Named Data Network Security - The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20\%. At the amount of interest in 1000 about more than 30\%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests.

Named Data Network Security - With the growing recognition that current Internet protocols have significant security flaws; several ongoing research projects are attempting to design potential next-generation Internet architectures to eliminate flaws made in the past. These projects are attempting to address privacy and security as their essential parameters. NDN (Named Data Networking) is a new networking paradigm that is being investigated as a potential alternative for the present host-centric IP-based Internet architecture. It concentrates on content delivery, which is probably underserved by IP, and it prioritizes security and privacy. NDN must be resistant to present and upcoming threats in order to become a feasible Internet framework. DDoS (Distributed Denial of Service) attacks are serious attacks that have the potential to interrupt servers, systems, or application layers. Due to the probability of this attack, the network security environment is made susceptible. The resilience of any new architecture against the DDoS attacks which afflict today s Internet is a critical concern that demands comprehensive consideration. As a result, research on feature selection approaches was conducted in order to use machine learning techniques to identify DDoS attacks in NDN. In this research, features were chosen using the Information Gain and Data Reduction approach with the aid of the WEKA machine learning tool to identify DDoS attacks. The dataset was tested using KNearest Neighbor (KNN), Decision Table, and Artificial Neural Network (ANN) algorithms to categorize the selected features. Experimental results shows that Decision Table classifier outperforms well when compared to other classification algorithms with the with the accuracy of 85.42\% and obtained highest precision and recall score with 0.876 and 0.854 respectively when compared to the other classification techniques.

Named Data Network Security - With the continuous development of network technology as well as science and technology, artificial intelligence technology and its related scientific and technological applications, in this process, were born. Among them, artificial intelligence technology has been widely used in information detection as well as data processing, and has remained one of the current hot research topics. Those research on artificial intelligence, recently, has focused on the application of network security processing of data as well as fault diagnosis and anomaly detection. This paper analyzes, aiming at the network security detection of students real name data, the relevant artificial intelligence technology and builds the model. In this process, this paper firstly introduces and analyzes some shortcomings of clustering algorithm as well as mean algorithm, and then proposes a cloning algorithm to obtain the global optimal solution. This paper, on this basis, constructs a network security model of student real name data information processing based on trust principle and trust model.

Moving Target Defense - Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy.

Moving Target Defense - False Data Injection Attack(FDIA) is a typical network attack, which can bypass the Bad Data Detection(BDD) and affect State Estimation(SE), the estimation results is vital for power system, thus posing a great threat to the security of power system. In this paper, a new defense scheme is proposed, which is based on flexible switching of spare lines. By switching on the spare lines of some working transmission lines flexibly, the transmission line parameters in the power system topology can be changed, so as to reduce the possibility of FDIA. The impact of switching spare lines on power system operation and FDIA by ergodic method is analyzed. An optimization algorithm is designed to find the least system generator cost for power grid operator and the least attack space for attackers, this algorithm is tested in the IEEE 5-bus system and IEEE 30-bus system, and the results show that the scheme has a good performance in resisting FDIA.

Moving Target Defense - The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers’ speed by at least 78\% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times.

Multifactor Authentication - The article describes the development and integrated implementation of software modules of photo and video identification system, the system of user voice recognition by 12 parameters, neural network weights, Euclidean distance comparison of real numbers of arrays. The user s biometric data is encrypted and stored in the target folder. Based on the generated data set was developed and proposed a method for synthesizing the parameters of the mathematical model of convolutional neural network represented in the form of an array of real numbers, which are unique identifiers of the user of a personal computer. The training of the training model of multifactor authentication is implemented using categorical cross-entropy. The training sample is generated by adding distorted images by changing the receptive fields of the convolutional neural network. The authors have studied and applied features of simulation modeling of user authorization systems. The main goal of the study is to provide the necessary level of security of user accounts of personal devices. The task of this study is the software implementation of the synthesis of the mathematical model and the training neural network, necessary to provide the maximum level of protection of the user operating system of the device. The result of the research is the developed mathematical model of the software complex of multifactor authentication using biometric technologies, available for users of personal computers and automated workplaces of enterprises.

Multifactor Authentication - Authentication is a mandatory factor in network security since decades. Conventional authentication schemes failed to improve system’s security, performance and scalability thus, two-factor, three factor and multifactor authentication schemes are developed. As technology grows, from single server authentication to multiserver authentication schemes and protocols are emerged. Single to multifactor authentication can be used as per the aspect and field of study. Different aspects may use different cryptographic schemes, key agreement to improve security, performance and scalability.

Multifactor Authentication - Today, with the rapid development of the information society and the increasingly complex computer network environment, multi-factor authentication, as one of the security protection technologies, plays an important role in both IT science and business. How to safely complete multi-factor authentication without affecting user experience has attracted extensive attention from researchers in the field of business security protection and network security. The purpose of this paper is to apply multi-factor authentication technology to enterprise security protection systems, develop and design a security protection technology based on multi-factor authentication dynamic authorization, and provide enterprises with unified identity management and authority management methods. The cornerstone of trust and security to ensure uninterrupted and stable operation of users. The original master key k is subjected to secondary multi-factor processing, which enhances the user s authentication ability and effectively avoids the risk of easy password theft and disguised identity. In order to meet the given VoIP security requirements, a SIP multi-factor authentication protocol is proposed for the VoIP environment by using the multi-factor authentication technology to solve the security problem. The performance test results show that due to the influence of data encryption and decryption, the response time of the encrypted database is 100s longer than that of the unencrypted one, but the growth rate is 10\% smaller than that of the unencrypted one. Therefore, the performance of this scheme is better when the amount of data is larger.

Middleware Security - Online advertisements are a significant element of the Internet ecosystem. Businesses monitor their customers via pushing advertising (Ads). Within minutes, cybercriminals try to defraud and steal data through advertisements. Therefore, the issue of ads must be solved. Ads are obtrusive, a security risk, and they hinder performance and efficiency. Hence, the goal is to create an ad-blocker that would operate across the entire network and prevent advertisement on any website s web pages. To put it another way, it s a little computer with such a SoC (System - On - chip) also referred to as a Raspberry Pi that is merged with a networking system, for which we need to retrain the advertisements. On the home network, software named Pi Hole is used to block websites with advertisements. Any network traffic that passes via devices connected to the home network now passes through the Pi. As a result, the adverts are finally checked out during the Raspberry Pi before they reach the users machine and they will be blocked.

Middleware Security - Securing IoT networks has been one of recent most active research topics. However, unlike traditional network security, where the emphasis is given on the core network, IoT networks are mostly investigated from the data standpoint. Lightweight data transmission protocols, such as Message Queue Telemetry Transport (MQTT), are often deployed for data-sharing and device authentication due to limited onboard resources. This paper presents the MQTT protocol’s security vulnerabilities by incorporating Elliptic Curve Cryptographybased (ECC-based) security to improve confidentiality issues. We used commercially off-the-shelf (COTS) devices such as Raspberry Pi to build a simplified network topology that connects IoT devices in our smart home laboratory. The results illustrate an ECC-based security application in confidentiality increase of 70.65\% from 29.35\% in time parameter during publish/subscribe communication protocol for the smart home.

MANET Security - The detection and maintenance of the pathway from the source to the destination or from one node to another node is the major role played by the nodes in the MANET. During their period, nodes arrive or leave the network, and endlessly modify their comparative location. The dynamic nature introduces several security issues. Secure routing protocol is a significant area for attaining better security in the network by keeping the routing protocols against attacks. Thus, this research work focuses on developing a secure routing protocol for MAN ET. Here, a dynamic anomaly detection scheme has proposed to detect against malicious attacks in the network. This scheme has been incorporated with AODV protocol to enhance the performance of AODV in disseminating packets to target node. In this research work Protected AODV (PAODV) is protocol is introduced to identify the false alarm node in the network and route path for reliable communication between the source to destination. Simulation results it shows the detection rate, Packet drop rate and delay is minimized compare to the existing technique.

MANET Security - Recently, the mobile ad hoc network (MANET) has enjoyed a great reputation thanks to its advantages such as: high performance, no expensive infrastructure to install, use of unlicensed frequency spectrum, and fast distribution of information around the transmitter. But the topology of MANETs attracts the attention of several attacks. Although authentication and encryption techniques can provide some protection, especially by minimizing the number of intrusions, such cryptographic techniques do not work effectively in the case of unseen or unknown attacks. In this case, the machine learning approach is successful to detect unfamiliar intrusive behavior. Security methodologies in MANETs mainly focus on eliminating malicious attacks, misbehaving nodes, and providing secure routing.

MANET Security - The current stady is confined in proposing a reputation based approach for detecting malicious activity where past activities of each node is recorded for future reference. It has been regarded that the Mobile ad-hoc network commonly called as (MANET) is stated as the critical wireless network on the mobile devices using self related assets. Security considered as the main challenge in MANET. Many existing work has done on the basis of detecting attacks by using various approaches like Intrusion Detection, Bait detection, Cooperative malicious detection and so on. In this paper some approaches for identifying malicious nodes has been discussed. But this Reputation based approach mainly focuses on sleuthing the critcal nodes on the trusted path than the shortest path. Each node will record the activity of its own like data received from and Transferred to information. As soon as a node update its activity it is verified and a trust factor is assigned. By comparing the assigned trust factor a list of suspicious or malicious node is created..

MANET Security - Remote correspondence innovations are assuming a critical part in the plan and execution of Mobile Ad hoc Network (MANET). The portrayal of MANET, for example, dynamism in geography, restricted transfer speed and power usage expands the unlicensed correspondence advancements and intricacies in existing conventions. This paper analyzes the current and not so distant future Wireless correspondence Technologies in the 2.4 GHz band. Additionally, this paper thinks about the features and limits of those advances lastly closes with the need for the improvement of reasonable brought together convention for existing and future remote advances. It has been considered that the overview and correlation introduced in this paper would help specialists and application engineers in choosing a fitting innovation for MANET administrations.

MANET Security - Mobile ad hoc networks can expand access networks service zones and offer wireless to previously unconnected or spotty areas. Ad hoc networking faces transmission failures limited wireless range, disguised terminal faults and packet losses, mobility-induced route alterations, and battery constraints. A network layer metric shows total network performance. Ad-hoc networking provides access networks, dynamic multi-hop architecture, and peer-to-peer communication. In MANET, each node acts as a router, determining the optimum route by travelling through other nodes. MANET includes dynamic topology, fast deployment, energy-restricted operation, and adjustable capacity and bandwidth. Dynamic MANET increases security vulnerabilities. Researchers have employed intrusion detection, routing, and other techniques to provide security solutions. Current technologies can t safeguard network nodes. In a hostile environment, network performance decreases as nodes increase. This paper presents a reliable and energy-efficient Firefly Energy Optimized Routing (IFEOR)-based routing method to maximise MANET data transmission energy. IFEOR measures MANET firefly light intensity to improve routing stability. The route path s energy consumption determines the firefly s brightness during MANET data packet transfer. Adopting IFEOR enhanced packet delivery rates and routing overheads. End-to-end delay isn t reduced since nodes in a route may be idle before sending a message. Unused nodes use energy.

Microelectronics Security - With the increasing improvement of network security technology, network security management is forming a closedloop process of transitioning from post-fire fighting to prechecking, real-time monitoring and protection, and postdisposal reinforcement. This paper introduces a new system based on network asset risk assessment and network asset security protection, which is capable of detecting unrepaired security vulnerabilities in network assets and monitoring users’ assets for compliance, and notifying them if there are problems, and also has SYSLOG asset upload technology for uploading asset changes.

Malware Analysis - The rapid development of network information technology, individual’s information networks security has become a very critical issue in our daily life. Therefore, it is necessary to study the malware propagation model system. In this paper, the traditional integer order malware propagation model system is extended to the field of fractional-order. Then we analyze the asymptotic stability of the fractional-order malware propagation model system when the equilibrium point is the origin and the time delay is 0. Next, the asymptotic stability and bifurcation analysis of the fractional-order malware propagation model system when the equilibrium point is the origin and the time delay is not 0 are carried out. Moreover, we study the asymptotic stability of the fractional-order malware propagation model system with an interior equilibrium point. In the end, so as to verify our theoretical results, many numerical simulations are provided.

Information Reuse and Security - New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58\% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

Information Reuse and Security - Successive approximation register analog-to-digital converter (SAR ADC) is widely adopted in the Internet of Things (IoT) systems due to its simple structure and high energy efficiency. Unfortunately, SAR ADC dissipates various and unique power features when it converts different input signals, leading to severe vulnerability to power side-channel attack (PSA). The adversary can accurately derive the input signal by only measuring the power information from the analog supply pin (AVDD), digital supply pin (DVDD), and/or reference pin (Ref) which feed to the trained machine learning models. This paper first presents the detailed mathematical analysis of power side-channel attack (PSA) to SAR ADC, concluding that the power information from AVDD is the most vulnerable to PSA compared with the other supply pin. Then, an LSB-reused protection technique is proposed, which utilizes the characteristic of LSB from the SAR ADC itself to protect against PSA. Lastly, this technique is verified in a 12-bit 5 MS/s secure SAR ADC implemented in 65nm technology. By using the current waveform from AVDD, the adopted convolutional neural network (CNN) algorithms can achieve \textgreater99\% prediction accuracy from LSB to MSB in the SAR ADC without protection. With the proposed protection, the bit-wise accuracy drops to around 50\%.

Information Reuse and Security - The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology

Information Reuse and Security - With the development of software defined network and network function virtualization, network operators can flexibly deploy service function chains (SFC) to provide network security services more than before according to the network security requirements of business systems. At present, most research on verifying the correctness of SFC is based on whether the logical sequence between service functions (SF) in SFC is correct before deployment, and there is less research on verifying the correctness after SFC deployment. Therefore, this paper proposes a method of using Colored Petri Net (CPN) to establish a verification model offline and verify whether each SF deployment in SFC is correct after online deployment. After the SFC deployment is completed, the information is obtained online and input into the established model for verification. The experimental results show that the SFC correctness verification method proposed in this paper can effectively verify whether each SF in the deployed SFC is deployed correctly. In this process, the correctness of SF model is verified by using SF model in the model library, and the model reuse technology is preliminarily discussed.

Intrusion Intolerance - Network intrusion detection technology has developed for more than ten years, but due to the network intrusion is complex and variable, it is impossible to determine the function of network intrusion behaviour. Combined with the research on the intrusion detection technology of the cluster system, the network security intrusion detection and mass alarms are realized. Method: This article starts with an intrusion detection system, which introduces the classification and workflow. The structure and working principle of intrusion detection system based on protocol analysis technology are analysed in detail. Results: With the help of the existing network intrusion detection in the network laboratory, the Synflood attack has successfully detected, which verified the flexibility, accuracy, and high reliability of the protocol analysis technology. Conclusion: The high-performance cluster-computing platform designed in this paper is already available. The focus of future work will strengthen the functions of the cluster-computing platform, enhancing stability, and improving and optimizing the fault tolerance mechanism.

Malware Analysis and Graph Theory - Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7\%; the precision is 96.6\%; the recall is 96.8\%; the F1-score is 96.4\%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

Malware Analysis - The rapid development of network information technology, individual’s information networks security has become a very critical issue in our daily life. Therefore, it is necessary to study the malware propagation model system. In this paper, the traditional integer order malware propagation model system is extended to the field of fractional-order. Then we analyze the asymptotic stability of the fractional-order malware propagation model system when the equilibrium point is the origin and the time delay is 0. Next, the asymptotic stability and bifurcation analysis of the fractional-order malware propagation model system when the equilibrium point is the origin and the time delay is not 0 are carried out. Moreover, we study the asymptotic stability of the fractional-order malware propagation model system with an interior equilibrium point. In the end, so as to verify our theoretical results, many numerical simulations are provided.