Secure Multi-channel Group Key Generation in Wireless Networks with Multiple Cooperating Attackers

The broadcast nature of communication channels in infra-structureless wireless networks poses challenges to security. The problem becomes more difficult in settings with multiple cooperating attackers (and full knowledge of the protocol). We propose a key generation algorithm, the Secure Multi-channel Group Key Generation (MuGKeG), which is independent of variations in channel conditions, remains effective in the presence of multiple cooperating attackers and places no pre-requisite conditions on eavesdropper’s locations and channel conditions. Analytical closed form solution for the entropy of the secret group key generated has been derived and verified using NS-3 simulations. Real network implementation using USRP kits is under progress.

Predicting New Collaborations in Academic Citation Networks of IEEE and ACM Conferences

Of key interest in the study of social networks is their evolution with time. Among social networks, collaboration networks have been the subject of significant interest with regards to link prediction. The accurate prediction of the appearance of new collaborations between members of a collaboration network can help accelerate the realization of new synergies, foster innovation and raise productivity. For this study, the authors collect a large data set of publications in 630 conferences of the IEEE and ACM containing more than 257; 000 authors, 61; 000 papers, capturing more than 818; 000 collaborations spanning a period of 10 years. The data set is rich in semantic data that allows the exploration of many features that were not considered in previous approaches.

Post Summarization of Micro-Blogs of Sporting Events

Social networking sites e.g. Facebook, Twitter, LinkedIn are becoming popular among users as they are successful in connecting people and have become a great means of information dissemination, communication and entertainment. Popularity of these services motivates us to study characteristics of online social networks. In 2012, more than 500 million users have subscribed Twitter, generating over 340 million posts and 1.6 billion search queries each day. Due to the enormous number of posts generated by Twitter, it is often difficult to understand what is being said by people on a specific topic. In this research, we perform post summarization of critical events using twitter posts on three sporting events.

VNC: Vehicular Network Coverage for Multihop Quality-of-Service Data Delivery

ANDASH group, in collaboration with Sungkyunkwan University, proposes a Vehicular Network Coverage (VNC) algorithm for providing vehicles with the Quality-of-Service (QoS) Data Delivery in vehicular networks using infrastructure nodes, such as Road-Side Units (RSUs) and Relay Nodes. Researchers have considered two multihop data delivery directions, such as outbound delivery from vehicle to RSU and inbound delivery from RSU to vehicle. We define the QoS of data delivery with the mean and standard deviation of End-toEnd data delivery delay. For the given requirements of both outbound and inbound QoS delivery, we need to deploy an appropriate number of RSUs. In this research, we propose the optimal deployment of RSUs.

Deep packet inspection probe deployment

AN-DASH group, Dr. Usman Younis and a team of undergraduate students fro SEECS worked closely with Tellabs and WiChorus to install a deep packet inspection (DPI) probe in the SEECS Data Center. The probe is being used to generate traffic analytics of SEECS entire incoming and outgoing network traffic.

Agilent long-range RF sensor testbed

AN-DASH, in collaboration with Agilent Technologies and their local partner Mushko, has deployed a long range, passive RF sensor testbed on the campus of NUST. This testbed consists of four (4) Agilent 57200 RF sensors spread over an area of approximately 1 square Km. The sensors are currently equipped with antennas to scan the RF spectrum from 30KHz upto 1.1GHz and locate transmitters.

Social unrest detection by monitoring information flow on Twitter

The popular uprisings in a number of countries in the Middle East and North Africa in the Spring of 2011 were enabled in large part by local populations' access to social networking services such as Twitter and Facebook. This paper attempts to use language independent features of Twitter traffic mentioning different countries to distinguish between countries that are politically unstable and others that are stable. Towards this end, we collected several data sets of countries that were experiencing political unrest during the period now known as the "Arab Spring", as well as a set of countries that were not. Several different methods are used to model the flow of information between Twitter users.

Identifying Influential Neighborhoods

Estimates node’s importance by virtue of its criticality to the control to disrupt the flow of commodity in a network. We identify influential nodes and Influential neighborhoods. PCC is based on PCA and the KLT which takes the view of treating a graphs adjacency matrix as a covariance matrix PCC allows the addition of more features for the computation of node centralities PCC of a graph gives a form of compact representation that identifies influential nodes and influential neighborhoods.

Influential Nodes Identification in Online Social Networks

Notion of a central node in a network changes with application and the type of commodity flowing through a network we identify social hubs, nodes at the center of influential neighborhoods. Applications include friendship graph of 70,000 users service of Google’s Orkut service, a gaming graph of 143,020 users from Facebook’s ‘Fighters’ Club’ application. Basec idea is the use of Principal Component Centrality (PCC).

Distributed and Privacy Preserving Algorithm for Identifying Information Hubs

Identifying the top-k information hubs in a social network. Our method can identify hubs without requiring a central entity (enhancement of Kempe-McSherry algorithm). Applications include a Facebook data set of ~3.1 million users having more than 23 million links. Performance shows 50% more accuracy than existing distributed algorithms. Our scheme can rank top-k information hubs more accurately than existing approaches.

Behaviour of Facebook Users

We study the probability with which a friend belonging to a particular group of friends will interact from other friends belonging / not belonging to his own circle of friends. We collected data set of Facebook profiles of 50 volunteers. Our study shows that information propagating through Facebook tends to coalesce (to some extent) within communities.

Data Collection and Analysis

We collected and analysed Data of various Twitter events: Malala's diary (Blogs at BBC Urdu) September 2008, National Youth Peace Prize 19th December 2011, Assassination Attempt 9th October 2012, Malala Day, Speech at UN, 12th July 2013, meeting with Obama, 11 October 2013, 2013 Nobel Prize Nomination.

Identifying Leaders and Followers in Online Social Networks

Longitudinal User Centered Influence (LUCI) model. Input includes user interaction information. Clusters users into four categories: Introvert leaders, Extrovert leaders, Followers, Neutrals. Data set collected from "Everything2" with more than 3 million users over the duration of one year. Our average classification accuracy of up to 90.3% in classifying users as leaders and followers

Tag Clouds of Twitter Trending Topics

Collection of tweets on trending topics. Removing the stop words and building a graph using “NetworkX”. Creating word clouds based on eigenvector centralities. Application of principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC).

Sentiment Detection in Tweets

Classifies tweets by the positive or negative sentiments expressed in them. Performs sentiment tracking over time. Implemented in the form of a website at http://tweet-mood-check.appspot.com/.

Press Release Tracking System

An automated system that tracks the amount of information contained in a press release that is at every stage as talking points wind their way from Client / company / organization -> PR company -> Websites of news outlets (Dawn, Tribune, GeoTV). Allows performance analysis of press releases prepared by PR firms.

First report detection

Early detection of first reports / eyewitness reports of events / accidents / incidents critical to clients of Global Rescue. Early detection allows Global Rescue to pre-emptively prepare to evacuate clients from developing dangerous situations.