ACTIDS: An Active Strategy For Detecting And Localizing Network Attacks

In this work we investigate a new approach for detecting attacks which aim to degrade the network's Quality of Service (QoS). To this end, a new network-based intrusion detection system (NIDS) is proposed. Most contemporary NIDSs take a passive approach by solely monitoring the network's production traffic. This paper explores a complementary approach in which distributed agents actively send out periodic probes. The probes are continuously monitored to detect anomalous behavior of the network. The proposed approach takes away much of the variability of the network's production traffic that makes it so difficult to classify. This enables the NIDS to detect more subtle attacks which would not be detected using the passive approach alone. Furthermore, the active probing approach allows the NIDS to be effectively trained using only examples of the network's normal states, hence enabling an effective detection of zero-day attacks. Using realistic experiments, we show that an NIDS which also leverages the active approach is considerably more effective in detecting attacks which aim to degrade the network's QoS when compared to an NIDS which relies solely on the passive approach. Lastly, we show that the false positives rate remains very low even in the face of Byzantine faults.Comment: Full fledged pape

Machine learning \& artificial intelligence in the quantum domain

Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently, we have witnessed breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.Comment: Review paper. 106 pages. 16 figure

Towards Open Intent Discovery for Conversational Text

Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model it as a classification task with a predefined set of known categories. To generailze beyond these preexisting classes, we define a new task of \textit{open intent discovery}. We investigate how intent can be generalized to those not seen during training. To this end, we propose a two-stage approach to this task - predicting whether an utterance contains an intent, and then tagging the intent in the input utterance. Our model consists of a bidirectional LSTM with a CRF on top to capture contextual semantics, subject to some constraints. Self-attention is used to learn long distance dependencies. Further, we adapt an adversarial training approach to improve robustness and perforamce across domains. We also present a dataset of 25k real-life utterances that have been labelled via crowd sourcing. Our experiments across different domains and real-world datasets show the effectiveness of our approach, with less than 100 annotated examples needed per unique domain to recognize diverse intents. The approach outperforms state-of-the-art baselines by 5-15% F1 score points

Image Privacy Prediction Using Deep Neural Networks

Images today are increasingly shared online on social networking sites such as Facebook, Flickr, Foursquare, and Instagram. Despite that current social networking sites allow users to change their privacy preferences, this is often a cumbersome task for the vast majority of users on the Web, who face difficulties in assigning and managing privacy settings. Thus, automatically predicting images' privacy to warn users about private or sensitive content before uploading these images on social networking sites has become a necessity in our current interconnected world. In this paper, we explore learning models to automatically predict appropriate images' privacy as private or public using carefully identified image-specific features. We study deep visual semantic features that are derived from various layers of Convolutional Neural Networks (CNNs) as well as textual features such as user tags and deep tags generated from deep CNNs. Particularly, we extract deep (visual and tag) features from four pre-trained CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and ResNet, and compare their performance for image privacy prediction. Results of our experiments on a Flickr dataset of over thirty thousand images show that the learning models trained on features extracted from ResNet outperform the state-of-the-art models for image privacy prediction. We further investigate the combination of user tags and deep tags derived from CNN architectures using two settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. Our results show that even though the models trained on the visual features perform better than those trained on the tag features, the combination of deep visual features with image tags shows improvements in performance over the individual feature sets

A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2008-2018 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.Comment: To Appear in ACM Cumputing Surveys (CSUR

Conceptualizing Blockchains: Characteristics & Applications

Blockchain technology has recently gained widespread attention by media, businesses, public sector agencies, and various international organizations, and it is being regarded as potentially even more disruptive than the Internet. Despite significant interest, there is a dearth of academic literature that describes key components of blockchains and discusses potential applications. This paper aims to address this gap. This paper presents an overview of blockchain technology, identifies the blockchain's key functional characteristics, builds a formal definition, and offers a discussion and classification of current and emerging blockchain applications.Comment: 9 pages, 4 figure

Adversarial Learning for Chinese NER from Crowd Annotations

To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. In this paper, we propose an approach to performing crowd annotation learning for Chinese Named Entity Recognition (NER) to make full use of the noisy sequence labels from multiple annotators. Inspired by adversarial learning, our approach uses a common Bi-LSTM and a private Bi-LSTM for representing annotator-generic and -specific information. The annotator-generic information is the common knowledge for entities easily mastered by the crowd. Finally, we build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we create two data sets for Chinese NER tasks from two domains. The experimental results show that our system achieves better scores than strong baseline systems.Comment: 8 pages, AAAI-201

On the institutional innovation process : EU regulation through an evolutionary lens

The focal point of this paper is the study of the process of emergence of novel institutions and the identification of factors that may influence the outcome of this process. We view inst accepted sets of rules that influence We consider regulations as endogenously emerging institutions that evolve in accordance to other socioeconomic factors and analyze the regulatory process at each of its stages adopting an evolutionary approach. Evidence shows that the regulatory process resembles the innovation process as it can be viewed as a process of knowledge accumulation and transmission that is facilitate empirically contextualized in the European political system, the detergents industry and specific regulations formed at European level. Data is drawn by secondary resour of public and private stakeholders participating in the processEvolutionary theory, Institutions, Regulation, Policy

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

An Account of Opinion Implicatures

While previous sentiment analysis research has concentrated on the interpretation of explicitly stated opinions and attitudes, this work initiates the computational study of a type of opinion implicature (i.e., opinion-oriented inference) in text. This paper described a rule-based framework for representing and analyzing opinion implicatures which we hope will contribute to deeper automatic interpretation of subjective language. In the course of understanding implicatures, the system recognizes implicit sentiments (and beliefs) toward various events and entities in the sentence, often attributed to different sources (holders) and of mixed polarities; thus, it produces a richer interpretation than is typical in opinion analysis.Comment: 50 Pages. Submitted to the journal, Language Resources and Evaluatio