Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems

This paper presents a comparison between two alternative strategies for addressing feature selection on a well known case-based reasoning spam filtering system called SpamHunting. We present the usage of the k more predictive features and a percentage-based strategy for the exploitation of our amount of information measure. Finally, we confirm the idea that the percentage feature selection method is more adequate for spam filtering domain

Smart Buildings

This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing

Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection

Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (classifier) in network anomaly detection. We perform our experiments by employing a hybrid anomaly detection approach in network flow data. For this purpose, we extended a state-of-the-art autoencoder-based anomaly detection method by prepending a binary classifier acting as a prefilter for the anomaly detector. The method was evaluated on the publicly available real-world dataset UGR'16. Our empirical results indicate that the hybrid approach does offer a higher detection rate of known attacks than a standalone anomaly detector while still retaining the ability to detect zero-day attacks. Employing a supervised binary prefilter has increased the AUC metric by over 11%, detecting 30% more attacks while keeping the number of false positives approximately the same

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

Introspective knowledge acquisition for case retrieval networks in textual case base reasoning.

Textual Case Based Reasoning (TCBR) aims at effective reuse of information contained in unstructured documents. The key advantage of TCBR over traditional Information Retrieval systems is its ability to incorporate domain-specific knowledge to facilitate case comparison beyond simple keyword matching. However, substantial human intervention is needed to acquire and transform this knowledge into a form suitable for a TCBR system. In this research, we present automated approaches that exploit statistical properties of document collections to alleviate this knowledge acquisition bottleneck. We focus on two important knowledge containers: relevance knowledge, which shows relatedness of features to cases, and similarity knowledge, which captures the relatedness of features to each other. The terminology is derived from the Case Retrieval Network (CRN) retrieval architecture in TCBR, which is used as the underlying formalism in this thesis applied to text classification. Latent Semantic Indexing (LSI) generated concepts are a useful resource for relevance knowledge acquisition for CRNs. This thesis introduces a supervised LSI technique called sprinkling that exploits class knowledge to bias LSI's concept generation. An extension of this idea, called Adaptive Sprinkling has been proposed to handle inter-class relationships in complex domains like hierarchical (e.g. Yahoo directory) and ordinal (e.g. product ranking) classification tasks. Experimental evaluation results show the superiority of CRNs created with sprinkling and AS, not only over LSI on its own, but also over state-of-the-art classifiers like Support Vector Machines (SVM). Current statistical approaches based on feature co-occurrences can be utilized to mine similarity knowledge for CRNs. However, related words often do not co-occur in the same document, though they co-occur with similar words. We introduce an algorithm to efficiently mine such indirect associations, called higher order associations. Empirical results show that CRNs created with the acquired similarity knowledge outperform both LSI and SVM. Incorporating acquired knowledge into the CRN transforms it into a densely connected network. While improving retrieval effectiveness, this has the unintended effect of slowing down retrieval. We propose a novel retrieval formalism called the Fast Case Retrieval Network (FCRN) which eliminates redundant run-time computations to improve retrieval speed. Experimental results show FCRN's ability to scale up over high dimensional textual casebases. Finally, we investigate novel ways of visualizing and estimating complexity of textual casebases that can help explain performance differences across casebases. Visualization provides a qualitative insight into the casebase, while complexity is a quantitative measure that characterizes classification or retrieval hardness intrinsic to a dataset. We study correlations of experimental results from the proposed approaches against complexity measures over diverse casebases

Machine Learning Security

Abstrac

Interacting meaningfully with machine learning systems: Three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple “right/wrong” judgments. If the users themselves could work hand-in-hand with machine learning systems, the users’ understanding and trust of the system could improve and the accuracy of learning systems could be improved as well. We conducted three experiments to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study that investigated users’ willingness to interact with machine learning reasoning, and what kinds of feedback users might give to machine learning systems. We then investigated the viability of introducing such feedback into machine learning systems, specifically, how to incorporate some of these types of user feedback into machine learning systems, and what their impact was on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human–computer collaboration via on-the-spot interactions as a promising direction for machine learning systems and users to collaboratively share intelligence