set for training, increasing privacy leakage risk. In this paper, a network intrusion detection algorithm based on Gaussian differential privacy federated learning (NIDS-FLGDP) is proposed. NIDS-FLGDP adopts the client–server architecture of federated learning, introduces the differential privacy of the Gaussian mechanism to ensure the security of the calculation process, uses the improved FedAvg algorithm to reduce communication overhead, and uses the improved 1D CNN to participate in collaborative training for the local model. Optimal parameters for Gaussian differential privacy and the optimal number of participating clients were determined from experiments. Model accuracy rates for binary classification and multi-classification training NIDS-FLGDP are 0.97, 0.975, 0.97 and 0.97, 0.985, 0.96, respectively, for KDD CUP99, NSL_KDD, and UNSW_NB15 network intrusion detection datasets. The results show that NIDS-FLGDP improves intrusion detection performance while protecting network traffic privacy compared with the previous methods. Its applicability and effectiveness have been fully verified, which provides a practical reference for the safe processing and analysis of a large number of diversified network traffic data in the future.

A Cyber-Physical Power System Risk Assessment Model Against Cyberattacks

With the integration of cyber devices into power systems, such as information communication technology devices and intelligent electronic devices, there are currently a large number of software vulnerabilities (SVs) in cyber devices, which are probably used to obtain the authority of cyber devices for malicious attackers and be carried out subsequently severe attacks on power systems. Thus, it is inappropriate to only consider the power risks as the risk assessment for the system with the strong coupling between cyber systems and power systems. In this article, we investigate a dynamic risk assessment model for the cyber-physical power system (CPPS) against cyberattacks, combining the network security vulnerability of the supervisory control and data acquisition (SCADA) system in a substation and physical consequences in power systems caused by it under malicious control. By construction, a dynamic network security vulnerability assessment method for the SCADA system is well studied by considering SVs and deployed network security defense measures, which is also corrected by the propagation characteristics. Moreover, we estimate physical consequences by minimum shedding loads in N-1 contingencies caused by the SCADA system in a substation under malicious control. Finally, simulations on modified IEEE 14-bus and IEEE 118-bus systems verify the proposed model's effectiveness, highlighting the importance of network security in CPPS.