Trusted Node-Based Algorithm to Secure Home Agent NATed IPv4 Network from IPv6 Routing Header Attacks

Providing a secure mobile communication in mixed IPv4/IPv6 networks is a challenging task. One of the most critical vulnerabilities associated with the IPv6 protocol is the routing header that potentially may be exploited by attackers to bypass the security. This paper discusses an algorithm to secure home agent network from the routing header vulnerability, where the home agent network uses IPv4 Network Address Translation (NAT) router. The algorithm also takes into account multi-hops destination in the routing header. Verification was done through implementation of the algorithm at the Home Agent modul in a testbed network. The experimental results show that the proposed algorithm provides secure communication between Correspondent nodes and Mobile Nodes that moved into the NATed network without causing a significance filtering delay

An Incentive Mechanism for Cooperative Data Replication in MANETs - A Game Theoretical Approach

Wireless ad hoc networks have seen a great deal of attention in the past years, especially in cases where no infrastructure is available. The main goal in these networks is to provide good data accessibility for participants. Because of the wireless nodes’ continuous movement, network partitioning occurs very often. In order to subside the negative effects of this partitioning and improve data accessibility and reliability, data is replicated in nodes other than the original owner of data. This duplication costs in terms of nodes’ storage space and energy. Hence, autonomous nodes may behave selfishly in this cooperative process and do not replicate data. This kind of phenomenon is referred to as a strategic situation and is best modeled and analyzed using the game theory concept. In order to address this problem we propose a game theory data replication scheme by using the repeated game concept and prove that it is in the nodes’ best interest to cooperate fully in the replication process if our mechanism is used

An Efficient Cluster-based Routing Protocol for Mobile Ad Hoc Networks

Clustering algorithm used in CBRP is a variation of simple lowest-ID clustering algorithm in which the node with a lowest ID among its neighbors is elected as the Cluster-head. Neglecting mobility and energy for selecting cluster-head is one of the weakness points of this protocol. In this paper the cluster formation algorithm is introduced, that uses the relative mobility metric, the residual energy and connectivity degree. After forming the cluster, whenever the cluster-head's energy is less than the aggregate energy of its member nodes, it remains as the cluster-head. Using NS-2 we evaluate rate of cluster-head changes, normalization routing overhead and packet delivery ratio. Comparisons denote that the proposed CBRP has better performances with respect to the original CBRP and Cross-CBRP

Protecting home agent client from IPv6 routing header vulnerability in mixed IP networks

Mixed IPv4/IPv6 networks will continue to use mobility support over tunneling mechanisms for a long period of time until the establishment of IPv6 end-to-end connectivity. Encapsulating IPv6 traffi c within IPv4 increases the level of hiding internal contents. Thus, mobility in mixed IPv4/IPv6 networks introduces new security vulnerabilities. One of the most critical vulnerabilities associated with the IPv6 protocol is the routing header that potentially may be used by attackers to bypass the network security devices. This paper proposes an algorithm (V6HAPA) for protecting home agent clients from the routing header vulnerability, considering that the home agents reside behind an IPv4 Network Address Translation (NAT) router. The experimental results show that the V6HAPA provides enough confidence to protect the home agent clients from attackers

An Incentive Mechanism for Cooperative Data Replication in MANETs - a Game Theoretical Approach

Wireless ad hoc networks have seen a great deal of attention in the past years, especially in cases where no infrastructure is available. The main goal in these networks is to provide good data accessibility for participants. Because of the wireless nodes' continuous movement, network partitioning occurs very often. In order to subside the negative effects of this partitioning and improve data accessibility and reliability, data is replicated in nodes other than the original owner of data. This duplication costs in terms of nodes' storage space and energy. Hence, autonomous nodes may behave selfishly in this cooperative process and do not replicate data. This kind of phenomenon is referred to as a strategic situation and is best modeled and analyzed using the game theory concept. In order to address this problem we propose a game theory data replication scheme by using the repeated game concept and prove that it is in the nodes' best interest to cooperate fully in the replication process if our mechanism is used

Агрессивная форма ювенильной макромастии

ПОДРОСТКИМОЛОЧНЫЕ ЖЕЛЕЗЫ ЧЕЛОВЕКА /АНОМАЛБОЛЕЗНИ РЕДКИЕМАКРОМАСТИЯГИПЕРТРОФИЯМОЛОЧНОЙ ЖЕЛЕЗЫ ПЛАСТИКАХИРУРГИЧЕСКИЕ ОПЕРАЦИИ ВОССТАНОВИТЕЛЬНЫЕМОЛОЧНОЙ ЖЕЛЕЗЫ БОЛЕЗНИ /ТЕР /ХИ

Proceedings from the 1st Albaha University–Uppsala University Collaborative Symposium on Quality in Computing Education

This is the proceedings from the first AlBaha University - Uppsala University Collaborative Symposium on Quality in Computing Education (ABU3QCE), held in AlBaha, Saudi Arabia, 24-25 February 2015. ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on enhancing quality in computing education. Contributions cover a broad spectrum of computing education challenges ranging from; computer science, computer engineering, computer information systems, computer information technology to software engineering education. ABU3QCE aims to publish research that combines teaching and learning experience with theoretically founded research within the field. The proceedings papers cover a wide range of topics such as cultural aspects of teaching and learning, technology enhanced teaching, and professional competencies and their role in the curriculum and in higher education. The symposium is a collaborative initiative of AlBaha University, Saudi Arabia, and Uppsala University, Sweden. It is our hope that this symposium will highlight current efforts, and also be the starting point for discussions, and inspire others to contribute to take the quality of computing education one step further

Proceedings from the 1st Albaha University–Uppsala University Collaborative Symposium on Quality in Computing Education

This is the proceedings from the first AlBaha University - Uppsala University Collaborative Symposium on Quality in Computing Education (ABU3QCE), held in AlBaha, Saudi Arabia, 24-25 February 2015. ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on enhancing quality in computing education. Contributions cover a broad spectrum of computing education challenges ranging from; computer science, computer engineering, computer information systems, computer information technology to software engineering education. ABU3QCE aims to publish research that combines teaching and learning experience with theoretically founded research within the field. The proceedings papers cover a wide range of topics such as cultural aspects of teaching and learning, technology enhanced teaching, and professional competencies and their role in the curriculum and in higher education. The symposium is a collaborative initiative of AlBaha University, Saudi Arabia, and Uppsala University, Sweden. It is our hope that this symposium will highlight current efforts, and also be the starting point for discussions, and inspire others to contribute to take the quality of computing education one step further

A Mixed Clustering Approach for Real-Time Anomaly Detection

Anomaly detection in real-time data is accepted as a vital area of research. Clustering techniques have effectively been applied for the detection of anomalies several times. As the datasets are real time, the time of data generation is important. Most of the existing clustering-based methods either follow a partitioning or a hierarchical approach without addressing time attributes of the dataset distinctly. In this article, a mixed clustering approach is introduced for this purpose, which also takes time attributes into consideration. It is a two-phase method that first follows a partitioning approach, then an agglomerative hierarchical approach. The dataset can have mixed attributes. In phase one, a unified metric is used that is defined based on mixed attributes. The same metric is also used for merging similar clusters in phase two. Tracking of the time stamp associated with each data instance is conducted simultaneously, producing clusters with different lifetimes in phase one. Then, in phase two, the similar clusters are merged along with their lifetimes. While merging the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are merged using superimposition operation, producing a fuzzy time interval. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging to sparse clusters, not belonging to any of the clusters or falling in the fuzzy lifetimes with low membership values can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies. The experimental results with a real world dataset and a synthetic dataset show that the proposed algorithm can detect the anomalies with 90% and 98% accuracy, respectively

An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior, which are assumed to be potential intrusions or threats. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behaviors are hard to predict as there is no precise boundary differentiating one from another. Here, rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach consisting of rough set theory and intuitionistic fuzzy set theory is proposed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of both rough set and intuitionistic fuzzy set to deal with inherent uncertainty, vagueness, and indiscernibility in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. A data instance can possibly or certainly belong to a class with degrees of membership and non-membership. The empirical study with a real-world and a synthetic dataset demonstrates that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29%, respectively