Optical Network Security Research Articles

Retraction Note: Radio optical network security analysis with routing in quantum computing for 5G wireless communication using blockchain machine learning model

Retraction Note: Radio optical network security analysis with routing in quantum computing for 5G wireless communication using blockchain machine learning model

Phase-locking-free all-optical matching system for 100 Gbaud QPSK optical signal

Photonic firewall is a monitoring protection device that will directly detect and locate optical network attacks at the optical layer, which can effectively ensure the security of optical networks. An all-optical matching system is the core part of photonic firewall, which determines the performance of a photonic firewall, so it is of great significance to research and develop all-optical matching system for high-speed and high-order modulation formats signals. At present, an all-optical matching system for binary modulation formats is relatively mature, but the all-optical matching system for high-order phase modulation format signals is still limited by how to solve the problem of phase synchronization. In this paper, a new all-optical matching system based on self-interference matching is proposed for quadrature phase shift keying (QPSK) optical signal, which avoids the introduction of local target sequence optical signals and phase-locking circuits. The theoretical analysis and simulation verification are conducted for the designed system. The results demonstrate that the system can accurately identify and locate 4-symbol or 8-symbol target sequences in the input QPSK optical signals with 16-symbol and 32-symbol data sequences at a data rate of 100 Gbaud.

Integrating optical security management with optical‐layer controller architecture for enhanced network security

AbstractTo guarantee the availability, confidentiality, and integrity of data transferred over optical channels—especially in the context of fifth‐generation (5G) communication infrastructure—optical network security management is essential. This paper provides an overview of security management for optical networks, emphasizing the importance of this practice in today's communication infrastructure and the difficulties presented by ever changing cyberthreats. The architecture of optical security management is shown, with special attention to how well it integrates with current optical‐layer controllers and how it facilitates the coordination of security operations among optical networks. The study also looks at use cases for optical network security management in 5G networks, such as safe data transfer, defense against cyberattacks, maintaining privacy in 5G apps, network slicing security, and resistance to physical assaults. Such instances highlight the adaptability and significance of optical network security management in bolstering 5G networks' security, privacy, and resilience across a range of businesses and applications.

Application of Intelligent Cloud Computing Technology in Optical Communication Network Security of Smart Grid

In order to improve the security of smart grid optical communication network, this paper combines intelligent cloud computing technology to build a security system of optical communication network of power grid. In the aspect of improving communication security performance, multi-user access is realized by non-orthogonal power domain segmentation, and different users use different powers to add and superimpose the same spectrum resources, so as to increase spectrum utilization. At the sending end, this paper improves the safe channel capacity of users by means of pre-coding and artificial noise, and realizes the safe transmission of information. In terms of transmission stability, the cloud computing platform is used as a data processing platform, and multiple nodes are processed synchronously through optical communication state identification, which can more effectively improve the speed of optical communication state identification data. In order to test the performance of the power grid information dispatching model designed in this paper in optimizing power grid configuration and improving power grid load, simulation experiments are carried out. Through the experimental analysis, we can see that the communication method proposed in this paper can accurately identify the intrusion factors, and can effectively improve the security of smart grid optical communication network.

Secure key generation and distribution scheme based on historical fiber channel state information with LSTM.

In this paper, a scheme to realize unclonable physical-layer security key generation and distribution (PL-SKGD) based on historical fiber channel state information (HFCSI) is proposed. PL-SKGD schemes based on channel characteristics for enhancing the physical-layer security of optical networks have been proposed in recent years. However, there are potential disadvantages in these schemes, such as 1) low key generation rate (KGR): the slow frequency of the analog waveform change of the channel characteristic leading to low KGR; 2) incompatibility with existing infrastructure: active scrambling to increase the frequency of channel characteristic changes, or tracking changes of channel characteristics requires additional devices; 3)easy to be cloned: all of the optical channel state information is reflected in the signal transmitted inside the fiber, which makes it easy to reproduce by illegal eavesdropper through features analysis and other methods. In order to solve the above problems, a PL-SKGD scheme is designed which uses the chain structure composed of long short-term memory neural network (LSTM-NN) units to learn and store the unique mapping relationship between historical channel time series and provides unclonability based on the fundamental fact that the eavesdropper Eve can never obtain the full HFCSI. The simulation conducted in a quadrature phase shift keying point-to-point optical link system verified successfully that KGR = 0.82 Gbit/s error-free SKGD. The loss function of LSTM-NN drops sharply in the early stages of training and remains a small value. The security of the SKGD system is analyzed, which effectively improves the unclonability of the system. Finally, it is verified that the optimal fiber channel length for error-free SKGD of the proposed scheme is 150 km considering the error correction capability of information reconciliation and weighing key sequence error rate and valid bit generation rate.

Security in Optical Wireless Communication-Based Vehicular Ad Hoc Networks Using Signature and Certificate Revocation

A vehicular ad hoc network is a capable method of making possible road well-being, traffic supervision, as well as information distribution for driving users and travelers. One definitive objective in the blueprint of this type of network is to refuse to accept a variety of malevolent mistreatment and security assaults. The paper analyzed the existing procedure, which has covered the ways of offering security duties and conserving user solitude in the context of wireless access in vehicular environments (WAVE) applications. To make the standards more realistic, it considers techniques of certificate revocation and conditional privacy preservation. These techniques are found to be the most promising ones in vehicular ad hoc networks (VANET). Therefore, a group of narrative security methods is proposed for the safe implementation of the techniques of certificate revocation and conditional privacy preservation. For enhancement of security, the paper proposes that the message should be encapsulated with a proper certificate and be duly signed. For faster communication, we propose the use of optical wireless communication (OWC) between an onboard unit (OBU) and a roadside unit (RSU).

A Novel Optical Transmitter With Chaotic Fingerprint for Identity Authentication in Physical-Layer Security of Optical Networks

A Novel Optical Transmitter With Chaotic Fingerprint for Identity Authentication in Physical-Layer Security of Optical Networks

RETRACTED ARTICLE: Radio optical network security analysis with routing in quantum computing for 5G wireless communication using blockchain machine learning model

RETRACTED ARTICLE: Radio optical network security analysis with routing in quantum computing for 5G wireless communication using blockchain machine learning model

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.

Root Cause Analysis for Autonomous Optical Network Security Management

The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL- and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

With the latest technological advancements and attractive features of next generation intelligent optical networks such as high bandwidth, low power consumption, and low transmission loss, etc., they have been considered as most viable solution to satisfy promptly growing bandwidth demands. However, main optical network components bring forth a set of security challenges and reliability issues, accompanied by new vulnerabilities within the network. This paper proposes a new design for an optical encryption and decryption method for enhancing optical network security using p–i–n photodiode which generates Pseudo Random Binary Sequence (PRBS) as a shot noise fluctuations and wavelength converter based design using Semiconductor Optical Amplifier based XOR gate which utilizes Cross-Phase Modulation. The system performance based on Bit Error Rate and Q factor are analyzed at different data rates for different link lengths up to 100 km using OptiSystem. It is observed that error free transmission with a BER of 10–12 is achieved a data rate of 10 Gbps for a link length of only 30 km for the system with PIN photodiode’s shot noise being used for PRBS sequence generation. However, wavelength conversion based system enables transmission of signal at 10Gbps signal up to a link length of 90 km.

Two chaotic synchronizations in er-doped fiber lasers and their application in two-channel coding secure communications

A dual-ring erbium-doped fiber laser has nonlinear dynamic characteristics based on optical coupling interaction of lasing from the two laser rings and can send chaotic lasing to encode and mask an information to perform secure communication. Two independent groups of single-ring erbium-doped fiber lasers and a dual-ring erbium-doped fiber laser are used to construct two-channel chaos secure communication system while the synchronization’s physical mathematical model is presented by using the “active-passive” chaotic synchronization method, where a dual-ring erbium-doped fiber laser is used as a transmitter and two independent groups of single-ring erbium-doped fiber lasers are used as two receivers. Our numerical simulation shows that these two receivers can synchronize with the transmitter respectively. A two-channel chaotic hidden encoding secure communications with a modulation frequency of 10kHz analog signal and a rate of 20kbit/s digital signal are numerically simulated respectively, which shows to have good decoding quality and strong security. The obtained result shows that the chaotic coding system can be well applied in chaotic two channel secure communications. The research results have an important reference value for optical multi-channel secure communication and network security.

Chaotic Non-Orthogonal Matrix-Based Encryption for Secure OFDM-PONs

We propose and experimentally demonstrate a novel encryption scheme to enhance the physical layer security in broadcast passive optical networks (PONs). We exploit, for the first time, a new dimension using non-orthogonality for encryption. Different from the conventional masking of data, the proposed chaotic non-orthogonal matrix (CNOM) can also dynamically scramble the number of subcarriers in an orthogonal frequency division multiplexing (OFDM) signal while maintaining the same bandwidth to further deceive potential eavesdroppers. By simultaneously using faster-than-Nyquist signaling and redundant precoding, the proposed scheme can achieve the compromise between spectral efficiency (SE) and resilience against system impairments, while increasing the overall key space. We then carry out a proof-of-concept experiment to verify the feasibility of the proposed CNOM-based encryption. Results showed that the transmission performance could be improved in addition to the security enhancement.

High security OFDM-PON based on an iterative cascading chaotic model and 4-D joint encryption

Optical access network security issues have become more prominent as the number of users and the desire for colossal network capacity increases dramatically. This paper proposes a secure method for data transmission by performing encryption at the physical layer for orthogonal frequency division multiplexing passive optical network (OFDM-PON), based on a combination of probabilistic shaping (PS) and 4-D iterative cascading chaotic scrambling, adopting improved-Logistic and delay-tent-sine double chaotic map. The chaos scrambling method uses an iteration method to generate scrambling vectors and encrypts the transmitted signal as needed, enhancing the security of the physical layer. The mathematical model for achieving secure transmission in four dimensions is presented and validated by the experimental results employing double chaotic map scrambling. PS-16-QAM based 16.48 Gb/s data is successfully encrypted using the proposed model and is transmitted across a 25 km fiber span using standard single-mode fiber. We also discussed the bit error rate and the beneficial effects of PS on the OFDM-PON system. The results show that the proposed OFDM-PON has high sensitivity and security, and PS technology has a 1 dB gain on EBR. This scheme has very reliable security performance and can improve system performance, which can be widely used in secure OFDM-PON systems.

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited

As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of the performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing what we believe to be a novel functional block called the security operation center, describing its architecture, specifying key requirements on the supported functionalities, and providing guidelines on its integration with optical-layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between the ML accuracy and run-time complexity.

Physical Layer Security in Multimode Fiber Optical Networks

The light propagation through a multimode fiber is used to increase information security during data transmission without the need for cryptographic approaches. The use of an inverse precoding method in a multimode fiber-optic communication network is based on mode-dependent losses on the physical layer. This leads to an asymmetry between legitimate (Bob) and illegitimate (Eve) recipients of messages, resulting in significant SNR advantage for Bob. In combination with dynamic mode channel changes, there are defined hurdles for Eve to reconstruct a sent message even in a worst-case scenario in which she knows the channel completely. This is the first time that physical layer security has been investigated in a fiber optical network based on measured transmission matrices. The results show that messages can be sent securely using traditional communication techniques. The technology introduced is a step towards the development of cyber physical systems with increased security.

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This article focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL), and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.

Optical spectrum feature analysis and recognition for optical network security with machine learning.

Physical layer attacks threaten services transmitted through optical networks. To detect attacks, we present an investigation of optical spectrum feature analysis (OSFA) and recognition. By analyzing the spectral features of optical signals, recognition and detection of unauthorized signals can be realized. In this paper, (1) we theoretically analyzed factors influencing optical spectrum (OS) features and simulated these factors. OSs collected from the simulation are quantitatively analyzed, spectral features are extracted by principal component analysis, and the theoretical derivation is validated. (2) We proposed support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) machine-learning OSFA methods. (3) Experimentally collected OSs from commercial small form-factor pluggable modules are used to verify the performance of the SVM and 1D-CNN methods, which achieved 98.54% and 100% recognition accuracies, respectively, demonstrating that the methods are promising solutions for optical network security.

Resource Allocation in Optical Networks Secured by Quantum Key Distribution

Optical network security is attracting increasing research attention, as loss of confidentiality of data transferred through an optical network could impact a huge number of users and services. Data encryption is an effective way to enhance optical network security. In particular, QKD is being investigated as a secure mechanism to provide keys for data encryption at the endpoints of an optical network. In a QKD-enabled optical network, apart from TDChs, two additional channels, called QSChs and PIChs, are required to support secure key synchronization. How to allocate network resources to QSChs, PIChs, and TDChs is emerging as a novel problem for the design of a security-guaranteed optical network. This article addresses the resource allocation problem in optical networks secured by QKD. We first discuss a possible architecture for a QKD-enabled optical network, where an SDN controller is in charge of allocating the three types of channels (TDCh, QSCh, and PICh) over different wavelengths exploiting WDM. To save wavelength resources, we propose to adopt OTDM to allocate multiple QSChs and PIChs over the same wavelength. An RWTA algorithm is designed to allocate wavelength and time slot resources for the three types of channels. Different security levels are included in the RWTA algorithm by considering different key updating periods (i.e., the period after which the secure key between two endpoints has to be updated). Illustrative simulation results show the effects of different security-level configuration schemes on resource allocation.

Light Source and Trail Recognition via Optical Spectrum Feature Analysis for Optical Network Security

In this letter, we propose a light source and trail recognition method via machine-learning-based optical spectrum feature analysis (OSFA) for optical network security. In order to realize light source and light trail recognition, we proposed a t-distribution stochastic neighbor embedding and support vector machine based OSFA method for stand-alone system. In order to improve the recognition accuracy, we further proposed a derivative cooperative multi-node recognition (CoMNR) mechanism. Experimental results show that 100% and 98.5% recognition accuracy for light sources and light trails can be achieved respectively at a single node. When CoMNR is adopted, the light trail recognition accuracy can be further improved to 100%.