Novarun Deb

A State-of-the-Art Survey on IDS for Mobile Ad-Hoc Networks and Wireless Mesh Networks

An Intrusion Detection System (IDS) detects malicious and selfish nodes in a network. Ad hoc networks are often secured by using either intrusion detection or by secure routing. Designing efficient IDS for wireless ad-hoc networks that would not affect the performance of the network significantly is indeed a challenging task. Arguably, the most common thing in a review paper in the domain of wireless networks is to compare the performances of different solutions using simulation results. However, variance in multiple configuration aspects including that due to different underlying routing protocols, makes the task of simulation based comparative evaluation of IDS solutions somewhat unrealistic. In stead, the authors have followed an analytic approach to identify the gaps in the existing IDS solutions for MANETs and wireless mesh networks. The paper aims to ease the job of a new researcher by exposing him to the state of the art research issues on IDS. Nearly 80% of the works cited in this paper are published with in last 3 to 4 years.

TIDS: trust-based intrusion detection system for wireless ad-hoc networks

This paper aims to propose a new trust-based Intrusion Detection system (IDS) for wireless, ad-hoc networks with or without mobility of nodes. In fact, the proposed solution not only detects intrusions but also proactively responds towards route setup avoiding the compromised nodes. It could be extended for mesh or hybrid networking environment too. Trust is evaluated as the weighted sum of direct evaluation of the neighboring nodes as well as from the indirect references. A sliding window is defined on the time scale and the IDS is to be evoked after every time slice. Indirect reference is derived from the recommendations of those 1---hop neighbors of the target node that are also neighbors of the evaluating node. The performance of the proposed algorithm has been evaluated using the Qualnet network simulator. Simulation results also establish superiority of the proposed algorithm over HIDS, another recent trust-based IDS for wireless ad-hoc network.

Extracting finite state models from i* models

The Naive Algorithm (NA) extracts all possible finite state models from i* models.We observe an explosion in the finite state model space.The Semantic Implosion Algorithm (SIA) is a solution to this explosion problem.NA and SIA are extensively simulated on different categories of i* models.SIA drastically reduces the model space growth from O(1020) (for NA) to O(103). i* models are inherently sequence agnostic. This makes the process of cross-checking i* models against temporal properties quite impossible. There is an immediate industrial need to bridge the gap between such a sequence agnostic model and a standardized model verifier so that model checking can be performed in the requirement analysis phase itself. In this paper, we first spell out the Naive Algorithm that generates all possible finite state models corresponding to a given i* model. The growth of the finite state model space can be mapped to the problem of finding the number of possible paths between the Least Upper Bound (LUB) and the Greatest Lower Bound (GLB) of a k-dimensional hypercube lattice structure. The mathematics for doing a quantitative analysis of the space growth has also been presented. The Naive Algorithm has its main drawback in the hyperexponential growth of the model space. The Semantic Implosion Algorithm is proposed as a solution to the hyperexponential problem. This algorithm exploits the temporal information embedded within the i* model of an enterprise to reduce the rate of growth of the finite state model space. A comparative quantitative analysis between the two approaches concludes the superiority of the Semantic Implosion Algorithm.

Using i ∗ model towards ontology integration and completeness checking in enterprise systems requirement hierarchy

i* is a goal oriented requirements engineering tool that can model varied requirements for solutions being developed by an enterprise. Different stakeholders within an enterprise may use partial or even completely non-intersecting vocabulary sets. This would lead to developing multiple i* models having independent ontologies. Collectively, these i* models define a requirements refinement hierarchy where different tiers of the hierarchy capture different levels of detail. In order to integrate such a distributed ontology, we require an appropriate mapping or correlate definitions between different ontologies at multiple levels. This paper tries to identify different types of bridging correlations that allow an integration of multi-ontological i* refinement hierarchies. We use these correlations to demonstrate how relative completeness can be established between adjacent tiers of the hierarchy. Varying degrees of relative completeness have also been identified and their fulfillment criteria have been summarized as theorems. A University Admissions System has been used as a case study to illustrate heuristics for achieving greater relative completeness.

CORIDS: a cluster-oriented reward-based intrusion detection system for wireless mesh networks

Wireless mesh networks WMNs are proliferating as one of the key technologies of the next-generation networks. Security is one of the prime concerns towards actual implementation of any network technology for commercial applications. Network security has intrinsically two approaches-prevention based and detection based. Implementing firewalls or intrusion prevention techniques is often not an attractive solution for energy-constrained network nodes such as mobile ad hoc network MANET nodes or mesh clients in WMNs. However, in the era of pervasive and ubiquitous computing, commercial transactions are performed on the move and over portable devices such as cell phones and laptops. These devices have energy constraints, and hence, one cannot afford to adopt security measures with high computational overhead. This influences a shift in paradigm from active intrusion prevention to passive intrusion detection. In this paper, a new cluster-oriented reward-based intrusion detection system CORIDS has been proposed for WMNs. The performance of CORIDS has been evaluated using the Qualnet network simulator. Simulation results also establish superiority of CORIDS over Misbehavior Detection Algorithm, another recent trust-based IDS for WMN, both in terms of higher detection efficiency and lower false positives. Copyright

POMSec: Pseudo-Opportunistic, Multipath Secured Routing Protocol for Communications in Smart Grid

Traffic engineering governs the operational performance of a network and its optimization. Splitting the network traffic using multipath routing is one of the standard techniques of traffic engineering. Multipath routing maximizes network resource utilization and throughput by giving nodes a choice of next hops for the same destination along with minimizing the delay. On the other hand, Opportunistic routing minimizes operational cost and the burden of redundant route maintenance by using a constrained redundancy in route selection. POMSec: Pseudo Opportunistic, Multipath Secure routing is one such algorithm that combines the advantages of both the routing methods and additionally implements an underlying trust model to secure the communication in Smart Grid.

Node localization for indoor tracking using artificial neural network

Wireless sensor network (WSN) always comes up with the need of deploying either mobile or immobile sensor nodes or both. Wireless communication among these nodes is crucial and it requires identifying the location of these nodes within a specific region. Global positioning system (GPS) is widely used for location tracking. However, when it comes to WSN, GPS has its limitations, due to its high power consumption and the overhead of additional hardware cost. The research challenge here lies in the efficient location tracking of wireless sensor nodes, especially in closed indoor and outdoor environments. This paper comes up with a simple and easy-to-implement technique using artificial neural networks (ANNs) to manipulate the location of the sensor nodes. In this paper, the back-propagation network training algorithm for providing supervised learning to multilayer perceptron is generalized to synthesize the WSN and gives out 2D Cartesian coordinates of the nodes. The technique is both cost-efficient and achieves 98% accuracy.

Mining goal refinement patterns: Distilling know-how from data

Goal models play an important role by providing a hierarchic representation of stakeholder intent, and by providing a representation of lower-level subgoals that must be achieved to enable the achievement of higher-level goals. A goal model can be viewed as a composition of a number of goal refinement patterns that relate parent goals to subgoals. In this paper, we offer a means for mining these patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models. The resulting machinery can be quiote powerful in its ability to mine know-how or constitutive norms. We offer an empirical evaluation using both real-life and synthetic datasets.

Measuring the Effectiveness of Knowledge Driven Web Applications

Today, web applications are often considered as real-life reflections of enterprises. Patrons do invest large finances. Besides, lot of time and effort are put in towards development and maintenance of the web applications. The effectiveness of such web applications is an indicator for both further investment and refinement of existing content, services and human resources. In this work, a new approach is proposed to quantify the effectiveness of knowledge driven web application. This is named as Unique Track Measure Orientation (UTMO). The proposed UTMO computes the effectiveness from navigation of different types of information in accordance with the business value. The business value could be anything-the brand value, turnover, or cumulative profit.

i*ToNuSMV: A Prototype for Enabling Model Checking of i* Models

Goal model like i* are inherently sequence agnostic whereas standard model checkers like SPIN or NuSMV accept extended finite state models which represent an ordering of state transitions that can occur within a system. These two notions are mutually conflicting and, thus, we cannot check temporal properties (like compliance rules), described using temporal languages (like Linear Temporal Logic (LTL), or Computational Tree Logic (CTL)), on goal models by feeding them as input to a standard model checker. The i*ToNuSMV tool aims to bridge this gap such that the compliance of i* models towards temporal constraints can be verified in the early requirements phase of software development itself.