Critical Node Identifcation for accessing network vulnerability, a necessary consideration

Timely identification of critical nodes is crucial for assessing network vulnerability and survivability. This thesis presents two new approaches for the identification of critical nodes in a network with the first being an intuition based approach and the second being build on a mathematical framework. The first approach which is referred to as the Combined Banzhaf & Diversity Index (CBDI) uses a newly devised diversity metric, that uses the variability of a node’s attributes relative to its neighbours and the Banzhaf power index which characterizes the degree of participation of a node in forming the shortest path route. The Banzhaf power index is inspired from the theory of voting games in game theory whereas, the diversity index is inspired from the analysis and understanding of the influence of the average path length of a network on its performance. This thesis also presents a new approach for evaluating this average path length metric of a network with reduced computational complexity and proposes a new mechanism for reducing the average path length of a network for relatively larger network structures. The proposed average path length reduction mechanism is tested for a wireless sensor network and the results compared for multiple existing approaches. It has been observed using simulations that, the proposed average path length reduction mechanism outperforms existing approaches by reducing the average path length to a greater extent and with a simpler hardware requirement. The second approach proposed in this thesis for the identification of critical nodes is build on a mathematical framework and it is based on suboptimal solutions of two optimization problems, namely the algebraic connectivity minimization problem and a min-max network utility problem. The former attempts to address the topological as- pect of node criticality whereas, the latter attempts to address its connection-oriented nature. The suboptimal solution of the algebraic connectivity minimization problem is obtained through spectral partitioning considerations. This approach leads to a distributed solution which is computationally less expensive than other approaches that exist in the literature and is near optimal, in the sense that it is shown through simulations to approximate a lower bound which is obtained analytically. Despite the generality of the proposed approaches, this thesis evaluates their performance on a wireless ad hoc network and demonstrates through extensive simulations that the proposed solutions are able to choose more critical nodes relative to other approaches, as it is observed that when these nodes are removed they lead to the highest degrada- tion in network performance in terms of the achieved network throughput, the average network delay, the average network jitter and the number of dropped packets

R-PEKS: RBAC Enabled PEKS for Secure Access of Cloud Data

In the recent past, few works have been done by combining attribute-based access control with multi-user PEKS, i.e., public key encryption with keyword search. Such attribute enabled searchable encryption is most suitable for applications where the changing of privileges is done once in a while. However, to date, no efficient and secure scheme is available in the literature that is suitable for these applications where changing privileges are done frequently. In this paper our contributions are twofold. Firstly, we propose a new PEKS scheme for string search, which, unlike the previous constructions, is free from bi-linear mapping and is efficient by 97% compared to PEKS for string search proposed by Ray et.al in TrustCom 2017. Secondly, we introduce role based access control (RBAC) to multi-user PEKS, where an arbitrary group of users can search and access the encrypted files depending upon roles. We termed this integrated scheme as R-PEKS. The efficiency of R-PEKS over the PEKS scheme is up to 90%. We provide formal security proofs for the different components of R-PEKS and validate these schemes using a commercial dataset

Increasing user controllability on device specific privacy in the Internet of Things

With recent advancements in information technology more and more devices are integrated in the Internet of Things. These devices gather significant amount of private information pertinent to a user and while, in some cases it helps in improving the life style of an individual, in others it raises major privacy concerns. This trade-off between utility and privacy is highly dependent upon the devices in consideration and as the utility of the generated data increases, the privacy of an individual decreases. In this paper, we formulate a utility-privacy trade-off that enables a user to make appliance specific decisions as to how much data can be shared. This is achieved by parametrizing the degree of privacy allowed for each device and enabling the user to configure the parameter of each device. We use the smart metering application as the test case scenario for the proposed approach. We evaluate its performance using simulations conducted on the ECO data set. Our results indicate that, the proposed approach is successful in identifying appliances with an accuracy of 81.8% and a precision of 70.1%. In addition, it is demonstrated that device specific changes of the configuration parameters allow the degree of privacy achieved for the particular device and the utility to be well controlled, thus demonstrating the effectiveness of the proposed approach. Moreover, it is shown that, as expected, devices with higher power consumption contribute more to the overall privacy and utility achieved. A comparative study is also conducted and the proposed approach is shown to outperform the existing ElecPrivacy approach by producing a trace that is harder to identify, as reported after testing the Weiss’ and Baranski’s algorithm, both of which are well known Non-Intrusive Load Monitoring algorithms. Finally, it is demonstrated that the addition of noise, which is an integral part of the propose approach, can greatly improve performance

DNA amplified fingerprinting, a useful tool for determination of genetic origin and diversity analysis in Citrus

We used three short repetitive nucleotide sequences [(GTG)5, (TAC)5, and (GACA)4] either as radiolabeled probes for hybridization with restricted Citrus DNA or as single primers in polymerase chain reaction amplification experiments with total genomic DNA. We tested the ability of the sequences to discriminate between seedlings of zygotic or nuclear origin in the progeny of a Volkamer lemon #Citrus volkameriana# Ten. & Pasq.) tree. The genetic variability within two species [#Citrus sinensis# (L.) Osbeck (sweet oranges) and #Citrus reticulata# Blanco and relatives (mandarins)] was evaluated. DNA amplified figerprinting with single primers was the more successful technique for discriminating between nucellular and zygotic seedlings. Although we were not able to distinguish among 10 cultivars of #C. sinensis#, all 10 #C. reticulata# cultivars tested were distinguishable. However, it still is difficult to identify the putative parents of a hybrid plant when the two parental genomes are closely related. (Résumé d'auteur

The changing UV and X-ray properties of the Of?p star CPD -28 2561

The Of?p star CPD -28 2561 was monitored at high energies with XMM-Newton and HST. In X-rays, this magnetic oblique rotator displays bright and hard emission that varies by ~55% with rotational phase. These changes occur in phase with optical variations, as expected for magnetically confined winds; there are two maxima and two minima in X-rays during the 73d rotational period of CPD -28 2561. However, contrary to previously studied cases, no significant hardness variation is detected between minima and maxima, with the exception of the second minimum which is slightly distinct from the first one. In the UV domain, broad-band fluxes remain stable while line profiles display large variations. Stronger absorptions at low velocities are observed when the magnetic equator is seen edge-on, which can be reproduced by a detailed 3D model. However, a difference in absorption at high velocities in the CIV and NV lines is also detected for the two phases where the confined wind is seen nearly pole-on. This suggests the presence of strong asymmetries about the magnetic equator, mostly in the free-flowing wind (rather than in the confined dynamical magnetosphere).Comment: 14 pages, 11 figures, accepted for publication by MNRA

CBDI: Combined Banzhaf & Diversity Index for Finding Critical Nodes

Critical node discovery plays a vital role in assessing the vulnerability of a network to an abrupt change, such as an adversarial attack or human intervention. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a node’s attributes relative to its neighbors and the Banzhaf Power Index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. We evaluate the performance of the new metric using simulations. Our results indicate that in a number of network topologies, the proposed metric outperforms other proposals which have appeared in the literature. The proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed

On the Complexity of Average Path length for Biological Networks and Patterns

Path length calculation is a frequent requirement in studies related to graph theoretic problems such as genetics. Standard method to calculate average path length of a graph requires traversing all nodes in the graph repeatedly, which is computationally expensive for graphs containing large number of nodes. We propose a novel method to calculate average path length for graphs commonly required in the studies of genetics. The proposed method is computationally less expensive and less time consuming compared to standard method. In this paper, a mathematical formulation is provided that calculates Average Path Length for graphs commonly present in biological networks, at the cost of uniform time complexity, for different size of networks

Spectral Partitioning for Node Criticality

Finding critical nodes in a network is a significant task, highly relevant to network vulnerability and security. We consider the node criticality problem as an algebraic connectivity minimization problem where the objective is to choose nodes which minimize the algebraic connectivity of the resulting network. Previous suboptimal solutions of the problem suffer from the computational complexity associated with the implementation of a maximization consensus algorithm. In this work, we use spectral partitioning concepts introduced by Fiedler, to propose a new suboptimal solution which significantly reduces the implementation complexity. Our approach, combined with recently proposed distributed Fiedler vector calculation algorithms enable each node to decide by itself whether it is a critical node. If a single node is required then the maximization algorithm is applied on a restricted set of nodes within the network. We derive a lower bound for the achievable algebraic connectivity when nodes are removed from the network and we show through simulations that our approach leads to algebraic connectivity values close to this lower bound. Similar behaviour is exhibited by other approaches at the expense, however, of a higher implementation complexity

Combined Banzhaf & Diversity Index (CBDI) for critical node detection

Critical node discovery plays a vital role in assessing the vulnerability of a computer network to malicious attacks and failures and provides a useful tool with which one can greatly improve network security and reliability. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary computer network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a node׳s attributes relative to its neighbours and the Banzhaf power index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. The proposed metric is evaluated using analysis and simulations. The criticality of nodes in a network is assessed based on the degradation in network performance achieved when these nodes are removed. We use several performance metrics to evaluate network performance including the algebraic connectivity which is a spectral metric characterizing the connectivity robustness of the network. Extensive simulations in a number of network topologies indicate that the proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed

Towards Transparency of IoT Message Brokers

In this paper we propose an ontological model for documenting provenance of MQTT message brokers to enhance the transparency of interactions between IoT agents