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首页> 外文期刊>Procedia Computer Science >Applying Intrusion Detection and Response systems for securing the Client Data Signals in the Egyptian Optical Network
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Applying Intrusion Detection and Response systems for securing the Client Data Signals in the Egyptian Optical Network

机译：应用入侵检测和响应系统来保护埃及光网络中的客户数据信号

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The physical layer of the Optical Transport Network (OTN) is the weakest layer in the network, as anyone can access the optical cables from any unauthorized location of the network and stat his attack by using any type of the vulnerabilities. The paper discusses the future role of the machine learning technology in the detection of the security threats and the automatic response to overcome the security challenges in the Egyptian optical network and it presents a new proposed model for the threats detection and the response to protect the client’s data on the physical layer of the optical network. The detections of the threats in the optical network will be done in our model by proposing learning algorithms from the continuous variations in the optical signal to noise ratio (OSNR) over the entire network and at the same time according to the past experiences of the proposed model it will decide if this variations are threats or normal attenuations on the optical cables. The response will be done by distributing the triggers for the encryption keys between the network elements according to the decisions of the intrusion detection system of the machine learning techniques. A new security layer will be established to the OTN frames in the infected sections only. The design of the proposed security model is done by using a structure of XOR, a Linear Feedback Shift Register (LFSR), Random Number Generator (RNG) associated with different techniques of learning algorithms, and expert system for the threats in the optical network. We propose the security model for different rates in the OTN and wavelength division multiplexing (WDM) system. The proposed model is implemented on the basis of protecting the infected sections in the physical layer only over the optical network by passing these signals into extra layer called security layer, and before forming the final frame of the OTN system, this done according to the decisions of our proposed security learning algorithms in the management layer of the optical network. The results show that using machine learning techniques in the proposed model of the OTN encryption scheme is providing a high security against any wiretapping attack and reduces the huge cost of implementing complete security algorithms over all the network sections without any benefits. Although the proposed model of the intrusion detection and response systems in the optical network will be implemented on the infected sections only the attacker who has the ability to access the fiber cables from any unauthorized location, will affect the OSNR value and as a consequences the system will detect this difference in the value of the OSNR and according to the past experience the system will response to this threat, the attacker will find encrypted data only and will take many years to find one right key to perform the decryption process.
机译：光传输网络（OTN）的物理层是网络中最薄弱的一层，因为任何人都可以从网络的任何未授权位置访问光缆，并通过使用任何类型的漏洞来说明其攻击。本文讨论了机器学习技术在检测安全威胁和自动响应以克服埃及光网络中的安全挑战方面的未来作用，并提出了一种新的提议的威胁检测和响应模型，以保护客户的安全。光网络物理层上的数据。在我们的模型中将通过从整个网络上的光信噪比（OSNR）的连续变化中提出学习算法，并根据建议的过去经验，提出学习算法，从而完成对光网络中威胁的检测。建模，它将确定这种变化是威胁还是光缆正常衰减。将根据机器学习技术的入侵检测系统的决策，通过在网络元素之间分配加密密钥的触发器来完成响应。仅在受感染部分中为OTN帧建立一个新的安全层。通过使用XOR的结构，线性反馈移位寄存器（LFSR），与学习算法的不同技术相关联的随机数生成器（RNG）以及光网络中威胁的专家系统，可以完成所提出的安全模型的设计。我们提出了OTN和波分复用（WDM）系统中不同速率的安全模型。提出的模型是在保护光网络上物理层中被感染部分的基础上实现的，方法是将这些信号传递到称为安全层的额外层中，并在形成OTN系统的最终帧之前，根据决定进行操作我们在光网络管理层中提出的安全学习算法。结果表明，在提出的OTN加密方案模型中使用机器学习技术可提供针对任何窃听攻击的高安全性，并降低了在所有网络部分上实施完整安全算法的巨大成本，而没有任何好处。尽管建议的光网络中入侵检测和响应系统模型将在受感染的部分上实施，但只有能够从任何未经授权的位置访问光缆的攻击者才会影响OSNR值，并因此影响系统它将检测到OSNR值的这种差异，并且根据过去的经验，系统将对此威胁做出响应，攻击者将仅找到加密的数据，并且将花费很多年才能找到一个正确的密钥来执行解密过程。

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