The automaton modeling of typical network attacks

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

The modern world technology is civilized, globalized and modernized. The technological development of social networks and e-commerce applications produce larger data. This data communication is major task, because device to device communication need network terminal. This data transmission is not safe because of different types of tools and software available to destroy the existing network. In the field of network security during data transfer from one particular node to other node some security vulnerability is happened this is the one of the critical issue in this sector. The reason for this network security is different types of data attacks are happen in day to day life. It is easy to establish a new network but protecting the entire network is a big issue. This network security is generally two parameter first one is communication and second one is data automation. The network security field is directly or indirectly linked with the concept of data encryption. The development in this network security has taken us to a level that from signature again we came back to thumb print. For example maintain the data secure we use the lock system which is a finger print type. This technology helps us to protect the physical data theft, but logical data theft is still problem for data transmission. This article will brief about the network security it also presents the various network security types. Those types are wired and wireless network security. Apart from the network security the following topics is also discussed in this article. Those are network security protocols and simulation tools in network security. The research problems in network security are privacy and vulnerability of data.

Detecting and identifying intrusions in a network is a challenging research area in the network security domain. Intrusion detection plays an essential role in computer network security since long. An Intrusion Detection System (IDS) is mainly used to detect an unauthorized access to a computer system or network. Moreover, it is capable to detect all types of malicious and harmful attacks in a network. The drawbacks of existing IDS are it can detect only the known attacks and it produces a large number of false alarms due to the unpredictable behavior of users and networks. It also requires extensive training sets in order to characterize the normal behavior of the nodes. In order to overcome these issues, an integration of Hidden Markov Model (HMM)–Relevance Vector Machine (RVM) algorithm namely, Probabilistic Relevance Classification (PRC) is proposed to detect intrusions in Supervisory Control and Data Acquisition (SCADA) network. Here, the power system attack dataset is used to detect the attacks in an SCADA network. In the preprocessing stage, the given data is preprocessed to segregate the relays as R1, R2, R3n and R4. Each relay contains the date, timestamp, control panel log report, relay log report, snort log report, marker, fault location, and load condition information. Then, the Boyer–Moore (BM) technique is employed to perform the string matching operation. After that, the PRC technique is implemented to classify the attack as known or unknown. The novelty of this paper is it manually trains the data and features for unknown attacks. The main intention of this work is to reduce the set of features, amount of database, and to increase the detection rate. The experimental results evaluate the performance in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GA), sensitivity, specificity, accuracy, error rate, and recall.

With the rapid development of Internet technology, computer has become an indispensable part of modern society. However, with the popularization of computer technology, the problem of network security is becoming increasingly prominent. In recent years, China's network security incidents occur frequently. As a means of attack, computer deception has seriously threatened national information security, social stability and people's life. Therefore, it is of great practical significance to study the role of computer deception in network security. Its purpose is to mislead and deceive users or systems by various means to achieve the purpose of illegally obtaining information, damaging the stability of the system or carrying out other illegal activities. Based on this, this paper aims to explore the role of computer deception in network security. Firstly, the concept, content and present situation of computer network security are expounded, and then the necessity of computer network security management and protection is analyzed. Secondly, the related technology of computer deception is introduced. Finally, the application of computer spoofing in network security is studied, and the effectiveness of computer spoofing in preventing and detecting network attacks is revealed. In addition, the ethical issues of computer deception and its potential in future cybersecurity strategies are discussed. Research shows that computer deception can not only enhance the defense mechanism, but also improve the organization's cognition and response ability to network threats, and become an indispensable security strategy. By evaluating current technologies and methods, this paper provides practical recommendations for researchers and practitioners to effectively use computer spoofing techniques to improve network security.

The dynamic nature of communication paradigms demands uninterrupted connectivity in order to achieve maximum functionality. It will take creative thinking to achieve this smoothness. It becomes clear that nonlinear analysis is a revolutionary methodology that provides original insights and solutions. In order to attain seamless connectedness, this study investigates how nonlinear analysis might be applied to revolutionise communication paradigms. A strong foundation for comprehending and improving communication systems is provided by nonlinear analysis, which has its roots in the study of complex systems. This paper delves into the fundamental concepts of nonlinear analysis and elucidates how they may be applied to the complicated dynamics of modern communication. Nonlinear analysis, which includes information theory, signal processing, and chaos theory, introduces a new paradigm in communication technology. Network congestion, signal distortion, and security holes are just a few of the issues plaguing today's communication infrastructure that cause disruptions in service. Nonlinear analysis is a useful technique for overcoming and reducing these challenges. The efficiency, dependability, and security of communication networks can all be improved by implementing nonlinear system dynamics. In this study, nonlinear analysis is applied to real-world problems in network security, wireless communication, and the Internet of Things (IoT) through several detailed case studies. Using these real-world examples, we can see how nonlinear methods can successfully alter established modes of communication and demonstrate their value. The implications of nonlinear analysis for future communication paradigms are far-reaching and extend beyond the capabilities of current technology. This essay delves into the future of communication technology, shedding light on emerging trends that may have profound impacts in the years ahead. It also examines the benefits and difficulties that come with employing nonlinear analysis to establish seamless communication. Nonlinear analysis is a potent instrument with potential applications in shifting communication paradigms, facilitating seamless connectivity, and paving the way for the creation of novel forms of communication technology. The study emphasises the significance of nonlinear techniques in addressing the challenges of modern communication systems and guiding them towards a more connected and successful future.

In order to solve wireless local area network (WLAN) security problem due to the open-wide nature of wireless radio and the improvement of computing power, a design of WLAN security program based on near field communication (NFC) is presented in this paper. In this paper, the importance of having access to handshake for WPA2 brute force is explained. The proposed design protects the four-way handshake by taking advantage of NFC short-range character to eliminate the risk of intercept. For implementation, Android system is selected as a mobile device development platform. The design is compatible with the IEEE 802.11i which ensures the massive expansion in the future. Furthermore, the design simplifies operations to improve users’ experience without much extra hardware cost and offers an option to the owner of WLAN to control the access physically, which benefits commercialization of NFC. From one perspective, this design can solve the wireless network security problem effectively.KeywordsWireless Local Area NetworkNear Field CommunicationWireless RouterDictionary AttackAndroid SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The IEEE 802.11 wireless networks are organized either in infrastructure mode or ad-hoc mode. These wireless networks are very common, because of their portability and ease of access. In infrastructure mode of wireless networks, wireless clients have to be aware of the environments due to vulnerability and various types of attacks that can made by wireless intruders. Wired Equivalent Privacy (WEP) and Wi-Fi Protected Access (WPA) security protocols are used to protect any wireless infrastructure against wireless intruders. The main problem of wireless network is to maintain the network security. Firewalls are not much capable to secure network from attacks because firewall can only detect the attacks which come from outside of the network. Therefore, Intrusion Detection System (IDS) plays an important role in network security. In general Intrusion Detection System (IDS) is used to detect unauthorized access, malicious access point and various types of wireless attacks in IEEE 802.11 infrastructure mode of wireless networks. Network based Wireless Intrusion Detection system (WIDS) is designed to detect Denial of Service (DOS) attack and Malicious Access Point (Evil Twin Attack) in Wi-Fi networks. Because DOS attack and Evil Twin Attack are the most significant attacks in IEEE 802.11 networks. In this work, a diverse detection algorithm is proposed to detect DOS attack and Evil Twin Attack which is based on number of deauthentication frames and beacon frames and some other fields. In this project, an experimental setup for wireless network infrastructure with one access point (AP) and three or four number of wireless clients are used to design Wireless Intrusion Detection System (WIDS). In that design, one client act as an attacker and another one client act as detector. In this work, Kali Linux based attack software tool is used to register wireless clients to launch attacks on the network. The set of commends are used to configure on the designed infrastructure wireless networks for testing the attacks and solutions

We live in an Internet-based world, and our personal and professional lives are more intertwined. Systems of information technology (OS). These mechanisms are frequently subjected to public scrutiny, to avoid being hacked or infected with a virus, we are all on the lookout for the greatest antivirus and anti-spyware software. Install the programme, yet the malware still exists. Our system has been hacked into. The most influential people Social Engineering is a type of system attack. These attacks aim to tricking individuals or businesses to perform acts that benefit the attackers or to provide them with sensitive information such as social security numbers, medical records, and passwords. Because it takes advantage of the inherent human desire to trust, social engineering is one of the most difficult problems in network security. This paper provides a comprehensive overview of social engineering attacks, including classifications, detection and prevention procedures.KeywordsAttacksSecuritySocial engineeringCyber securityPhishingPrivacyKnowledge worker

The development of big data has epromoted the development of Internet technology, but it has brought more network security and privacy problems. Therefore, how to solve network security problems is the main research direction of current network technology development. In order to deal with the harm of network attacks to personal privacy security, this paper studies and proposes an RFID mutual authentication protocol against exhaustive attacks based on improved Hash function, and proposes a security proof based on BAN logic rules. At the same time, to enhance the computing resources of the improved protocol, this paper proposes an improved authentication query protocol for multi-source RFID tags. In the performance analysis, when the distance between the reader and the tag reaches 10 m, the improved protocol can still be higher than 90%. The application test shows that the improved protocol proposed in the study is capable of resisting exhaustive attacks, its execution time is short, and it is less affected by the number of tags. The above results show that in the context of big data, the improved RFID mutual authentication protocol proposed by the research against network exhaustive attacks has a more significant defense effect, can effectively protect user privacy, and has a greater reference value in network security research.

With the rapid development of network technology, network security problems are gradually increasing, and the network attack situation is very severe. In a complex attack scenario, timely detection of potential attack behaviors and timely identification and pre-judgment of attack intentions are important components of security risks. However, the attack behavior in the network presents complexity, multi-step and uncertainty, which brings new technical challenges to attack intent analysis. Aiming at the problem that the attack intention of multi-step complex attack is difficult to identify, this paper proposes an attack intention analysis method based on attack path graph. Firstly, aiming at the multi-step complex attack behavior analysis problem, the key asset assessment technology is used to find out the key assets in the network system, and the hypothetical attack intention is generated according to the security protection requirements of the network system. Then, it is difficult to manually construct the attack path map in the large-scale network, and the automatic generation of the attack path map is realized. Finally, a method of network attack intent identification is proposed and a calculation method of attack intent probability is designed, which improves the efficiency and accuracy of attack intent recognition.

ABSTRACTTraditional protection measures are difficult to cope with in complex and changeable network attacks. This paper presents an innovative construction of a hybrid model based on the combination of graph neural network (GNN) and random forest (RF), and uses an adaptive feature selection mechanism and multi‐task learning strategy to carry out network security big data prediction analysis. Through experiments on CICIDS 2017 and KDD Cup 1999 datasets, the results show that the model has excellent performance in detecting various types of attacks, with an accuracy of up to 0.94 in DoS attack detection of the CICIDS 2017 dataset, and maintains strong robustness in different noise environments. The model performs outstandingly in training efficiency, generalization ability, and multi‐task learning performance, and significantly improves the accuracy and efficiency of network security threat prediction. Research shows that this model has opened up a new path for network security protection, has important value in dealing with complex network attack scenarios, and provides strong support for the development of the network security field.

In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph (DUCAG) model and a Causal Chain-based Risk Probability Calculation (CCRP) algorithm. The DUCAG model is constructed to represent the uncertain underlying causalities among network attack events, and the CCRP algorithm aims at dynamically updating the causality weights among different network attack events and attacker hypotheses based on alarm information and causal chain reasoning process. By causality simplification and causality reasoning methods, the CCRP efficiently predicts the attacker behaviors and potential attack likelihood under uncertain time-varying attack situations, and is robust to the incompleteness and redundancy in alarm information. Four experiments under different attack scenarios demonstrate that, the DUCAG model can effectively characterize and predict the complex and uncertain attack causalities, in a manner of high time efficiency. The proposed method has application significance on cloud computing platforms by dynamically evaluating network security status, predicting the future behaviors of attackers, and assisting in adjusting network defense strategies.

Network security has attracted more and more attention. In order to improve the defense ability against network attacks, researchers have put forward many solutions, and an intrusion detection system is one of them. In this paper, the NSL-KDD data set contains four types of attacks (U2R, R2L, Probe, DoS) and non-attacks. By adding the particle method, the density of each data is calculated and combined with the characteristic quantity of each data, and the data is divided into five categories. In order to improve the computational efficiency and remove the features of low correlation and high interference, we reduce the dimension of data features. The attack detection rates of U2R, R2L, Probe, and DoS, are calculated by machine learning methods such as DT, NN, SVM, K-NN, and NB. Experiments show that the particle method improves the detection rate of four types of attacks.

The popularity of the Internet makes the network develop rapidly. However, the network security threat is more complex and hidden. The traditional network security alarm system has the problems of low accuracy and low efficiency when dealing with huge redundant data. Therefore, the research comprehensively considers the network security problems, proposes a network security situational awareness model for asset information protection combined with knowledge graph, establishes an asset-based network security knowledge graph, utilizes attribute graphs to complete the network attack scenario discovery and network situational understanding, and verifies the effectiveness and superiority of the model. The experimental results show that the research-proposed model detects an average of 9706 attacks out of 10000 attacks. For 100 high-risk level attacks, the number of detections is higher than 98. The average correctness, recall, and false alarm rates of the research proposed model are 99.48%, 99.04%, and 0.86%, respectively. In addition, when the model is running, its maximum memory usage is only 22.67%, and the time to complete the attack detection at the same time is 258.4s, both of which are much lower than the comparison algorithms. Finally, the research-proposed model is able to effectively reflect the impact of attack events on the posture of asset nodes. The proposed cybersecurity situational awareness model is of great theoretical and practical significance for improving organizational cybersecurity, innovating cybersecurity solutions, and maintaining the security of asset information in the digital era.

Nowadays, the application of the Internet is more and more extensive, but also exposed many problems. Network attacks are gradually penetrating into all kinds of network terminals, and the attack means are more and more sophisticated and covert. Network paralysis, data and user information leakage and other security problems are increasingly prominent. The increasingly dangerous network security situation brings new challenges to the traditional single point and single source security defense system. At the same time, the network crime has caused more and more losses to the personal and property safety of the state, enterprises and citizens. Network security has become the most concerned problem of citizens. In order to clarify the influence of different factors on computer network security, construct effective solutions, and create a more secure environment for the development and expansion of network information technology, this paper analyzes the factors affecting computer network information security based on genetic algorithm, and proposes a BP neural network algorithm based on genetic algorithm optimization, which is used in network security optimization. The experimental results show that the accuracy of BP neural network algorithm optimized by genetic algorithm is as high as 86.5%. The combination of genetic algorithm and neural network can effectively improve the reliability of the network.KeywordsGenetic algorithmComputerNetwork securityApplication research