Network Security Problems Research Articles

Security mesh-based optical network exploiting the double masking scheme.

In order to tackle the security and privacy problems in optical networks, a novel mesh-based optical security network exploiting double masking (DM) scheme for multipoint confidential communication is proposed and studied theoretically. For each node in the network, a pair of mutually asynchronous vertical-cavity surface-emitting lasers (VCSELs) are required as transceivers, and the delay fiber (DF) is used to set different time delays as network node markers. In this security network, the encryption of the message is implemented on the transmitter of the source node by using the DM scheme, and the encrypted message is transmitted to the receiver of the destination node through the optical network for decryption. Each network node can output its individual chaotic signals separately with different time delay markers. By regulating different internal parameter mismatches, the synchronization characteristics of transceivers in a security network are numerically analyzed by using the cross correlation coefficient. Simulation results show that the chaos synchronization between transceivers enjoys fantastic robustness to mismatched parameters. Meanwhile, the tolerance of the DM scheme to the inherent parameter mismatch is excellent, so it is suitable for constructing secure networks in optical networks. Besides, based on the high quality synchronization with a correlation coefficient of 0.983, the communication performances of the longest path channel are investigated for a given metropolitan area network scale. Two pieces of 10 Gb/s messages can be effectively concealed in the chaos and decoded gratifyingly behind 100 km transmission, and the system has reliable security to resist illegal attacks. Finally, the network performance simulation is conducted for diverse configurations of the mesh-based optical networks. All the results confirmed the chaotic encryption scheme provides a novel way for any two legitimate nodes to establish security keys in optical networks.

Mixed Strategy Analysis in Attack-Defense Game Model Based on 5G Heterogeneous Network of CPS Using ncPSO

The development of the 5th Generation Mobile Communication Technology not only brings convenience to people but also brings many network security problems. Based on the static game theory of complete information, a game model of attack and defense with limited resources in heterogeneous networks of Cyber Physical Systems is established. This model analyzes the basic rules of the offensive and defensive strategies of both parties when the offensive and defensive resources are limited in the 5th Generation Mobile Communication Technology network environment. The model can also describe the interaction between attackers and defenders. A novel compact particle swarm optimization algorithm is proposed to solve the difficult problem of solving the Nash equilibrium of this game model. The simulation experiment proves that novel compact particle swarm optimization algorithm has good optimization ability and shows that the algorithm can effectively solve the Nash equilibrium of the model. The simulation experiment provides a strategic reference for the attack-defense game with limited resources.

Statistical analysis of the performance of four Apache Spark ML algorithms

Feature selection (FS) techniques generally require repeatedly training and evaluating models to assess theimportance of each feature for a particular task. However, due to the increasing size of currently availabledatabases, distributed processing has become a necessity for many tasks. In this context, the Apache SparkML library is one of the most widely used libraries for performing classification and other tasks with largedatasets. Therefore, knowing both the predictive performance and efficiency of its main algorithms beforeapplying a FS technique is crucial to planning computations and saving time. In this work, a comparativestudy of four Spark ML classification algorithms is carried out, statistically measuring execution times andpredictive power based on the number of attributes from a colon cancer database. Results were statistically analyzed, showing that, although Random Forest and Na¨ıve Bayes are the algorithms with the shortest execution times, Support Vector Machine obtains models with the best predictive power. The study of the performance of these algorithms is interesting as they are applied in many different problems, such as classification of pathologies from epigenomic data, image classification, prediction of computer attacks in network security problems, among others.

The dilemma and path of digital empowerment of enterprise asset management

The digital economy, as an important product of the third technology, has played a driving role in promoting the development of the global economy in the post-COVID-19 era. The digital economy-enabled enterprise asset management has not been specifically explained from the standpoint of enterprise asset management, and more research is required. This paper takes the specific embodiment of digital economy-enabled enterprise asset management as the breakthrough point, and focuses on analyzing the current problems of network security, vicious competition and labor employment that may exist in digital economy-enabled enterprise asset management, which have a negative impact on the sustainable development of enterprises. This paper provides corresponding countermeasures to serve as a reference for resolving problems that may arise as a result of the digitalization of enterprise asset management. Finally, this paper concludes that, while the digital economy enables enterprise asset management to play a relatively positive role in promoting traditional enterprise asset management, there are still some related issues that must be addressed. Enterprises need to take advantage of their own situations and avoid weaknesses, so as to improve the efficiency of enterprise asset management under the empowerment of the digital economy as much as possible.

A network attack link detection method based on the knowledge graph

Existing network security detection technologies often detect various network security problems separately. There are limitations in these technologies because of the correlation between entity attack detection and attack capital information. To resolve the problem of poor detection performance of existing network attack link detection methods, this paper proposes a network attack link detection method based on the knowledge graph. Specifically, the link knowledge graph is constructed, the embedded calculation of the knowledge graph of the link attack node is established, a knowledge graph audit model of link attack security detection is constructed, and the detection rules of the link attack knowledge graph are established to identify the association between the link network attack entity and the attack asset attribute, and improve the identification and detection of the attack source by associating the knowledge graph. Experimental results show that the proposed method has better link attack association awareness than other methods reported before.

Review on Security Issues and Applications of Trust Mechanism in Wireless Sensor Networks.

The scale of wireless sensor networks changes depending on specific application tasks. How to design a relatively simple security mechanism that can extend with the expansion of network scale is an arduous task. As the wireless sensor network mostly adopts the decentralized organization form, once the network nodes are distributed adequately, it is difficult to find out the failure of any node. In addition, the node has weak resistance to physical damage, so any node in the network may potentially become a cause of security vulnerability. In this study, firstly, the security standards that wireless sensor networks should have and the current security challenges faced by the network are discussed in detail, and the importance of security issues in wireless sensor networks is pointed out. Secondly, this paper studies and analyzes the current situation of wireless sensor network security problems including the internal and external attacks and the active and passive attacks. Thirdly, this paper also discusses the significance, concept, and characteristics of reputation and trust, the division and composition of reputation and trust system, and the common applications of the reputation and trust mechanism in wireless sensor networks.

Dwarf Mongoose Optimization-Based Secure Clustering with Routing Technique in Internet of Drones

Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches.

ClockIDS: A Real-Time Vehicle Intrusion Detection System Based on Clock Skew

Although intelligent connected vehicles (ICVs) can better assist drivers and improve their driving experience, they have huge network security problems and are frequently attacked. This is because the vehicle network is connected to the Internet, which expands the attack surface of ICV, and attackers have more ways to launch attacks. In recent years, many security experts fight against attackers and propose various types of vehicle intrusion detection systems (IDSs) to protect the controller area network (CAN). However, with the continuous enhancement of attack means, especially the appearance of the masquerade attack, most IDSs are no longer applicable. In this article, we design a new fingerprint-based vehicle IDS to protect the CAN, called ClockIDS. It establishes a unique fingerprint for each electronic control unit (ECU) based on clock skew. On this basis, ClockIDS realizes the functions of intrusion detection and attack source identification by utilizing the empirical rule and dynamic time warping. It neither occupies the bandwidth of CAN bus nor needs to modify the CAN protocol. Our experiments on two real vehicles show that ClockIDS can establish a unique fingerprint for ECU without being affected by the size of message period, and can detect three types of attack with a detection accuracy of 98.63%. In addition, this system can identify the attack source, and the average recognition accuracy is 96.77%. Furthermore, ClockIDS has high real-time performance, and the average time cost of each detection is only 1.99 ms.

An intellectual intrusion detection system using Hybrid Hunger Games Search and Remora Optimization Algorithm for IoT wireless networks

Smart devices are connected to the internet, called the Internet of Things (IoT); it is a wireless network that plays a vital role in modernizing people’s interaction by improving agility, mobility, efficiency and effectiveness. Although IoT offers various advantages, it also raises privacy and security problems in IoT networks. The security of such integrated networks still remains an active area of research. Information transported across a wireless network must be monitored and implemented efficiently to ensure its integrity, validity and confidentiality. In order to minimize the rate of security problems in IoT networks, an intrusion detection model is suggested. For that reason, various intrusion detection based techniques are introduced in existing works to improve the security of IoT networks. But existing methods do not provide an efficient result in securing IoT data. The main contribution of using intrusion detection techniques in the proposed work is to improve the security of IoT networks. In this work, a hybrid Hunger Games Search and Remora Optimization Algorithm (HHGS-ROA) is proposed to address the security problems. The proposed approach uses a real-world Aegean Wi-Fi Intrusion Dataset (AWID) for an IoT intruder detection system. Initially, the AWID raw captured traffic is fed into the pre-processing stage to increase the speed of feature selection. In the proposed approach, a hybrid HGS-ROA model is used to extract the features from the AWID dataset. Finally, the SVM classifier is proposed to classify network traffic as normal or malicious attacks. Therefore proposed approach achieves a low false-positive rate (FPR) with an enhanced detection rate. The proposed work is implemented in the PYTHON platform. Compared with existing methods in terms of accuracy, F1 score, detection rate, FAR, computation times, false alarm rate, MCC, and precision has proven to be more effective. The proposed approach performs better than existing algorithms in terms of evaluating metrics such as accuracy (99.16%), precision (99.76%), recall (99.40%), F1 score (99.58%), false alarm rate (0.00005%), MCC (99.97%) and false-positive rate (0.20%).

A secure relay selection scheme based on cooperative jamming for Underwater Acoustic Sensor Networks

Physical layer security (PLS) is one of the important methods to solve the security problems of Underwater Acoustic Sensor Networks (UASNs). In this paper, combining the characteristics of cooperative communication and cooperative jamming in PLS, we propose a Secure Relay Selection Scheme based on Cooperative Jamming for UASNs. On the one hand, according to the link quality and residual energy, one or several nodes are selected to forward information as relays. On the other hand, the scheme takes advantage of long propagation delay of the underwater acoustic channel and employs two different jamming ways: Self-Protection Jamming and Assistance Jamming, both of which can interfere with eavesdroppers without affecting the reception of legal users. The proposed scheme can increase the channel capacity of the legal channel by deploying relay nodes to forward the information and reduce the channel capacity of the eavesdropping channel by Self-Protection Jamming and Assistance Jamming. Simulation and field experiment results show that the proposed scheme can improve the PLS of the network.

Encryption Technology for Computer Network Data Security Protection

In order to solve the network security problem of data encryption technology, this paper proposes a research method of computer network information security maintenance based on data encryption technology. Taking the network reporting system of a mining enterprise as the research object, this paper comprehensively expounds on the design of network reporting system and data encryption in network communication from the aspects of demand investigation, demand analysis, system design, software system development, and so on and implements the development by using BS architecture + independent client. The experimental results show that the AES algorithm can be independently designated as 128 bits, 192 bits, and 256 bits. Conclusion. This scheme can realize the rapid and safe transmission of data information and provide an effective means for the transmission of confidential documents on the network, so it has a good application prospect.

DDoS Attack Target Detection based on AM+BPNN

The computer has developed from a single machine to a multi-machine network. The development of the network has penetrated into people's life, and similarly, the problem of network security has also followed. Distributed Denial of Service (DDoS) attack is one of the most popular network attacks at present. How to effectively detect DDoS attack targets and take urgent protective measures has become one of the difficulties in the research community. In this paper, a method of detecting DDoS attacks by using CNN neural network with attention mechanism (AM) is proposed by using the characteristics of a large amount of data in DDoS attacks. The technology used in this method is relatively mature, the implementation is simple, the cost is low, and it has certain practical significance.

Deep Q learning based secure routing approach for OppIoT networks

Opportunistic IoT (OppIoT) networks are a branch of IoT where the human and machines collaborate to form a network for sharing data. The broad spectrum of devices and ad-hoc nature of connections, further alleviate the problem of network and data security. Traditional approaches like trust based approaches or cryptographic approaches fail to preemptively secure these networks. Machine learning (ML) approaches, mainly deep reinforcement learning (DRL) methods can prove to be very effective in ensuring the security of the network as they are profoundly capable of solving complex and dynamic problems. Deep Q-learning (DQL) incorporates deep neural network in the Q learning process for dealing with high-dimensional data. This paper proposes a routing approach for OppIoT, DQNSec, based on DQL for securing the network against attacks viz. sinkhole, hello flood and distributed denial of service attack. The actor–critic approach of DQL is utilized and OppIoT is modeled as a Markov decision process (MDP). Extensive simulations prove the efficiency of DQNSec in comparison to other ML based routing protocols, viz. RFCSec, RLProph, CAML and MLProph.

Optimizing protection resource allocation for traffic-driven epidemic spreading.

Optimizing the allocation of protection resources to control the spreading process in networks is a central problem in public health and network security. In this paper, we propose a comprehensive adjustable resource allocation mechanism in which the over allocation of resources can be also numerically reflected and study the effects of this mechanism on traffic-driven epidemic spreading. We observe that an inappropriate resource allocation scheme can induce epidemic spreading, while an optimized heterogeneous resource allocation scheme can significantly suppress the outbreak of the epidemic. The phenomenon can be explained by the role of nodes induced by the heterogeneous network structure and traffic flow distribution. Theoretical analysis also gives an exact solution to the epidemic threshold and reveals the optimal allocation scheme. Compared to the uniform allocation scheme, the increase in traffic flow will aggravate the decline of the epidemic threshold for the heterogeneous resource allocation scheme. This indicates that the uneven resource allocation makes the network performance of suppressing epidemic degrade with the traffic load level. Finally, it is demonstrated that real-world network topology also confirms the results.

Analysis of Attack Intention Recognition

With the rapid development of network technology, network security problems are gradually increasing, and the network attack situation is very severe (Chen, Yanhua, Li, & Gao, 2019). In a complex attack scenario, timely detection of potential attack behaviours 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. Intrusion intention recognition is to interpret and judge the purpose, vision and intention of attackers through analyzing a large number of low-level alarm information, which is to give a reasonable explanation of a large number of attack data. This paper focuses on identifying attack intention in order to can determine the real purpose of attackers and predict the subsequent attack behavior, which is the premise and foundation of threat analysis and an important part of network security situation awareness. Keywords: Attack Intention, Multi-Stage Attack, Networks, Casual Network Approach, Systems, Cyber Security BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Tsatsu, K. Sabblah (2022): Analysis of Attack Intention Recognition Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 259-262 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P41

Network Security Problems and Countermeasures of Hospital Information System after Going to the Cloud.

In the current social context, information technology, network technology, and cloud computing have been widely used in all walks of life. The analysis of the specific application results of progressive technology shows that the use of technology has changed the working state of various industries and improved the work efficiency and quality of the industry. It should be noted that although the application of some technologies will bring many positive belongings, the potential risks brought by them cannot be ignored. As far as the hospital is concerned, the information system using cloud computing technology can make better use of the hospital's information data, but after the information system is on the cloud, new problems will appear in network security, resulting in the leakage of hospital patient information or research information. Based on this, in practice, it is necessary to analyze the network security problems after the hospital information system goes to the cloud and build and implement the corresponding strategies. The author analyzes and discusses the corresponding contents through work practice and combined with previous articles, in order to provide guidance and help for peers.

Security-Reliability Tradeoff Analysis of Untrusted Full-Duplex Relay Networks

In this work, we investigate the physical layer security problem of wireless cooperative network, where the communication from a source to a destination is assisted by an untrusted full-duplex amplify-and-forward (AF) relay. In order to realize a positive secrecy rate, cooperative jamming is exploited at the destination. The secrecy outage probability (SOP), connection outage probability (COP), and effective secrecy throughput (EST) are, respectively, derived in closed-form expressions for the fixed gain relaying (FGR) scheme and the variable gain relaying (VGR) scheme. Subsequently, we conduct an asymptotic analysis for both the relaying schemes in the high signal-to-noise ratio (SNR) region to offer valuable insights into practical design. Theory and simulation results demonstrate that the self-interference caused by full-duplex transmission is harmful for the reliability performance, while it is beneficial to the security performance. Moreover, the EST of the considered system increases as the self-interference level decreases, which implies that the self-interference deteriorates an overall performance in terms of the security-reliability tradeoff.

Anomaly Intrusion Detection of Wireless Communication Network-Based on Markov Chain Model

In order to solve the increasingly serious security problems of wireless networks, research on abnormal intrusion detection methods of wireless communication networks based on Markov chain model is proposed. What is usually observed is not the known intrusion behavior but the abnormal phenomenon in the communication process studied, which is completed by detecting the change of system behavior or usage. In this paper, the Markov chain model is used to detect the abnormal intrusion of wireless communication networks. Through the analysis and selection of parameters, the experimental results are ideal, and a variety of judgment methods are compared and analyzed. First, this method can easily distinguish between normal and abnormal data, which reduces the time by about 50% compared with the previous method; Second, the detection result of analysis method 2 is better than that of analysis method 1, and the accuracy is about 20%. The new method proposed in this paper has the characteristics of simple calculation, low algorithm complexity, and easy online detection. This method overcomes the disadvantage that the single-step Markov chain analysis and detection method cannot be strictly established in the nature of the Markov chain, has lower algorithm complexity than the multistep Markov chain analysis and detection method, and is simpler than the parameter calculation of hidden Markov chain model.

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network.

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

Application of Hybrid Encryption Algorithm in Hardware Encryption Interface Card

In order to effectively solve the increasingly prominent network security problems, cryptographic algorithm is the key factor affecting the effectiveness of IPSec VPN encryption. Therefore, this paper mainly studies cryptographic algorithms and puts forward the following solutions: briefly analyze the concept and function of IPSec VPN, as well as the basic theoretical knowledge of IPSec Security Protocol and cryptography, and analyze the traditional cryptography, modern cryptography, symmetric cryptographic algorithms and asymmetric algorithms, and their security. At the same time, the executable and security performances of AES and DES algorithms are compared and analyzed. This paper studies the elliptic curve encryption algorithm ECC, expounds the mathematical basis of realizing the algorithm, and compares and analyzes the security performance and execution efficiency of ECC. Based on the above two algorithms, a hybrid encryption algorithm is proposed, and the realization mechanism of the hybrid encryption algorithm is studied and discussed. The hybrid encryption algorithm combines the advantages of ECC and AES. The algorithm selects 128-bit AES and 256-bit ECC. In order to better cover up plaintext C, AES is used to encrypt information. While enhancing security, speed is also considered. The improved encryption, decryption, and signature authentication algorithms are relatively safe and fast schemes. ECC algorithm is improved, and on this basis, ECC algorithm and AES algorithm are combined. Moreover, HMAC message authentication algorithm is added, and the performance of the improved algorithm is significantly improved.