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Most of the recent efforts addressing the issue of data privacy have focused on devising algorithms for anonymization and diversification. Our objective is upstream of these works: we are concerned with the diagnosis of privacy risk and more specifically in this paper with l-diversity. We show that diagnosing l-diversity for various definitions of the concept is a knowledge discovery problem that can be mapped to the framework proposed by Mannila and Toivonen. The problem can therefore be solved with level-wise algorithms such as the apriori algorithm. We introduce and prove the necessary monotonicity property with respect to subset operator on attributes set for several instantiations of the l-diversity principle. We present and evaluate an algorithm based on the apriori algorithm. This algorithm computes, for instance, ˵maximum sets of attributes that can safely be published without jeopardizing sensitive attributes″, even if they were quasi-identifiers available from external sources, and ˵minimum subsets of attributes which jeopardize anonymity″. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach based on the honeybee model to improve the intrusion detection system. The power of defending the intruder from entering the hive, the effectiveness of exchanging information between the bees and the successfulness of other existing AI approaches that honey bee can be efficiently compared to, have lead us towards analyzing a new area in honeybee concerning security. Most existing systems only detect general and known attacks. Therefore a lot of malicious attacks intrude without any detection. We demonstrate the methods that use HoneybeeGuard in filtration and classification; ˵undesirable–absent″ and ˵desirable–present″, to identify a malicious packet, and detect the known and unknown intruders. [ABSTRACT FROM AUTHOR]

Privacy preservation is realized by transforming data into k-anonymous (k-anonymization) and l-diverse (l-diversification) versions while minimizing information loss. Frequency l-diversity is possibly the most practical instance of the generic l-diversity principle for privacy preservation. In this paper, we propose an algorithm for frequency l-diversification. Our primary objective is to minimize information loss. Most studies in privacy preservation have focused on k-anonymization. While simple principles of l-diversification algorithms can be obtained by adapting k-anonymization algorithms it is not straightforward for some other principles. Our algorithm, called Bucket Clustering, adapts k-member Clustering. However, in order to guarantee termination we use hashing and buckets as in the Anatomy algorithm. In order to minimize information loss we choose tuples that minimize information loss during the creation of clusters. We empirically show that our algorithm achieves low information loss with acceptable efficiency. [ABSTRACT FROM AUTHOR]

Conventional knowledge acquisition methods such as semantic knowledge, production rules and question answering systems have been addressed to a variety of typical knowledge based systems. However, very limited causal knowledge based methods have been addressed to the problem of intrusion detection. In this paper, we propose an approach based on causal knowledge reasoning for anomaly intrusion detection. Fuzzy cognitive maps (FCM) are ideal causal knowledge acquiring tool with fuzzy signed graphs which can be presented as an associative single layer neural network. Using FCM, our methodology attempt to diagnose and direct network traffic data based on its relevance to attack or normal connections. By quantifying the causal inference process we can determine the attack detection and the severity of odd packets. As such packets with low causal relations to attacks can be dropped or ignored and/or packets with high causal relations to attacks are to be highlighted. [ABSTRACT FROM AUTHOR]