Controllability of Social Networks and the Strategic Use of Random Information

This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network, and considers two well-known strategies for influencing social contexts. One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad-hoc metrics defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests. The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills, supporting our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable

Inter-arrival times of message propagation on directed networks

One of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets, networks of infected computers used to send viruses, unsolicited commercial emails (SPAM) or denial of service attacks. We map this problem to the propagation of multiple random walkers on directed networks and we evaluate the inter-arrival time distribution between successive walkers arriving at a target. We show that the temporal organization of this process, which models information propagation on unstructured peer to peer networks, has the same features as SPAM arriving to a single user. We study the behavior of the message inter-arrival time distribution on three different network topologies using two different rules for sending messages. In all networks the propagation is not a pure Poisson process. It shows universal features on Poissonian networks and a more complex behavior on scale free networks. Results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies, by studying inter-arrival times at any node of the network.Comment: 9 pages, 12 figure

The Architectural Dynamics of Encapsulated Botnet Detection (EDM)

Botnet is one of the numerous attacks ravaging the networking environment. Its approach is said to be brutal and dangerous to network infrastructures as well as client systems. Since the introduction of botnet, different design methods have been employed to solve the divergent approach but the method of taking over servers and client systems is unabated. To solve this, we first identify Mpack, ICEpack and Fiesta as enhanced IRC tool. The analysis of its role in data exchange using OSI model was carried out. This further gave the needed proposal to the development of a High level architecture representing the structural mechanism and the defensive mechanism within network server so as to control the botnet trend. Finally, the architecture was designed to respond in a proactive state when scanning and synergizing the double data verification modules in an encapsulation manner within server system

Centrality Measures for Networks with Community Structure

Understanding the network structure, and finding out the influential nodes is a challenging issue in the large networks. Identifying the most influential nodes in the network can be useful in many applications like immunization of nodes in case of epidemic spreading, during intentional attacks on complex networks. A lot of research is done to devise centrality measures which could efficiently identify the most influential nodes in the network. There are two major approaches to the problem: On one hand, deterministic strategies that exploit knowledge about the overall network topology in order to find the influential nodes, while on the other end, random strategies are completely agnostic about the network structure. Centrality measures that can deal with a limited knowledge of the network structure are required. Indeed, in practice, information about the global structure of the overall network is rarely available or hard to acquire. Even if available, the structure of the network might be too large that it is too much computationally expensive to calculate global centrality measures. To that end, a centrality measure is proposed that requires information only at the community level to identify the influential nodes in the network. Indeed, most of the real-world networks exhibit a community structure that can be exploited efficiently to discover the influential nodes. We performed a comparative evaluation of prominent global deterministic strategies together with stochastic strategies with an available and the proposed deterministic community-based strategy. Effectiveness of the proposed method is evaluated by performing experiments on synthetic and real-world networks with community structure in the case of immunization of nodes for epidemic control.Comment: 30 pages, 4 figures. Accepted for publication in Physica A. arXiv admin note: text overlap with arXiv:1411.627

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Evidence Collection for Forensic Investigation in Peer to Peer Systems

Abstract Peer to Peer(P2P) file sharing networks are amongst the best free sources of information on the internet. Voluntary participation and lack of control makes them a very attractive option to share data anonymously. However a small group of people take advantage of the freedom provided by these networks and share content that is prohibited by law. Apart from copyrighted content, there are cases where people share les related to Child Pornography which is a criminal offense. Law enforcement attempts to track down these offenders by obtaining a court order for search and seizure of computers at a suspect location. These seized computers are forensically examined using storage and memory-forensics tools. However before the search warrant is issued strong evidence must be presented to provide a reason for suspiscion. Deficient investigation in the intial stages might lead to mis-identification of the source and steer the investigation in a wrong direction. Initial evidence collection on peer to peer le sharing networks is a challenge due to the lack of a central point of control and highly dynamic nature of the networks. The goal of this work is to create a working prototype of an initial evidence collection tool for forensics in P2P networks. The prototype is based on the idea that P2P networks could be monitored by introducing modified peer nodes onto the network for a certain time period and recording relevant information about nodes that possess criminally offensive content. Logging information sent by a suspicious node along with timestamps and unique identication information would provide a strong, verfiiable initial evidence. This work presents one such working prototype in alignment with the goals stated above

Multiple Scale-Free Structures in Complex Ad-Hoc Networks

This paper develops a framework for analyzing and designing dynamic networks comprising different classes of nodes that coexist and interact in one shared environment. We consider {\em ad hoc} (i.e., nodes can leave the network unannounced, and no node has any global knowledge about the class identities of other nodes) {\em preferentially grown networks}, where different classes of nodes are characterized by different sets of local parameters used in the stochastic dynamics that all nodes in the network execute. We show that multiple scale-free structures, one within each class of nodes, and with tunable power-law exponents (as determined by the sets of parameters characterizing each class) emerge naturally in our model. Moreover, the coexistence of the scale-free structures of the different classes of nodes can be captured by succinct phase diagrams, which show a rich set of structures, including stable regions where different classes coexist in heavy-tailed and light-tailed states, and sharp phase transitions. Finally, we show how the dynamics formulated in this paper will serve as an essential part of {\em ad-hoc networking protocols}, which can lead to the formation of robust and efficiently searchable networks (including, the well-known Peer-To-Peer (P2P) networks) even under very dynamic conditions

Scalability and egalitarianism in peer-to-peer networks

Many information-technology innovations are driven, in their early stages, by an egalitarian ethos that empowers individuals through dis-intermediation. Bitcoin and peer to peer financial systems were inspired by these egalitarian ambitions. However, in bitcoin we have recently witnessed a strong centralization around a few large mining pools, which puts control of most of the system in the hands of a few. In this chapter we investigate the physical limits of distributed consensus mechanisms over networks, and discuss whether there are scalability and efficiency reasons that incentivize centralization. We compute the time to reach majority consensus in a variety of settings, comparing egalitarian networks with centralized networks, and quantifying the effect of network topology on the propagation of information