Model for cascading failures with adaptive defense in complex networks

This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology, where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them. The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures. Particularly, there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding, a costless strategy of defense for preventing cascade breakdown is proposed. It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes.

Intelligent Immunity Based Security Defense System for Multi-Access Edge Computing Network

In this paper,the security problem for the multi-access edge computing(MEC)network is researched,and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to protect the security of whole system.In the proposed security defense system,the security is protected by the intelligent immunity through three functions,identification function,learning function,and regulation function,respectively.Meanwhile,a three process-based intelligent algorithm is proposed for the intelligent immunity system.Numerical simulations are given to prove the effeteness of the proposed approach.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Network Defense Methodology: A Comparison of Defense in Depth and Defense in Breadth

The defense in depth methodology was popularized in the early 2000’s amid growing concerns for information security;this paper will address the shortcomings of early implementations. In the last two years, many supporters of the defense in depth security methodology have changed their allegiance to an offshoot method dubbed the defense in breadth methodology. A substantial portion of this paper’s body will be devoted to comparing real-world usage scenarios and discussing the flaws in each method. A major goal of this publication will be to assist readers in selecting a method that will best benefit their personal environment. Scenarios certainly exist where one method may be clearly favored;this article will help identify the factors that make one method a clear choice over another. This paper will strive not only to highlight key strengths and weaknesses for the two strategies listed, but also provide the evaluation techniques necessary for readers to apply to other popular methodologies in order to make the most appropriate personal determinations.

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,etc.Hence,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to attacks.The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats.In traditional models,the solution has been made for defending the networks,only to specific attacks.However,in real-time applications,the kind of attack that is launched by the adversary is not known.Additionally,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be considered.With that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various attacks.Moreover,for efficient model design,the work includes the part of feature evaluation of some general attacks of WSNs.The IDRM also includes determination of optimal secure paths and Node security for secure routing operations.The performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications.It is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

Black Box Adversarial Defense Based on Image Denoising and Pix2Pix

Deep Neural Networks (DNN) are widely utilized due to their outstanding performance, but the susceptibility to adversarial attacks poses significant security risks, making adversarial defense research crucial in the field of AI security. Currently, robustness defense techniques for models often rely on adversarial training, a method that tends to only defend against specific types of attacks and lacks strong generalization. In response to this challenge, this paper proposes a black-box defense method based on Image Denoising and Pix2Pix (IDP) technology. This method does not require prior knowledge of the specific attack type and eliminates the need for cumbersome adversarial training. When making predictions on unknown samples, the IDP method first undergoes denoising processing, followed by inputting the processed image into a trained Pix2Pix model for image transformation. Finally, the image generated by Pix2Pix is input into the classification model for prediction. This versatile defense approach demonstrates excellent defensive performance against common attack methods such as FGSM, I-FGSM, DeepFool, and UPSET, showcasing high flexibility and transferability. In summary, the IDP method introduces new perspectives and possibilities for adversarial sample defense, alleviating the limitations of traditional adversarial training methods and enhancing the overall robustness of models.

User Station Security Protection Method Based on Random Domain Name Detection and Active Defense

The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures

Studied in this article is whether the Bayesian Network Model (BNM) can be effectively applied to the prioritization of defense in-depth security tools and procedures and to the combining of those measures to reduce cyber threats. The methods used in this study consisted of scanning 24 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals using the Likert Scale Model for the article’s list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The defense in depth tools and procedures are then compared to see whether the Likert scale and the Bayesian Network Model could be effectively applied to prioritize and combine the measures to reduce cyber threats attacks against organizational and private computing systems. The findings of the research reject the H0 null hypothesis that BNM does not affect the relationship between the prioritization and combining of 24 Cybersecurity Article’s defense in depth tools and procedures (independent variables) and cyber threats (dependent variables).

Analysis on The Application of Data Mining Technology in Computer Network Virus Defense

With the rapid development of computer network,the society has entered the information and digital era,it plays an important role in speeding up the pace of social development and providing more convenient services for people.However, the security problem of computer network is becoming more and more serious. All kinds of network viruses pose a great threat to the security of computer network.As the most advanced data processing technology currently, data mining technology can effectively resist the invasion of network virus to computer system,and plays an important role in improving the security of the computer network.This paper starts with the concept of data mining technology and the characteristics of computer network virus,and makes an in-depth analysis on the specific application of data mining technology in the computer network virus defense.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense effectiveness.Therefore,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense strategies.the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

Adversarial Attacks and Defenses in Deep Learning

With the rapid developments of artificial intelligence(AI)and deep learning(DL)techniques,it is critical to ensure the security and robustness of the deployed algorithms.Recently,the security vulnerability of DL algorithms to adversarial samples has been widely recognized.The fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by humans.Successful implementations of adversarial attacks in real physical-world scenarios further demonstrate their practicality.Hence,adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years.In this paper,we first introduce the theoretical foundations,algorithms,and applications of adversarial attack techniques.We then describe a few research efforts on the defense techniques,which cover the broad frontier in the field.Several open problems and challenges are subsequently discussed,which we hope will provoke further research efforts in this critical area.

无虚拟通道的3D NoC ZoneDefense容错路由算法

针对3D Mesh NoC中的节点故障,提出了一种无虚拟通道容错路由算法,称为3D ZoneDefense容错路由算法(3D-ZDFT)。该算法建立在3D防御区域基础之上。3D防御区域能够提供故障体的位置信息。根据防御区域提供的故障体位置信息,3D-ZDFT可提前发现故障位置并改变转发端口,在实现容错的同时避免引入死锁。实验结果表明,与Ham FA相比,3D-ZDFT具有较低的网络延迟和更高的可靠性。面积开销分析显示,3DZDFT比Ham FA的面积开销高约3.1%。提出的算法在保证网络性能的前提下,有效地实现了容错功能。

Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

These days,deep learning and computer vision are much-growing fields in this modern world of information technology.Deep learning algorithms and computer vision have achieved great success in different applications like image classification,speech recognition,self-driving vehicles,disease diagnostics,and many more.Despite success in various applications,it is found that these learning algorithms face severe threats due to adversarial attacks.Adversarial examples are inputs like images in the computer vision field,which are intentionally slightly changed or perturbed.These changes are humanly imperceptible.But are misclassified by a model with high probability and severely affects the performance or prediction.In this scenario,we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly again.We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence.We have used MNIST and CIFAR10 datasets for experiments and analysis of our defense method.In the end,we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.

Distributed Air &Missile Defense with Spatial Grasp Technology

A high-level technology is revealed that can effectively convert any distributed system into a globally programmable machine capable of operating without central resources and self-recovering from indiscriminate damages. Integral mission scenarios in Distributed Scenario Language (DSL) can be injected from any point, runtime covering & grasping the whole system or its parts, setting operational infrastructures, and orienting local and global behavior in the way needed. Many operational scenarios can be simultaneously injected into this spatial machine from different points, cooperating or competing over the shared distributed knowledge as overlapping fields of solutions. Distributed DSL interpreter organization and benefits of using this technology for integrated air and missile defense are discussed along with programming examples in this and other fields.

Research on Cyberspace Mimic Defense Based on Dynamic Heterogeneous Redundancy Mechanism

With the rapid growth of network technology, the methods and types of cyber-attacks are increasing rapidly. Traditional static passive defense technologies focus on external security and known threats to the target system and cannot resist advanced persistent threats. To solve the situation that cyberspace security is easy to attack and difficult to defend, Chinese experts on cyberspace security proposed an innovative theory called mimic defense, it is an active defense technology that employs “Dynamic, Heterogeneous, Redundant” architecture to defense attacks. This article first briefly describes the classic network defense technology and Moving Target Defense (MTD). Next, it mainly explains in detail the principles of the mimic defense based on the DHR architecture and analyzes the attack surface of DHR architecture. This article also includes applications of mimic defense technology, such as mimic routers, and mimic web defense systems. Finally, it briefly summarizes the existing research on mimic defense, expounds the problems that need to be solved in mimic defense, and looks forward to the future development of mimic defense.

Mechanism and Defense on Malicious Code

With the explosive growth of network applications, the threat of the malicious code against network security becomes increasingly serious. In this paper we explore the mechanism of the malicious code by giving an attack model of the malicious code, and discuss the critical techniques of implementation and prevention against the malicious code. The remaining problems and emerging trends in this area are also addressed in the paper.

A NEW DYNAMIC DEFENSE MODEL BASED ON ACTIVE DECEPTION

Aiming at the traditional passive deception models,this paper constructs a Decoy Platform based on Intelligent Agent(DPIA) to realize dynamic defense.The paper explores a new dynamic defense model based on active deception,introduces its architecture,and expatiates on communication methods and security guarantee in information transference.Simulation results show that the DPIA can attract hacker agility and activity,lead abnormal traffic into it,distribute a large number of attack data,and ensure real network security.

Discussion and Research on Information Security Attack and Defense Platform Construction in Universities Based on Cloud Computing and Virtualization

This paper puts forward the plan on constructing information security attack and defense platform based on cloud computing and virtualization, provides the hardware topology structure of the platform and technical framework of the system and the experimental process and technical principle of the platform. The experiment platform can provide more than 20 attack classes. Using the virtualization technology can build hypothesized target of various types in the laboratory and diversified network structure to carry out attack and defense experiment.