Biblio

One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.

This paper investigates the event-triggered control (ETC) problem for stochastic networked control systems (NCSs) with exogenous disturbances and Denial-of-Service (DoS) attacks. The ETC strategy is proposed to reduce the utilization of network resource while defending the DoS attacks. Based on the introduced ETC strategy, sufficient conditions, which rely on the frequency and duration properties of DoS attacks, are obtained to achieve the stochastic input-to-state stability and Zeno-freeness of the ETC stochastic NCSs. An example of air vehicle system is given to explain the effectiveness of proposed ETC strategy.

The current network security situation of the electric power dispatching and control system is becoming more and more severe. On the basis of the original network security management platform, to increase the collection of network security data information and improve the network security analysis ability, this article proposes the electric power dispatching and control system network security situation awareness analysis module. The perception layer accesses multi-source heterogeneous data sources. Upwards through the top layer, data standardization will be introduced, who realizes data support for security situation analysis, and forms an association mapping with situation awareness elements such as health situation, attack situation, behavior situation, and operation situation. The overall effect is achieving the construction goals of "full control of equipment status, source of security attacks can be traced, operational risks are identifiable, and abnormal behaviors can be found.".

The rapid growth of IPv6 traffic makes security issues become more important. This paper proposes an IPv6 network security system that integrates signature-based Intrusion Detection Systems (IDS) and machine learning classification technologies to improve the accuracy of IPv6 denial-of-service (DoS) attacks detection. In addition, this paper has also enhanced IPv6 network security defense capabilities through software-defined networking (SDN) and network function virtualization (NFV) technologies. The experimental results prove that the detection and defense mechanisms proposed in this paper can effectively strengthen IPv6 network security.

In recent years, IPv6 and its application are more and more widely deployed. Most network devices support and open IPv6 protocol stack. The security of IPv6 network is also concerned. In the IPv6 network security technology, address scanning is a key and difficult point. This paper presents a TGAs-based IPv6 address scanning method. It takes the known alive IPv6 addresses as input, and then utilizes the information entropy and clustering technology to mine the distribution law of seed addresses. Then, the final optimized target address set can be obtained by expanding from the seed address set according to the distribution law. Experimental results show that it can effectively improve the efficiency of IPv6 address scanning.

With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two layers with the cloud and cellular networks coexisting with the underlaid device-to-device communications. The connectivity of IoTs plays an important role in information dissemination for mission-critical and civilian applications. However, IoT communication networks are vulnerable to cyber attacks including the denial-of-service and jamming attacks, resulting in link removals in the IoT network. In this paper, we develop a heterogeneous IoT network design framework in which a network designer can add links to provide additional communication paths between two nodes or secure links against attacks by investing resources. By anticipating the strategic cyber attacks, we characterize the optimal design of the secure IoT network by first providing a lower bound on the number of links a secure network requires for a given budget of protected links, and then developing a method to construct networks that satisfy the heterogeneous network design specifications. Therefore, each layer of the designed heterogeneous IoT network is resistant to a predefined level of malicious attacks with minimum resources. Finally, we provide case studies on the Internet of Battlefield Things to corroborate and illustrate our obtained results.

Moving target defense technology is one of the revolutionary techniques that is “changing the rules of the game” in the field of network technology, according to recent propositions from the US Science and Technology Commission. Building upon a recently-developed approach called Self Cleansing Intrusion Tolerance (SCIT), this paper proposes a moving target defense system that is based on server switching and cleaning. A protected object is maneuvered to improve its safety by exploiting software diversity and thereby introducing randomness and unpredictability into the system. Experimental results show that the improved system increases the difficulty of attack and significantly reduces the likelihood of a system being invaded, thus serving to enhance system security.

Deep Neural Networks (DNN) have strong capabilities of memories, feature identifications and automatic analyses, solving various complex problems. However, DNN classifiers have obvious fragility that adding several unnoticeable perturbations to the original examples will lead to the errors in the classifier identification. In the field of communications, the adversarial examples will greatly reduce the accuracy of the signal identification, causing great information security risks. Considering the adversarial examples pose a serious threat to the security of the DNN models, studying their generation mechanisms and testing their attack effects are critical to ensuring the information security of the communication networks. This paper will study the generation of the adversarial examples and the influences of the adversarial examples on the accuracy of the DNN-based communication signal identification. Meanwhile, this paper will study the influences of the adversarial examples under the white-box models and black-box models, and explore the adversarial attack influences of the factors such as perturbation levels and iterative steps. The insights of this study would be helpful for ensuring the security of information networks and designing robust DNN communication networks.

Network security is critical to be able to maintain the information, especially on servers that store a lot of information; several types of attacks can occur on servers, including brute force and DDoS attacks; in the case study in this research, there are four servers used so that a network security system that can synchronize with each other so that when one server detects an attack, another server can take precautions before the same attack occurs on another server.fail2ban is a network security tool that uses the IDPS (Intrusion Detection and Prevention System) method which is an extension of the IDS (Intrusion Detection System) combined with IP tables so that it can detect and prevent suspicious activities on a network, fail2ban automatically default can only run on one server without being able to synchronize on other servers. With a network security system that can run on multiple servers, the attack prevention process can be done faster because when one server detects an attack, another server will take precautions by retrieving the information that has entered the collector database synchronizing all servers other servers can prevent attacks before an attack occurs on that server.

Considering the problem of network security under the background of big data, the clustering analysis algorithms can be utilized to improve the correctness of network intrusion detection models for security management. As a kind of iterative clustering analysis algorithm, K-means algorithm is not only simple but also efficient, so it is widely used. However, the traditional K-means algorithm cannot well solve the network security problem when facing big data due to its high complexity and limited processing ability. In this case, this paper proposes to optimize the traditional K-means algorithm based on the Spark platform and deploy the optimized clustering analysis algorithm in the distributed architecture, so as to improve the efficiency of clustering algorithm for network intrusion detection in big data environment. The experimental result shows that, compared with the traditional K-means algorithm, the efficiency of the optimized K-means algorithm using a Spark-based method is significantly improved in the running time.

Connected vehicles as a promising concept of Intelligent Transportation System (ITS), are a potential solution to address some of the existing challenges of emission, traffic congestion as well as fuel consumption. To achieve these goals, connectivity among vehicles through the wireless communication network is essential. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with purposes of disrupting the performance of the connected vehicles, lead to catastrophic collision and traffic congestion. In this study, we consider a platoon of connected vehicles equipped with Cooperative Adaptive Cruise Control (CACC) which are subjected to a specific type of cyber-attack namely "False Data Injection" attack. We developed a novel method to model the attack with ghost vehicles injected into the connected vehicles network to disrupt the performance of the whole system. To aid the analysis, we use a Partial Differential Equation (PDE) model. Furthermore, we present a PDE model-based diagnostics scheme capable of detecting the false data injection attack and isolating the injection point of the attack in the platoon system. The proposed scheme is designed based on a PDE observer with measured velocity and acceleration feedback. Lyapunov stability theory has been utilized to verify the analytically convergence of the observer under no attack scenario. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation study.

Robot networks are susceptible to fail under the presence of malicious or defective robots. Resilient networks in the literature require high connectivity and large communication ranges, leading to high energy consumption in the communication network. This paper presents robot formations with guaranteed resiliency that use smaller communication ranges than previous results in the literature. The formations can be built on triangular and square lattices in the plane, and cubic lattices in the three-dimensional space. We support our theoretical framework with simulations.

As the hottest frontier technology in the field of artificial intelligence, machine learning is subverting various industries step by step. In the future, it will penetrate all aspects of our lives and become an indispensable technology around us. Among them, network security is an area where machine learning can show off its strengths. Among many network security problems, privacy protection is a more difficult problem, so it needs more introduction of new technologies, new methods and new ideas such as machine learning to help solve some problems. The research contents for this include four parts: an overview of machine learning, the significance of machine learning in network security, the application process of machine learning in network security research, and the application of machine learning in privacy protection. It focuses on the issues related to privacy protection and proposes to combine the most advanced matching algorithm in deep learning methods with information theory data protection technology, so as to introduce it into biometric authentication. While ensuring that the loss of matching accuracy is minimal, a high-standard privacy protection algorithm is concluded, which enables businesses, government entities, and end users to more widely accept privacy protection technology.

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNN), which has caused severe problems in the network security field. Ensuring the accuracy of steganalysis is becoming increasingly difficult. In this paper, we designed a two-channel generative adversarial network (TGAN), inspired by the idea of adversarial training that is based on our previous work. The TGAN consisted of three parts: The first hiding network had two input channels and one output channel. For the second extraction network, the input was a hidden image embedded with the secret image. The third detecting network had two input channels and one output channel. Experimental results on two independent image data sets showed that the proposed TGAN performed well and had better detecting capability compared to other algorithms, thus having important theoretical significance and engineering value.

Artificial intelligence has been widely used in industries such as smart manufacturing, medical care and home furnishings. Among them, the value of the application in communication security is very important. This paper makes a further exploration of the artificial intelligence technology and its application, and gives a detailed analysis of its development, standardization and the application.

Using artificial intelligence technology to help network security has become a major trend. At present, major countries in the world have successively invested R & D force in the attack and defense of automatic network based on artificial intelligence. The U.S. Navy, the U.S. air force, and the DOD strategic capabilities office have invested heavily in the development of artificial intelligence network defense systems. DARPA launched the network security challenge (CGC) to promote the development of automatic attack system based on artificial intelligence. In the 2016 Defcon final, mayhem (the champion of CGC in 2014), an automatic attack team, participated in the competition with 14 human teams and once defeated two human teams, indicating that the automatic attack method generated by artificial intelligence system can scan system defects and find loopholes faster and more effectively than human beings. Japan's defense ministry also announced recently that in order to strengthen the ability to respond to network attacks, it will introduce artificial intelligence technology into the information communication network defense system of Japan's self defense force. It can be predicted that the deepening application of artificial intelligence in the field of network attack and defense may bring about revolutionary changes and increase the imbalance of the strategic strength of cyberspace in various countries. Therefore, it is necessary to systematically investigate the current situation of network attack and defense based on artificial intelligence at home and abroad, comprehensively analyze the development trend of relevant technologies at home and abroad, deeply analyze the development outline and specification of artificial intelligence attack and defense around the world, and refine the application status and future prospects of artificial intelligence attack and defense, so as to promote the development of artificial intelligence attack and Defense Technology in China and protect the core interests of cyberspace, of great significance

At present, our life is almost inseparable from the network, the network provides a lot of convenience for our life. However, a variety of network security incidents occur very frequently. In recent years, with the continuous development of neural network technology, more and more researchers have applied neural network to intrusion detection, which has developed into a new research direction in intrusion detection. As long as the neural network is provided with input data including network data packets, through the process of self-learning, the neural network can separate abnormal data features and effectively detect abnormal data. Therefore, the article innovatively proposes an intrusion detection method based on deep convolutional neural networks (CNN), which is used to test on public data sets. The results show that the model has a higher accuracy rate and a lower false negative rate than traditional intrusion detection methods.

Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability.

The dynamicity and complexity of clouds highlight the importance of automated root cause analysis solutions for explaining what might have caused a security incident. Most existing works focus on either locating malfunctioning clouds components, e.g., switches, or tracing changes at lower abstraction levels, e.g., system calls. On the other hand, a management-level solution can provide a big picture about the root cause in a more scalable manner. In this paper, we propose DOMINOCATCHER, a novel provenance-based solution for explaining the root cause of security incidents in terms of management operations in clouds. Specifically, we first define our provenance model to capture the interdependencies between cloud management operations, virtual resources and inputs. Based on this model, we design a framework to intercept cloud management operations and to extract and prune provenance metadata. We implement DOMINOCATCHER on OpenStack platform as an attached middleware and validate its effectiveness using security incidents based on real-world attacks. We also evaluate the performance through experiments on our testbed, and the results demonstrate that DOMINOCATCHER incurs insignificant overhead and is scalable for clouds.

Network security policies contain requirements - including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.

A common way to characterize security enforcement mechanisms is based on the time at which they operate. Mechanisms operating before a program's execution are static mechanisms, and mechanisms operating during a program's execution are dynamic mechanisms. This paper introduces a different perspective and classifies mechanisms based on the granularity of program code that they monitor. Classifying mechanisms in this way provides a unified view of security mechanisms and shows that all security mechanisms can be encoded as dynamic mechanisms that operate at different levels of program code granularity. The practicality of the approach is demonstrated through a prototype implementation of a framework for enforcing security policies at various levels of code granularity on Java bytecode applications.

Packet classification is used to determine the behavior of incoming packets to network devices. Because it is achieved using a linear search on a classification rule list, a larger number of rules leads to a longer communication latency. To decrease this latency, the problem is generalized as Optimal Rule Ordering (ORO), which aims to identify the order of rules that minimizes the classification latency caused by packet classification while preserving the classification policy. Because ORO is known to be NP-complete by Hamed and Al-Shaer [Dynamic rule-ordering optimization for high-speed firewall filtering, ASIACCS (2006) 332-342], various heuristics for ORO have been proposed. Sub-graph merging (SGM) by Tapdiya and Fulp [Towards optimal firewall rule ordering utilizing directed acyclical graphs, ICCCN (2009) 1-6] is the state of the art heuristic algorithm for ORO. In this paper, we propose a novel heuristic method for ORO. Although most heuristics try to recursively determine the maximum-weight rule and move it as far as possible to an upper position, our algorithm pairs rules that cause policy violations until there are no such rules to simply sort the rules by these weights. Our algorithm markedly decreases the classification latency and reordering time compared with SGM in experiments. The sets consisting of thousands of rules that require one or more hours for reordering by SGM can be reordered by the proposed method within one minute.

In the era of universal digitization, ensuring network and data security is extremely important. As a part of the Regional Center for Cybersecurity initiative, a three-stage machine learning network security solution is being developed and will be deployed in March 2021. The solution consists of prevention, monitoring, and curation stages. As prevention, we utilize Natural Language Processing to extract the security-related information from social media, news portals, and darknet. A deep learning architecture is used to monitor the network in real-time and detect any abnormal traffic. A combination of regular expressions, pattern recognition, and heuristics are applied to the abuse reports to automatically identify intrusions that passed other security solutions. The lessons learned from the ongoing development of the system, alongside the results, extensive analysis, and discussion is provided. Additionally, a cybersecurity-related corpus is described and published within this work.

With the rapid development of smart grids and the continuous deepening of informatization, while realizing remote telemetry and remote control of massive data-based grid operation, electricity information network security problems have become more serious and prominent. A method for electricity information network security situation prediction method based on improved deep belief network is proposed in this paper. Firstly, the affinity propagation clustering algorithm is used to determine the depth of the deep belief network and the number of hidden layer nodes based on sample parameters. Secondly, continuously adjust the scaling factor and crossover probability in the differential evolution algorithm according to the population similarity. Finally, a chaotic search method is used to perform a second search for the best individuals and similarity centers of each generation of the population. Simulation experiments show that the proposed algorithm not only enhances the generalization ability of electricity information network security situation prediction, but also has higher prediction accuracy.

Aiming at the requirements of network access control, illegal outreach control, identity authentication, security monitoring and application system access control of information network, an integrated network access and behavior control model based on security policy is established. In this model, the network access and behavior management control process is implemented through abstract policy configuration, network device and application server, so that management has device-independent abstraction, and management simplification, flexibility and automation are improved. On this basis, a general framework of policy-based access and behavior management control is established. Finally, an example is given to illustrate the method of device connection, data drive and fusion based on policy-based network access and behavior management control.