Samir K. Sadhukhan

Cyber-risk decision models

Security breaches adversely impact profit margins, market capitalization and brand image of an organization. Global organizations resort to the use of technological devices to reduce the frequency of a security breach. To minimize the impact of financial losses from security breaches, we advocate the use of cyber-insurance products. This paper proposes models to help firms decide on the utility of cyber-insurance products and to what extent they can use them. In this paper, we propose a Copula-aided Bayesian Belief Network (CBBN) for cyber-vulnerability assessment (C-VA), and expected loss computation. Taking these as an input and using the concepts of collective risk modeling theory, we also compute the premium that a cyber risk insurer can charge to indemnify cyber losses. Further, to assist cyber risk insurers and to effectively design products, we propose a utility based preferential pricing (UBPP) model. UBPP takes into account risk profiles and wealth of the prospective insured firm before proposing the premium. Display Omitted Proposed Cyber risk insurance products to minimize the impact of financial loss of security breach.Cyber risk insurance products complement security technology.Our proposed Copula aided Bayesian Belief networks model helps to asses cyber risk.Collective risk & Utility Theory used to computes premium for Cyber risk insurance products.Cyber risks mode for to decide to opt for cyber insurance or not for organizations.

e-Risk Management with Insurance: A Framework Using Copula Aided Bayesian Belief Networks

e-business organizations are heavily dependent on distributed 24X7 robust information computing systems, for their daily operations. To secure distributed online transactions, they spend millions of dollars on firewalls, anti-virus, intrusion detection systems, digital signature and encryption. Nonetheless, a new virus or a clever hacker can easily compromise these deterrents, resulting in losses to the tune of millions of dollars annually. To cope up with the problem, in this work we propose to further enhance their security management by investing in e-risk insurance products as a viable alternative to reduce these individual financial losses. We develop a framework, based on copula aided Bayesian Belief Network (BBN) model, to quantify the risk associated with online business transactions, arising out of a security breach, and thereby help in designing e-insurance products. We have simulated marginal data for each BBN nodes. The Copula model helps in arriving at the joint probability distributions from these marginal data. From the joint distribution data, we arrive at the conditional distribution tables for each node. This is input to the Bayesian Belief Network model. The output is frequency of occurrence of an e-risk event. Frequency of loss multiplied with the expected loss amount, provides the risk premium to be charged by insurance companies.

Post-deployment tuning of UMTS cellular networks through dual-homing of RNCs

In conventional UMTS cellular networks, during deployment usually a set of NodeBs is assigned to one Radio Network Controller (RNC), and a set of RNCs to one Serving GPRS Support Node (SGSN) for data services, as well as to one Mobile Switching Centre (MSC) for voice services. Operators thus far have considered single-homing of RNCs to MSCs/SGSNs (i.e., many-to-one mapping) with an objective to reduce the total cost over a fixed period of time. However, a single-homing network does not remain cost-effective any more when subscribers later on begin to show specific inter-MSC/SGSN mobility patterns (say, diurnality of office goers) over time. This necessitates post-deployment topological extension of the network in terms of dual-homing of RNCs, in which some specific RNCs are connected to two MSCs/SGSNs via direct links resulting in a more complex many-to-two mapping structure in parts of the network. The partial dual-homing attempts to increase link cost minimally and reduce handoff cost maximally, thereby significantly reducing the total cost in a post-deployment optimal extension. In this paper, we formulate the scenario as a combinatorial optimization problem and solve the NP-Complete problem using two meta-heuristic techniques, namely Simulated Annealing (SA) and Tabu search (TS). We then compare these techniques with a novel optimal heuristic search method that we propose typically to solve the problem. The comparative results reveal that, though all of them perform equally well for small networks, for larger networks, the search-based method is more efficient than meta-heuristic techniques in finding optimal solutions quickly.

A novel direction-based diurnal mobility model for handoff estimation in cellular networks

In real-life scenario where users mostly know their destinations a priori, their movement is not purely random (as assumed in random walk model); rather it is somewhat direction-based and depends on the present location and the final destination. For instance, an office goer often returns home in the evening (i.e., mobility is diurnal). While no existing research has studied direction-based diurnal mobility model which could be of immense importance to cellular operators, we formulate a Markov model for such movement and give a theoretical upper bound of cell boundary crossings (i.e., expected number of handoffs) by the user. We have theoretically calculated the number of handoffs based on our proposed mobility model and verified the result in a simulated environment. We find that a majority of inter-RNC/MSC handoffs [4] comes from only a few cells which should be made dual-homed by the operators for their cost reduction.

A Heuristic Technique for Solving Dual-Homing Assignment Problem of 2.5G Cellular Networks

2.5 cellular network topology involves grouping of NodeBs into Radio Network Controllers (RNCs), RNCs into serving GPRS support nodes (SGSNs), and RNCs into mobile switching centres (MSCs), so as to minimize the total cost of operation (TCO) including equipment cost, cable cost, installation cost, cost of call setup and handoff cost. The problem has been traditionally solved under single-homing consideration with an objective to reduce the TCO over a certain period of time. However, a single homing solution may not be effective with a group of subscribers having a specific diurnal pattern of their inter-SGSN mobility. In this scenario dual-homing of RNCs may be one of the approaches to reduce the TCO. In this paper, we have addressed the problem by making RNCs dual-homed. The problem is formulated as a combinatorial optimization problem and solved using a heuristic technique. The performance of the heuristic technique is compared with an exhaustive search technique for small size problems and it is found that the proposed technique is capable of finding a good quality solutions

A Routing and Wavelength Assignment (RWA) Technique to Minimize the Number of SONET ADMs in WDM Rings

WDM networks are increasingly gaining popularity as the backbone networks all over the world. In WDM network planning, electronic equipments like ADMs contribute heavily towards the capital expenditure of the network (Each ADM costs

A sustainable and collaborative strategy for dynamic spectrum management in next generation wireless networks

50,000-

Joint optimization of 2-tier dual-homing for NodeBs and RNCs in UMTS networks using meta-heuristic techniques

200,000). In WDM rings, determining minimum number of ADMs is NP-Hard. All the previous works have focused on Wavelength Assignment problem by keeping the routing fixed. In this paper, we suggest an integrated topology independent procedure called M3AWI (Minimum Average Minimum Weight Minimum Intersection). M3AWI is used to solve problems on four different types of routing namely, clockwise routing, Anti-clockwise routing, shortest-path routing, and hybrid routing. Our experimental results show that the combined Routing and Wavelength assignment (RWA) algorithms on an average save around 10% of the ADMs.We have also shown that the distribution of lightpaths is an important parameter to the service providers for selecting a routing policy.

A metaheuristic based fair dynamic spectrum allocation policy

Next generation wireless technologies offer various services from voice call to full motion pictures and even to high speed internet access. Consequently, the service providers (SP) armed with different wireless technologies (like 2.5G/3G/LTE) would require an adequate and significant amount of spectrum bandwidth for satisfying the need of their customers. Hence to achieve complete commercialization, the SPs, operating simultaneously, would demand for more and more spectrum from the regulatory body of the country. The spectrum demand on the part of the SP may vary with time (dynamic) because of varied kind of loads which are generated depending on the nature of the client-base, their requirements and their expected quality of experience. This work has addressed this challenging issue of allocating spectrum dynamically to different technologies under the portfolio of an SP. Here, we have conceived a scenario where service providers (SP) own multiple access networks (ANs) of different technologies. We envisage that an entity, called local spectrum controller (LSC) which is dedicated for managing the common pool of spectrum allocated to each SP. LSC is mainly responsible for distributing the spectrum to individual ANs of an SP in a fair manner. Since the available spectrum may not be sufficient enough to satisfy the aggregate demand from all ANs simultaneously, an LSC may face a situation, where satisfying individual demands from all ANs may result in a compromise between the demand and supply. This demand-supply situation would force an LSC or an SP to adhere to some dynamic spectrum management strategy, where demands of an AN would have to be satisfied depending on the current state of available spectrum and required usage of it. This calls for an adaptive dynamic strategy to be introduced by an SP for efficient spectrum distribution. The dynamic disparity of spectrum allocation can be idealized as a game between LSC and ANs. Hence, in the present work, we have modeled the problem of dynamic spectrum allocation as an n-player cooperative bankruptcy game and have solved the problem with the help of Shapley value and @t-value separately. We have investigated whether the ANs find it beneficial to cooperate with each other to make the solution sustainable enough. To evaluate the performances of the games that the ANs play, we have designed a novel utility function for each AN. We have identified plausible aims of an SP as minimizing overall dissatisfaction (MOD) and maximizing equality of distribution (MED). Next, we have studied performances of the above two solution concepts against max-min fairness algorithm (benchmarked in our case) with respect to the above objectives of LSC. Finally, we have proposed a unique heuristic in order to facilitate the decision making process of dynamic spectrum allocation, which leads to an adaptive yet optimized spectrum allocation strategy.

Post deployment planning of 3G cellular networks through dual homing of NodeBs

Conventional design of UMTS networks usually involves single-homing (i.e., many-to-one mapping) of NodeBs to Radio Network Controllers (RNCs) in tier-1 (i.e., a group of NodeBs is connected to a single RNC) and that of RNCs to Mobile Switching Centres (MSCs) as well as to Serving GPRS Support Nodes (SGSNs) in tier-2 (i.e., a group of RNCs is connected to a single MSC/SGSN). Thus, any NodeB is connected to only one RNC and any RNC is connected to only one MSC/SGSN. However, as subscriber distribution changes over time and new mobility patterns of subscribers begin to evolve, single homing solution sometimes becomes inefficient in terms of handoff cost minimization. One solution to this brown-field operational problem is dual-homing extension of some selected NodeBs and RNCs (i.e., some NodeBs are connected to two RNCs in tier-1 and some RNCs to two MSCs/SGSNs in tier-2) in order to reduce the handoff cost. Traditionally, this optimization problem has been formulated separately for each tier and solved independently, thereby missing the global optimal solution. In this paper, we have first shown how to combine the optimization problems across the two tiers and then mapped the joint dual homing optimization problem into a classical search problem. Next, we have used two common meta-heuristic techniques, namely Simulated Annealing and Tabu Search, to solve the above problem. Comparison of the results obtained from joint dual homing with the published results for individual dual homing reveals that the joint dual homing performs considerably better than individual dual homing that attacks NodeB level and RNC level separately and independently.