Afrand Agah

Intrusion detection in sensor networks: a non-cooperative game approach

Insufficiency of memory and battery power of sensors makes the security of sensor networks a hard task to do. This insufficiency also makes applying the existing methods of securing other type of networks on the sensor networks unsuitable. We propose a game theoretic framework for defensing nodes in a sensor network. We apply three different schemes for defense. Our main concern in all three schemes is finding the most vulnerable node in a sensor network and protecting it. In the first scheme we formulate attack-defense problem as a two-player, nonzero-sum, non-cooperative game between an attacker and a sensor network. We show that this game achieves Nash equilibrium and thus leading to a defense strategy for the network. In the second scheme we use Markov decision process to predict the most vulnerable sensor node. In the third scheme we use an intuitive metric (nodes traffic) and protect the node with the highest value of this metric. We evaluate the performance of each of these three schemes, and show that the proposed game framework significantly increases the chance of success in defense strategy for sensor network.

A game theory based approach for security in wireless sensor networks

Based on cooperative game theory, we propose a new technique for handling security issues in mobile wireless sensor networks. We define a game between sensor nodes and concentrate on three fundamental factors: cooperation, reputation and quality of security. Stronger cooperation between two nodes implies more reliable data communication between them. And the more a node cooperates the better is its reputation, which decreases when misbehavior is detected. When security of the network is compromised, the percentage of exposed traffic measures the quality of security of sensor nodes. By incorporating these three factors, we cluster the sensor nodes such that within a cluster, the payoff function of all sensor nodes are close to each other, where payoff is the largest possible individual gain for each sensor according to a defined utility metric. We define one strategy set for each node, which guarantees reaching to an equilibrium point for payoff function.

A non-cooperative game approach for intrusion detection in sensor networks

In this paper, we propose a new technique for handling security issues in mobile wireless sensor networks. We define a non cooperative game between an attacker and the sensor network. The attacker and the sensor network are the players of the game, where each one tries to maximize its own payoff. The sensor network tries to defend the sensor nodes against intrusions. We formulate the attack-defense game as a two-player, nonzero-sum, non-cooperative game. and show that this game achieves Nash equilibrium, thus leading to a defense strategy for the network, and significantly increases the chance of detecting intrusions.

Preventing DoS attack in sensor networks: a game theoretic approach

In order to isolate malicious nodes in wireless sensor and actor networks and provide a secure routing, we formulate the attack-defense problem as a two-player, nonzero-sum, non-cooperative game between an attacker and a wireless sensor and actor network. We show that this game achieves Nash equilibrium and thus leads to a defense strategy for the network. We propose two novel schemes for preventing denial of service attack. The first approach is called utility based dynamic source routing (UDSR). It incorporates the total utility of each route in data packets, where utility is a value that we are trying to maximize in the game theoretic approach. The second approach is based on a watch-list, where each node earns a rating from its neighbors, based on its previous cooperation in the network. Results show that the proposed game frameworks significantly increase the chance of success in defense strategy for the wireless sensor and actor networks.

Enforcing security for prevention of DoS attack in wireless sensor networks using economical modeling

This paper focuses on the assessment of the properties of security enforcement mechanisms for prevention of denial of service (DoS) attacks in wireless sensor networks. First part of the paper demonstrates the requirement for security enforcement using auction theory that allows detection of non cooperative nodes. In the proposed protocol, nodes prefer to participate in forwarding incoming packets and gaining reputation in the network. Nodes willing to do so must compete against each other. The competition is based on auction theory. We also formulate attack-defense problem as a non-cooperative nonzero-sum two-player game between an attacker and a wireless sensor network. This game achieves Nash equilibrium and thus leading to a defense strategy for the network. This approach is called utility based dynamic source routing (UDSR), which incorporates the total utility of each route in data packets. Results show that the proposed game frameworks significantly increase the chance of success in defense strategy for the wireless sensor network

Security in wireless mobile and sensor networks

Security plays a significant role in the current and future wireless mobile networks. The goal of secured communication must ensure that the data we receive is authentic, confidential and have not been altered. Because of the characteristics of wireless medium, limited protection of mobile nodes, nature of connectivity and lack of centralized managing point, wireless mobile networks are too vulnerable and often subject to attacks. In this chapter we discuss security issues and challenges in wireless mobile ad hoc and sensor networks as well as in pervasive computing infrastructures. We also describe security protocols for such environments.

Measuring influence in online social networks: With a focus on Gowalla and Brightkite

In the most basic sense, a network is any collection of objects in which some pairs of these objects are connected by links. In a network of objects, objects can be people or computers, which we refer to them as nodes of the network. Social links among friends play an important role in dictating their behavior. In online social networks (OSNs), these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions and more. Such flow of information determines the influence of a node. In this paper, we investigate the relative influence of a node, using large amount of data sets collected from Gowalla and Brightkite. We, initially, identify the correlated nodes in these large data sets using customized divide-and-conquer algorithm. Then, we measure the influence of each of these nodes using a linear function. Users who have more friends and who made more check-ins (an event when the time and the location of a particular user is recorded) are not necessarily having top influence to dictate friends behavior. Also, users who have the highest influence are those whose total number of friends are closer to the average number of nodes in the network, which is the total number of friends of each node divided by the total number of nodes. We believe that these observations can help organizations to narrow down their digital marketing strategies.

Characterizing User Influence Within Twitter

This paper explores whether influence can be quantified from public Twitter data. Compared to other social media applications, Twitter is content-centered, rather than relationship-centered. There is no indication of mutual relationships for the user within the application, making it difficult to gauge influence. By analyzing the data that already had mutual relationships, we identify the characteristics that created the boundaries of a community, and influence within it. We looked at Twitter user data, as well as Tweet data to find ways to characterize user influence among them. We measure type of users based on factors such as: those that they follow and how active they are. The Expert members are mutually agreed upon, as evidenced by their large followings, and the large number of followers who have added them to a list. They are most likely to post replies and original tweets, and are unlikely to re-tweet. Active members keep the conversation going, as evidenced by their strong followings. They are more likely than the other types to re-tweet. Passive members, the largest group, participate by liking (Favorite) tweets that they consume, encouraging experts and active members to continue their actions, and sustaining the boundaries of the group.