Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

The performance of modularity maximization in practical contexts

Although widely used in practice, the behavior and accuracy of the popular module identification technique called modularity maximization is not well understood in practical contexts. Here, we present a broad characterization of its performance in such situations. First, we revisit and clarify the resolution limit phenomenon for modularity maximization. Second, we show that the modularity function Q exhibits extreme degeneracies: it typically admits an exponential number of distinct high-scoring solutions and typically lacks a clear global maximum. Third, we derive the limiting behavior of the maximum modularity Q_max for one model of infinitely modular networks, showing that it depends strongly both on the size of the network and on the number of modules it contains. Finally, using three real-world metabolic networks as examples, we show that the degenerate solutions can fundamentally disagree on many, but not all, partition properties such as the composition of the largest modules and the distribution of module sizes. These results imply that the output of any modularity maximization procedure should be interpreted cautiously in scientific contexts. They also explain why many heuristics are often successful at finding high-scoring partitions in practice and why different heuristics can disagree on the modular structure of the same network. We conclude by discussing avenues for mitigating some of these behaviors, such as combining information from many degenerate solutions or using generative models.Comment: 20 pages, 14 figures, 6 appendices; code available at http://www.santafe.edu/~aaronc/modularity

Defensive Alliances in Signed Networks

The analysis of (social) networks and multi-agent systems is a central theme in Artificial Intelligence. Some line of research deals with finding groups of agents that could work together to achieve a certain goal. To this end, different notions of so-called clusters or communities have been introduced in the literature of graphs and networks. Among these, defensive alliance is a kind of quantitative group structure. However, all studies on the alliance so for have ignored one aspect that is central to the formation of alliances on a very intuitive level, assuming that the agents are preconditioned concerning their attitude towards other agents: they prefer to be in some group (alliance) together with the agents they like, so that they are happy to help each other towards their common aim, possibly then working against the agents outside of their group that they dislike. Signed networks were introduced in the psychology literature to model liking and disliking between agents, generalizing graphs in a natural way. Hence, we propose the novel notion of a defensive alliance in the context of signed networks. We then investigate several natural algorithmic questions related to this notion. These, and also combinatorial findings, connect our notion to that of correlation clustering, which is a well-established idea of finding groups of agents within a signed network. Also, we introduce a new structural parameter for signed graphs, signed neighborhood diversity snd, and exhibit a parameterized algorithm that finds a smallest defensive alliance in a signed graph

Cohesive subgraph identification in large graphs

Graph data is ubiquitous in real world applications, as the relationship among entities in the applications can be naturally captured by the graph model. Finding cohesive subgraphs is a fundamental problem in graph mining with diverse applications. Given the important roles of cohesive subgraphs, this thesis focuses on cohesive subgraph identification in large graphs. Firstly, we study the size-bounded community search problem that aims to find a subgraph with the largest min-degree among all connected subgraphs that contain the query vertex q and have at least l and at most h vertices, where q, l, h are specified by the query. As the problem is NP-hard, we propose a branch-reduce-and-bound algorithm SC-BRB by developing nontrivial reducing techniques, upper bounding techniques, and branching techniques. Secondly, we formulate the notion of similar-biclique in bipartite graphs which is a special kind of biclique where all vertices from a designated side are similar to each other, and aim to enumerate all maximal similar-bicliques. We propose a backtracking algorithm MSBE to directly enumerate maximal similar-bicliques, and power it by vertex reduction and optimization techniques. In addition, we design a novel index structure to speed up a time-critical operation of MSBE, as well as to speed up vertex reduction. Efficient index construction algorithms are developed. Thirdly, we consider balanced cliques in signed graphs --- a clique is balanced if its vertex set can be partitioned into CL and CR such that all negative edges are between CL and CR --- and study the problem of maximum balanced clique computation. We propose techniques to transform the maximum balanced clique problem over G to a series of maximum dichromatic clique problems over small subgraphs of G. The transformation not only removes edge signs but also sparsifies the edge set

Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms

Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target\u27s resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets. To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target

From cellular networks to mobile cloud computing: security and efficiency of smartphone systems.

In my first year of my Computer Science degree, if somebody had told me that the few years ahead of me could have been the last ones of the so-called PC-era, I would have hardly believed him. Sure, I could imagine computers becoming smaller, faster and cheaper, but I could have never imagined that in such a short time the focus of the market would have so dramatically shifted from PCs to personal devices. Today, smartphones and tablets have become our inseparable companions, changing for the better numerous aspects of our daily life. The way we plan our days, we communicate with people, we listen to music, we search for information, we take pictures, we spend our free time and the way we note our ideas has been totally revolutionized thanks to them. At the same time, thanks also to the rapid growth of the Cloud Computing based services, most of our data and of the Internet services that we use every day are just a login-distance away from any device connected to the Internet that we can find around us. We can edit our documents, look our and our friends’ pictures and videos, share our thoughts, access our bank account, pay our taxes using a familiar interface independently from where we are. What is the most fascinating thing is that all these new possibilities are not anymore at the hand of technically-savvy geeks only, but they are available to newer and older generations alike thanks to the efforts that recently have been put into building user interfaces that feel more natural and intuitive even to totally unexperienced users. Despite of that, we are still far from an ideal world. Service providers, software engineers, hardware manufacturers and security experts are having a hard time in trying to satisfy the always growing expectations of a number of users that is steadily increasing every day. People are always longing for faster mobile connectivity at lower prices, for longer lasting batteries and for more powerful devices. On top of that, users are more and more exposed to new security threats, either because they tend to ignore even the most basic security-practices, or because virus writers have found new ways to exploit the now world-sized market of mobile devices. For instance, more people accessing the Internet from their mobile devices forces the existing network infrastructure to be continuously updated in order to cope with the constantly increase in data consumption. As a consequence, AT&T’s subscribers in the United States were getting extremely slow or no service at all because of the mobile network straining to meet iPhone users’ demand [5]. The company switched from unlimited traffic plans to tiered pricing for mobile data users in summer 2010. Similarly, Dutch T-Mobile’s infrastructure has not been able to cope with intense data traffic, thus forcing the company to issue refunds for affected users [6]. Another important aspect is that of mobile security. Around a billion of people today have their personal information on Facebook and half of them access Facebook from their mobile phone [7]; the size of the online-banking in America has almost doubled since 2004, with 16% of the American mobile users conducting financial-related activities from their mobile device [8]; on 2010, customers spent one billion of dollars buying products on Amazon via mobile devices [9]. These numbers give an idea of the amount of people that today could find themselves in trouble by not giving enough care into protecting their mobile device from unauthorized access. A distracted user who loses his phone, or just forgets it in a public place, even if for a short time only, could allow someone else to get unrestrained access to his online identity. By copying the contents of the phone, including passwords and access keys, an attacker could steal money from the user’s bank account, read the user’s emails, steal the user’s personal files stored on the cloud, use the user’s personal information to conduct scams, frauds, and other crimes using his name and so on. But identity theft is not the only security problem affecting mobile users. Between 2011 and 2012, the number of unique viruses and malwares targeting mobile devices has increased more than six times, according to a recent report [10]. Typically, these try to get installed in the target device by convincing the user to download an infected app, or by making them follow a link to a malicious web site. The problems just exposed are major issues affecting user’s experience nowadays. We believe that finding effective, yet simple and widely adoptable solutions may require a new point of view, a shift in the way these problems are tackled. For these reasons, we evaluated the possibility of using a hybrid approach, that is, one where different technologies are brought together to create new, previously unexplored solutions. We started by considering the issues affecting the mobile network infrastructure. While it is true that the usage of mobile connectivity has significantly increased over the past few years, it is also true that socially close users tend to be interested in the same content, like, the same Youtube videos, the same application updates, the same news and so on. By knowing that, operators, instead of spending billions [11] to update their mobile network, could try an orthogonal approach and leverage an ad-hoc wireless network between the mobile devices, referred to in literature as Pocket Switched Networks [12]. Indeed, most of the smartphones on the market today are equipped with short-ranged radio interfaces (i.e., Bluetooth, WiFi) that allow them to exchange data whenever they are close enough to each other. Popular data could be then stored and transferred directly between devices in the same social context in an ad-hoc fashion instead of being downloaded multiple times from the mobile network. We therefore studied the possibility of channeling traffic to a few, socially important users in the network called VIP delegates, that can help distributing contents to the rest of the network. We evaluated VIP selection strategies that are based on the properties of the social network between mobile devices users. In Chapter 2, through extensive evaluations with real and synthetic traces, we show the effectiveness of VIP delegation both in terms of coverage and required number of VIPs – down to 7% in average of VIPs are needed in campus-like scenarios to offload about 90% of the traffic. These results have also been presented in [1]. Next we moved to the security issues. On of the highest threats to the security of mobile users is that of an identity theft performed using the data stored on the device. The problem highlighted by this kind of attacks is that the most commonly used authentication mechanisms completely fail to distinguish the honest user from somebody who just happens to know the user’s login credentials or private keys. To be resistant to identity theft attacks, an authentication mechanism should, instead, be built to leverage some intrinsic and difficult to replicate characteristic of each user. We proposed the Personal Marks and Community Certificates systems with this aim in mind. They constitute an authentication mechanism that uses the social context sensed by the smartphone by means of Bluetooth or WiFi radios as a biometric way to identify the owner of a device. Personal Marks is a simple cryptographic protocol that works well when the attacker tries to use the stolen credentials in the social community of the victim. Community Certificates works well when the adversary has the goal of using the stolen credentials when interacting with entities that are far from the social network of the victim. When combined, these mechanisms provide an excellent protection against identity theft attacks. In Chapter 3 we prove our ideas and solutions with extensive simulations in both simulated and real world scenarios—with mobility traces collected in a real life experiment. This study appeared in [2]. Another way of accessing the private data of a user, other than getting physical access to his device, could be by means of a malware. An emerging trend in the way people are fooled into installing malware-infected apps is that of exploiting existing trust relationships between socially close users, like those between Facebook friends. In this way, the malware can rapidly expand through social links from a small set of infected devices towards the rest of the network. In our quest for hybrid solutions to the problem of malware spreading in social networks of mobile users we developed a novel approach based on the Mobile Cloud Computing paradigm. In this new paradigm, a mobile device can alleviate the burden of computationally intensive tasks by offloading them to a software clone running on the cloud. Also, the clones associated to devices of users in the same community are connected in a social peer-to-peer network, thus allowing lightweight content sharing between friends. CloudShield is a suite of protocols that provides an efficient way stop the malware spread by sending a small set of patches from the clones to the infected devices. Our experiments on different datasets show that CloudShield is able to better and more efficiently contain malware spreading in mobile wireless networks than the state-of-the-art solutions presented in literature. These findings (which are not included in this dissertation) appeared in [3] and are the result of a joint work with P.h.D student S. Kosta from Sapienza University. My main contribution to this work was in the simulation of both the malware spreading and of the patching protocol schemes on the different social networks datasets. The Mobile Cloud Computing paradigm seems to be an excellent resource for mobile systems. It alleviates battery consumption on smartphones, it helps backing up user’s data on-the-fly and, as CloudShield proves, it can also be used to find new, effective, solutions to existing problems. However, the communication between the mobile devices and their clones needed by such paradigm certainly does not come for free. It costs both in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). Being aware of the issues that heavy computation or communication can cause to both the battery life of the devices [13], and to the mobile infrastructure, we decided to study the actual feasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we considered two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user’s data and apps is needed. In Chapter 5 we give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieved this by means measurements done on a real testbed of 11 Android smartphones and on their relative clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration. This study has been presented in [4] and is the result of a collaboration with P.h.D. Student S. Kosta from Sapienza University. S. Kosta mainly contributed to the experimental setup, deployment of the testbed and data collection