Biblio

Apriori Algorithm is the most Classic Association Rule Mining Algorithm, which has unique advantages, but it also has some disadvantages such as high overhead. This paper first describes Apriori Algorithm, points out its shortcomings, introduces related concepts, and then proposes a method based on Hash technology and compressed combination item set technology to improve APRIORI algorithm. This paper introduces the basic idea and the concrete process of the improvement in detail, analyzes the efficiency of the improved algorithm by the experiment, and advances the application of the improved algorithm in the library personalized service.

Software-defined networking (SDN) separates the control plane from underlying devices, and allows it to control the data plane from a global view. While SDN brings conveniences to management, it also introduces new security threats. Knowing reactive rules, attackers can launch denial-of-service (DoS) attacks by sending numerous rule-matched packets which trigger packet-in packets to overburden the controller. In this work, we present a novel method ``INferring SDN by Probing and Rule Extraction'' (INSPIRE) to discover the flow rules in SDN from probing packets. We evaluate the delay time from probing packets, classify them into defined classes, and infer the rules. This method involves three relevant steps: probing, clustering and rule inference. First, forged packets with various header fields are sent to measure processing and propagation time in the path. Second, it classifies the packets into multiple classes by using k-means clustering based on packet delay time. Finally, the apriori algorithm will find common header fields in the classes to infer the rules. We show how INSPIRE is able to infer flow rules via simulation, and the accuracy of inference can be up to 98.41% with very low false-positive rates.

Pervasive Computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work we review these general issues and propose a Pervasive Computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some Artificial Intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones.