Security Defense Technology Research Articles

Geopolitical Implications of the US-India Strategic Partnership in the Indo-Pacific Region

A geo-economics and geopolitical analysis of the shifting dynamics of the bilateral relationship between the USA and India has significant implications for the IPE in the Indo-Pacific region. This paper aims to evaluate the interaction process between the US and India, utilizing major theories related to China's emergence. This work, guided by the methodologies of realism, neoclassical realism, constructivism, and geo-economics, analyzes the strategic coordination of security defense technology and economic cooperation. It also scrutinizes how the cooperation impacts regional players such as ASEAN, Japan, and Australia, balancing the divergence of geographical perspectives and India's decoupled strategic sovereignty. The last chapter of the study analyses the transformation of the Indo-Pacific by the partnership and presents ways to advance this alliance. In fact, this paper will assist in developing a theoretical understanding regarding the great-power rivalry with the region and the importance of the bilateral partnership for the maintenance of the rule-based international order.

Network anomaly detection and security defense technology based on machine learning: A review

Robust solutions are essential for protecting complex network systems in the constantly changing cybersecurity scenario. This investigation examines the role of machine learning (ML) in improving the safety of digital infrastructure by examining network anomaly detection and security defense. We evaluate the effectiveness of key ML techniques, including deep learning (DL), ensemble methods, and supervised and unsupervised learning, in recognizing abnormalities and reducing dangers. Real-time monitoring and adaptive reactions are emphasized in the analysis of ML-based systems integration into comprehensive security frameworks. This section also addresses challenges like interpretability, harmful assaults, and model resilience. Real-world case examples emphasize the importance of ML in the advancement of network security. The paper continues with a discussion of ML's future role in the optimization of security measures and the mitigation of emergent threats.

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.

Review on Security Defense Technology Research in Edge Computing Environment

Review on Security Defense Technology Research in Edge Computing Environment

A Security Resilience Metric Framework Based on the Evolution of Attack and Defense Scenarios

The frequent attacks show that no information system is absolutely safe and the security capabilities are relative. Security resilience becomes a complementary priority for improving information systems’ continuous service and security capabilities in the face of attacks such as unknown vulnerabilities and backdoors. Endogenous security defense technology has become an important research aspect to improve the security resilience of information systems. However, there are some limitations in the research of the information system security resilience evaluation model, such as lacking indexes to characterize the system security resilience under an attack environment. In this paper, a security resilience enhancement strategy based on dynamic defense is constructed to improve the security performance of the system through IP port hopping and attack surface conversion. For the adversarial behaviors of attackers and defenders, we propose a security resilience metric framework based on the evolution of attack and defense scenarios, which is evaluated using a resilient security evaluation model based on the fuzzy Choquet integral. In the model, the weights of evaluation indicators are calculated based on the DEMATEL method. The 2-addable fuzzy measures of each indicator are calculated secondarily. Then the security performance of the system is calculated using the fuzzy Choquet integral. Absorptive capacity, adaptive capacity, and resilience factor are proposed to better supervise the metric framework’s validity. Finally, four groups of control cases were created by building the Web service system after the endogenous security transformation as the experimental simulation scenario. Experimental simulation results show the superiority of the proposed metric model.

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.

Security Risk and Preventive Measures of Multimedia Database System under Remote Control of Network Robot

The rapid development of network technology makes the world enter a new era, but it also brings a variety of severe information security problems, which makes the network security more and more important. Based on the existing literature and information, this paper expounds the current situation of database information system security. In view of the security risks of the media database information, this paper proposes to use the remote control system of the network robot, through sorting out the organizational structure characteristics and technology of multimedia data, establish the security defense system, and try to manage the multimedia data security. This system takes the mobile robot as the control object and adds the vision and ultrasonic transducer for the robot, so that it can carry on the semi-autonomous movement under the control of human. In addition, the host PC is used as the main control terminal to send instructions to the remote robot and receive the feedback data information. The simulation results show that the robot remote control system has good security, can ensure the establishment of database security defense technology system, and has the characteristics of convenient operation, easy to expand, and strong mobility. From the research point of view of this paper, the combination of robotics and multimedia information security is booming and will become a new application prospect.

AI-based technology adhering in cyber security

Under the background of increasingly serious information security threats, it has attracted wide attention to solve network security problems efficiently. As a security defense technology to protect the network from attacks, intrusion detection system plays a very important role in the field of computer system and network security.

Zero trust access authorization and control of network boundary based on cloud sea big data fuzzy clustering

With the digital transformation of enterprises, the traditional security defense technology has been unable to meet the security requirements of enterprises, and the data security and privacy protection have brought great challenges to the Internet. Therefore, taking zero trust as the security concept and taking the network boundary as the best practice landing technology architecture, this paper studies the zero trust access authorization and control of network boundary based on cloud big data fuzzy clustering of. Through the network stealth technology, it constructs a virtual boundary for the enterprise, uses the cloud big data fuzzy clustering algorithm to mine the user behavior related data, and designs the trust evaluation mechanism to obtain the user trust level. The dynamic access authorization control mechanism is designed to judge the access requests in and out of the permission boundary. Combined with the user’s trust level, the legal requests and illegal requests are distinguished to complete the zero trust access authorization and control of network boundary. Experimental results show that: the method can accurately control the access authorization of the network boundary, improve the success rate of access authorization and control interaction; the interception rate of illegal access is high, and it has high securit.

Automatic Botnet Attack Identification Based on Machine Learning

At present, the severe network security situation has put forward high requirements for network security defense technology. In order to automate botnet threat warning, this paper researches the types and characteristics of Botnet. Botnet has special characteristics in attributes such as packets, attack time interval, and packet size. In this paper, the attack data is annotated by means of string recognition and expert screening. The attack features are extracted from the labeled attack data, and then use K-means for cluster analysis. The clustering results show that the same attack data has its unique characteristics, and the automatic identification of network attacks is realized based on these characteristics. At the same time, based on the collection and attribute extraction of Botnet attack data, this paper uses RF, GBM, XGBOOST and other machine learning models to test the warning results, and automatically analyzes the attack by importing attack data. In the early warning analysis results, the accuracy rates of different models are obtained. Through the descriptive values of the three accuracy rates of Accuracy, Precision, and F1_Score, the early warning effect of each model can be comprehensively displayed. Among the five algorithms used in this paper, three have an accuracy rate of over 90%. The three models with the highest accuracy are used in the early warning model. The research shows that cyberattacks can be accurately predicted. When this technology is applied to the protection system, accurate early warning can be given before a network attack is launched.

A federated learning method for network intrusion detection

AbstractIntrusion detection is a common network security defense technology. At present, there are many research using deep learning to realize network intrusion detection. This method has been proved to have high detection accuracy. However, deep learning requires large‐scale data sets for training. The network intrusion detection data set of some institution is lacking. If the network traffic data set is uploaded for centralized deep learning training, it will face privacy problems. Combined with the characteristics of network traffic, this article proposes a network intrusion detection method based on federated learning. This method allows multiple ISPs or other institutions to conduct joint deep learning training on the premise of retaining local data. It not only improves the detection accuracy of the model but also protects privacy in network traffic. This article conducts experiments on the CICIDS2017 network intrusion detection data set. Experimental results show that worker participating in federated learning have higher detection accuracy. The accuracy and other performance of federated learning are almost equal to those of centralized deep learning models.

RETRACTED ARTICLE: Forest management and resource monitoring based on AMI intrusion detection algorithm and artificial intelligence

Smart grid is the combination of computer network and traditional power system to realize the intelligence of power grid. AMI is an advanced measurement system, which connects the power system and users, realizes the two-way interaction of data and information between power supply enterprises and users, and promotes the development of smart grid. Therefore, the safe use of AMI system is the key to the development of smart grid. With the increasingly close connection between smart grid and computer network, the number of network attacks against AMI system continues to increase. However, the current security defense technology of AMI system is passive defense technology represented by protocol and encryption, which can not resist the invasion of unknown network attacks. The active defense technology represented by intrusion detection is an important barrier of AMI security defense. Now, AMI intrusion detection algorithm based on machine learning is often proposed. This paper will focus on the above problems in the process of AMI intrusion detection. Because the data size is small and the data function is not complete, the detection accuracy of DBN-OS-RKELM algorithm is low. An improved generalized regression neural network (GRNN) intrusion detection algorithm DBN-FOA-GRNN is proposed. The algorithm uses the generalized regression neural network (GRNN) with excellent nonlinear mapping function and high convergence rate. In order to improve the performance of GRNN intrusion detection, population optimization algorithm (FOA) is used to optimize the unique random parameter of GRNN to reduce the possibility of falling into local optimum. This paper uses the public intrusion detection data set NSL-KDD to verify and analyze the algorithm. Experimental results show that the two intrusion detection algorithms proposed in this paper have some improvement in detection effect compared with the traditional machine learning methods. The combination of the two algorithms can solve the problem of intrusion detection of various data, and meet the requirements of AMI intrusion detection.

New Active Defense Technology under the Background of Power Information Network Security

With the rapid development of the national power industry, the security of power information network has been widely concerned by the society, and the related impact has also been prominent. At this stage, the development of power information technology is more and more inseparable from the network, so network security has become more and more important, if the network has a serious threat in practical application, the loss caused will be unbearable. In the process of controlling the network security of power information industry, one of the most important and effective methods is active defense. Based on this, this paper puts forward the research of active defense technology under the background of power information network security, expounds the defects of firewall technology and intrusion trap technology in passive network security defense technology, and introduces the pitfalls of active defense new technology In addition, the paper discusses the new active defense technology and its application in power information network security, and puts forward reasonable suggestions. Therefore, the use of information technology to solve the hidden danger of power network security is important.

Moving target defense controller of mobile system based on Openflow sensor security scheme

This paper introduces a sensor security and defense scheme based on Openflow in mobile Internet of Things system. Adopting the flexible network characteristics of the Openflow network structure, at the network level of the mobile internet of things system, the goal of protecting the sensor devices, routers, switches and other devices in the mobile internet of things system is achieved by simultaneously carrying out the double random digital transformation of the network IP address and the data transmission ports of the cross-region Openflow exchange equipment through each sensor device in the mobile internet of things system and each router device in the data transmission process. Compared with the existing security defense technology of mobile IoT system, this technical solution is compatible and relatively easy to deploy, and can protect the whole system data transmission of mobile IOT system. In the initial stage of network system sniffing, such as hacker attack, the maximum extent of active security protection is provided to the mobile Internet of Things system carrying public network data by hiding sensor information.

Application of Artificial Intelligence Technology in Security Defense of Cyberspace

In the process of its development and application, the computer network has created the virtual network space, and as the relationship with the computer network is becoming more and more close, the improvement of the security defense of cyberspace ability is becoming more and more urgent. The use of artificial intelligence technology in cyberspace security defense can not only accept and process a large number of Internet information quickly and efficiently, but also improve the security defense of cyberspace and prevent the infringement of illegal elements, which is a matter of great concern to every user. Based on the analysis of the characteristics of cyberspace security defense and artificial intelligence technology, this paper discusses the application of artificial intelligence technology in cyberspace security defense with examples.

Application of New Active Defense Technology in Power Information Network Security

With the rapid development of the Internet and the expansion of the network scale, while the network brings unlimited convenience to people, the problem of network security has become increasingly prominent. Network security has become one of the main factors hindering the development of the Internet. In the operation of power network systems, in order to ensure network security, many security technologies are also being continuously applied. In the traditional network security defense technology, passive defense technology is used, all its defense measures are static and all rely on network administrators to implement equipment and manual configuration. In the new network era, the illegal attacks on power network information systems have become more and more frequent, at the same time, hackers’ offensive methods will also be updated and become more sophisticated. Therefore, traditional network security defense technologies have been unable to resist or even cannot withstand. Based on this, this paper focuses on new active defense technologies, analyzes its application in power information network security, and proposes defense of power information network security based on attack detection using honeypot technology suggestions.

Mimic Encryption System for Network Security

With the rapid development of the Internet, increasingly more attention has been paid to network security problems. A network security defense technology has become a very important research field. Currently, most network equipment transmits data in plaintext at the data link layer, which exposes important information, such as IP addresses, port numbers, and application protocols, to an attacker and provides an opportunity for network attacks. To protect a network against attacks and ensure its security, this paper proposes a mimic encryption system for network security. Based on the concepts of moving target defense and mimic security defense, using the principles of randomization, dynamism, and diversification, a data link layer mimic encryption system is constructed from the underlying network of an information system. By transforming the frame format, a reconfigurable encryption algorithm, an hash algorithm, and a pseudo-random number generator are used to design different combination encryption modes. Then, the hash value of an encrypted frame is obtained by performing the hash operation, and feedback update is performed to generate new key parameters for the hash key pool. In addition, the pseudo-random selection of combinations of encryption algorithms and keys is performed to achieve “one frame-one key”. Finally, an FPGA is used as the network encryption card, and a CPU is used to realize two-party key agreement and the upper layer application. Using the FPGA + CPU hardware and software collaboration, the attack surface is expanded. Taking advantage of the high anti-interference property of an FPGA, part of the attack against the software system is filtered. The experimental results and analysis show that the encryption and decryption performance of this system in a 10 G network are approximately 500 MB/s. Thus, the system can effectively prevent the leakage of user data and resist network sniffing, vulnerability attacks, exhaustive key search attacks, and ciphertext-only attacks. Moreover, this system provides high security.