**Multi-Period Attack-Aware Optical Network Planning under Demand Uncertainty**

Konstantinos Manousakis, Panayiotis Kolios and Georgios Ellinas

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.68491

#### **Abstract**

In this chapter, novel attack‐aware routing and wavelength assignment (Aa‐RWA) algo‐ rithms for multiperiod network planning are proposed. The considered physical layer attacks addressed in this chapter are high‐power jamming attacks. These attacks are modeled as interactions among lightpaths as a result of intra‐channel and/or inter‐chan‐ nel crosstalk. The proposed Aa‐RWA algorithm first solves the problem for given traffic demands, and subsequently, the algorithm is enhanced in order to deal with demands under uncertainties. The demand uncertainty is considered in order to provide a solu‐ tion for several periods, where the knowledge of demands for future periods can only be estimated. The objective of the Aa‐RWA algorithm is to minimize the impact of possible physical layer attacks and at the same time minimize the investment cost (in terms of switching equipment deployed) during the network planning phase.

**Keywords:** physical layer attacks, routing and wavelength assignment, optical networks, multi‐period planning, demand uncertainty

## **1. Introduction**

In wavelength division multiplexed (WDM) optical networks, wavelength routing is used for establishing communication between source‐destination pairs. In these networks, data are transmitted over all‐optical WDM channels called lightpaths. A connection is established by utilizing a lightpath, which is determined by choosing a path between the source and the destination and allocating a wavelength on all the links of the path. The selection of the path and wavelength is an important optimization problem and is known as the routing and wave‐ length assignment (RWA) problem [1].

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In WDM optical networks, transparent optical cross‐connects (OXCs) are used in order to pro‐ vide efficient space and wavelength switching functions [2]. An OXC takes as input signals at multiple wavelengths and some of these wavelengths can be dropped locally, while others pass through by switching them to the appropriate output ports. For the implementation of OXCs, wavelength selective switch (WSS) technology is used for the deployment of cost‐effec‐ tive and dynamic wavelength‐switched networks [3].

In transparent optical networks, where data signals remain in the optical domain until they reach their destinations, connections are vulnerable to physical layer attacks. An attack is defined as an intentional action against the ideal and secure functioning of the network. One type of attack in optical networks is high‐power jamming which can affect the signal through in‐band jamming that is the result of intra‐channel crosstalk or out‐of‐band jamming that is the result of inter‐channel crosstalk and nonlinearities [4]. This type of attack propagates through the transparent network affecting several connections, and as a consequence, the localization of this kind of attack is a difficult problem. Due to the high bit rates of optical networks and the interaction of the connections, a jamming attack can potentially cause a huge amount of information loss. Therefore, the limitation of attack propagation is a crucial consideration in optical network planning. An overview of security challenges in communica‐ tion networks can be found in Ref. [5].

Physical layer attacks in optical networks have been studied by several researchers [6–10]. In these works, the concept of attack‐aware routing and wavelength assignment (Aa‐RWA) is analyzed. Specifically, in Ref. [6], authors proposed an integer linear program (ILP) formula‐ tion and a tabu search heuristic algorithm for the routing sub‐problem in optical networks in order to minimize the effect of out‐of‐band jamming and the gain competition caused in optical fibers and optical amplifiers, respectively. In Ref. [7], authors proposed ILP formula‐ tion and heuristic algorithms for the wavelength assignment sub‐problem in optical networks in order to minimize the in‐band jamming attack caused in optical nodes. In Ref. [8], authors proposed ILP and heuristic algorithms based on simulated annealing techniques in order to minimize the in‐band and out‐of‐band jamming attacks. Moreover, in Ref. [9, 10], authors proposed a greedy randomized adaptive search procedure (GRASP) heuristic and an ILP formulation, respectively, for the placement of power equalizers in order to limit the jamming attack propagation in transparent optical networks.

Another important aspect in network planning that usually is not taken into account is the uncertainty of the connection requests. In most cases, the demands are considered to be known before network planning; however, in some cases, network planning must be performed for a period of time where the demand requests can only be forecasted with uncertainty. One approach to deal with demand uncertainty is by overprovisioning, essentially allocat‐ ing many resources that can satisfy any traffic demand. However, this approach requires a high cost investment (capital expenditure—capex) from the network operators [11]. More sophisticated approaches to deal with demand uncertainty are necessary in order to achieve a cost‐effective network investment strategy [12].

Stochastic programming (SP) [13] and robust optimization (RO) [14] are the main alternative techniques to deal with uncertain data both in a single period and in a multi‐period decision making process. In SP, the probability distribution functions of the underlying stochastic parameters must be known. On the other hand, RO addresses the uncertain nature of the prob‐ lem without making specific assumptions on probability distributions. The uncertain param‐ eters are assumed to belong to a deterministic uncertainty set. RO adopts an approach that addresses uncertainty by guaranteeing the feasibility and optimality of the solution against all instances of the parameters within the uncertainty set.

In Ref. [15], authors apply robust optimization in order to incorporate the uncertainty of demands into the network upgrade problem. Under the robust network upgrade model, the network planning can be performed by tuning the trade‐off between network cost and robust‐ ness level. Further, in Ref. [16], authors propose multi‐period network planning approaches based on SP, where the demands are forecasted over periods of time and the network invest‐ ments are performed based on these forecasts.

In this chapter, novel Aa‐RWA algorithms are proposed to address the problem of multi‐ period network planning under demand uncertainty with the objective to minimize the impact of possible physical layer attacks and at the same time to minimize the network infra‐ structure investment cost. Physical layer attacks are modeled as interactions among connec‐ tions through in‐band and out‐of‐band channel crosstalk. Moreover, the investment cost is taken into account in this formulation via the number of WSSs required in order to minimize the impact of a possible physical layer attack.

The simulation results show that when the distribution of demands for all the time periods is taken into account in advance, better results can be obtained in terms of the number of WSSs required to be placed in the network nodes so as to minimize the impact of a jamming attack, compared to the case where the distribution is known only for the period under consideration.

The chapter is organized as follows. Section 2 describes the network architecture, while Section 3 describes the planning approaches for demand uncertainty. In Section 4, the physical layer attacks in optical networks are presented, and in Section 5, the problem of attack‐aware RWA with given traffic demands is solved. This is followed in Section 6 by the attack‐aware RWA under demand uncertainties. Performance results are presented in Section 7, while Section 8 presents some concluding remarks.
