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Ciupe, Aurelia, Mititica, Doru Florin, Meza, Serban, Orza, Bogdan.  2019.  Learning Agile with Intelligent Conversational Agents. 2019 IEEE Global Engineering Education Conference (EDUCON). :1100—1107.

Conversational agents assist traditional teaching-learning instruments in proposing new designs for knowledge creation and learning analysis, across organizational environments. Means of building common educative background in both industry and academic fields become of interest for ensuring educational effectiveness and consistency. Such a context requires transferable practices and becomes the basis for the Agile adoption into Higher Education, at both curriculum and operational levels. The current work proposes a model for delivering Agile Scrum training through an assistive web-based conversational service, where analytics are collected to provide an overview on learners' knowledge path. Besides its specific applicability into Software Engineering (SE) industry, the model is to assist the academic SE curriculum. A user-acceptance test has been carried out among 200 undergraduate students and patterns of interaction have been depicted for 2 conversational strategies.

Geyer, Fabien, Carle, Georg.  2018.  Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning. Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. :40-45.

Automated network control and management has been a long standing target of network protocols. We address in this paper the question of automated protocol design, where distributed networked nodes have to cooperate to achieve a common goal without a priori knowledge on which information to exchange or the network topology. While reinforcement learning has often been proposed for this task, we propose here to apply recent methods from semi-supervised deep neural networks which are focused on graphs. Our main contribution is an approach for applying graph-based deep learning on distributed routing protocols via a novel neural network architecture named Graph-Query Neural Network. We apply our approach to the tasks of shortest path and max-min routing. We evaluate the learned protocols in cold-start and also in case of topology changes. Numerical results show that our approach is able to automatically develop efficient routing protocols for those two use-cases with accuracies larger than 95%. We also show that specific properties of network protocols, such as resilience to packet loss, can be explicitly included in the learned protocol.

Kaul, Sonam Devgan, Hatzinakos, Dimitrios.  2019.  Learning Automata Based Secure Multi Agent RFID Authentication System. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.
Radio frequency identification wireless sensing technology widely adopted and developed from last decade and has been utilized for monitoring and autonomous identification of objects. However, wider utilization of RFID technologies has introduced challenges such as preserving security and privacy of sensitive data while maintaining the high quality of service. Thus, in this work, we will deliberately build up a RFID system by utilizing learning automata based multi agent intelligent system to greatly enhance and secure message transactions and to improve operational efficiency. The incorporation of these two advancements and technological developments will provide maximum benefit in terms of expertly and securely handle data in RFID scenario. In proposed work, learning automata inbuilt RFID tags or assumed players choose their optimal strategy via enlarging its own utility function to achieve long term benefit. This is possible if they transmit their utility securely to back end server and then correspondingly safely get new utility function from server to behave optimally in its environment. Hence, our proposed authentication protocol, expertly transfer utility from learning automata inbuilt tags to reader and then to server. Moreover, we verify the security and privacy of our proposed system by utilizing automatic formal prover Scyther tool.
Sahoo, Kshira Sagar, Tiwary, Mayank, Sahoo, Sampa, Nambiar, Rohit, Sahoo, Bibhudatta, Dash, Ratnakar.  2018.  A Learning Automata-Based DDoS Attack Defense Mechanism in Software Defined Networks. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. :795-797.

The primary innovations behind Software Defined Networks (SDN)are the decoupling of the control plane from the data plane and centralizing the network management through a specialized application running on the controller. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential target. Especially, Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to both control plane and forwarding plane of SDN. Asthe control layer is vulnerable to DDoS attack, the goal of this paper is to provide a defense system which is based on Learning Automata (LA) concepts. It is a self-operating mechanism that responds to a sequence of actions in a certain way to achieve a specific goal. The simulation results show that this scheme effectively reduces the TCP connection setup delay due to DDoS attack.

Kaelbling, L. P., Lozano-Pérez, T..  2017.  Learning composable models of parameterized skills. 2017 IEEE International Conference on Robotics and Automation (ICRA). :886–893.

There has been a great deal of work on learning new robot skills, but very little consideration of how these newly acquired skills can be integrated into an overall intelligent system. A key aspect of such a system is compositionality: newly learned abilities have to be characterized in a form that will allow them to be flexibly combined with existing abilities, affording a (good!) combinatorial explosion in the robot's abilities. In this paper, we focus on learning models of the preconditions and effects of new parameterized skills, in a form that allows those actions to be combined with existing abilities by a generative planning and execution system.

Kumar, Suren, Dhiman, Vikas, Koch, Parker A, Corso, Jason J..  2018.  Learning Compositional Sparse Bimodal Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40:1032—1044.

Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal perceptual domains that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on red triangles and blue squares; yet, implicitly will also have learned red squares and blue triangles. The structure of the projections and hence the compositional basis is learned automatically; no assumption is made on the ordering of the compositional elements in either modality. Although our modeling paradigm is general, we explicitly focus on a tabletop building-blocks setting. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes (blocks) in the tabletop setting. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.

Yu, Wenchao, Zheng, Cheng, Cheng, Wei, Aggarwal, Charu C., Song, Dongjin, Zong, Bo, Chen, Haifeng, Wang, Wei.  2018.  Learning Deep Network Representations with Adversarially Regularized Autoencoders. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2663-2671.

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.

Yoon, Man-Ki, Mohan, Sibin, Choi, Jaesik, Christodorescu, Mihai, Sha, Lui.  2017.  Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System. Proceedings of the Second International Conference on Internet-of-Things Design and Implementation. :191–196.

Existing techniques used for anomaly detection do not fully utilize the intrinsic properties of embedded devices. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. Our prototype applied to a real-world open-source embedded application shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.

Song, Linhai, Huang, Heqing, Zhou, Wu, Wu, Wenfei, Zhang, Yiying.  2016.  Learning from Big Malwares. Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems. :12:1–12:8.

This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.

Chen, Yuqi, Poskitt, Christopher M., Sun, Jun.  2018.  Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System. 2018 IEEE Symposium on Security and Privacy (SP). :648–660.
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.
Landsborough, Jason, Harding, Stephen, Fugate, Sunny.  2017.  Learning from Super-mutants: Searching Post-apocalyptic Software Ecosystems for Novel Semantics-preserving Transforms. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1529–1536.

In light of recent advances in genetic-algorithm-driven automated program modification, our team has been actively exploring the art, engineering, and discovery of novel semantics-preserving transforms. While modern compilers represent some of the best ideas we have for automated program modification, current approaches represent only a small subset of the types of transforms which can be achieved. In the wilderness of post-apocalyptic software ecosystems of genetically-modified and mutant programs, there exist a broad array of potentially useful software mutations, including semantics-preserving transforms that may play an important role in future software design, development, and most importantly, evolution.

Charikar, Moses, Steinhardt, Jacob, Valiant, Gregory.  2017.  Learning from Untrusted Data. Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. :47–60.

The vast majority of theoretical results in machine learning and statistics assume that the training data is a reliable reflection of the phenomena to be learned. Similarly, most learning techniques used in practice are brittle to the presence of large amounts of biased or malicious data. Motivated by this, we consider two frameworks for studying estimation, learning, and optimization in the presence of significant fractions of arbitrary data. The first framework, list-decodable learning, asks whether it is possible to return a list of answers such that at least one is accurate. For example, given a dataset of n points for which an unknown subset of $\alpha$n points are drawn from a distribution of interest, and no assumptions are made about the remaining (1 - $\alpha$)n points, is it possible to return a list of poly(1/$\alpha$) answers? The second framework, which we term the semi-verified model, asks whether a small dataset of trusted data (drawn from the distribution in question) can be used to extract accurate information from a much larger but untrusted dataset (of which only an $\alpha$-fraction is drawn from the distribution). We show strong positive results in both settings, and provide an algorithm for robust learning in a very general stochastic optimization setting. This result has immediate implications for robustly estimating the mean of distributions with bounded second moments, robustly learning mixtures of such distributions, and robustly finding planted partitions in random graphs in which significant portions of the graph have been perturbed by an adversary.

Shimada, Koki, Bentley, Peter.  2018.  Learning How to Flock: Deriving Individual Behaviour from Collective Behaviour with Multi-agent Reinforcement Learning and Natural Evolution Strategies. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :169–170.
This work proposes a method for predicting the internal mechanisms of individual agents using observed collective behaviours by multi-agent reinforcement learning (MARL). Since the emergence of group behaviour among many agents can undergo phase transitions, and the action space will not in general be smooth, natural evolution strategies were adopted for updating a policy function. We tested the approach using a well-known flocking algorithm as a target model for our system to learn. With the data obtained from this rule-based model, the MARL model was trained, and its acquired behaviour was compared to the original. In the process, we discovered that agents trained by MARL can self-organize flow patterns using only local information. The expressed pattern is robust to changes in the initial positions of agents, whilst being sensitive to the training conditions used.
Navid Aghasadeghi, University of Illinois at Urbana-Champaign, Huihua Zhao, Texas A&M University, Levi J. Hargrove, Northwestern University, Aaron D. Ames, Texas A&M University, Eric J. Perreault, Northwestern University, Timothy Bretl, University of Illinois at Urbana-Champaign.  2013.  Learning Impedance Controller Parameters for Lower-Limb Prostheses. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Impedance control is a common framework for control of lower-limb prosthetic devices. This approach requires choosing many impedance controller parameters. In this paper, we show how to learn these parameters for lower-limb prostheses by observation of unimpaired human walkers. We validate our approach in simulation of a transfemoral amputee, and we demonstrate the performance of the learned parameters in a preliminary experiment with a lower-limb prosthetic device.

D'Angelo, Mirko, Gerasimou, Simos, Ghahremani, Sona, Grohmann, Johannes, Nunes, Ingrid, Pournaras, Evangelos, Tomforde, Sven.  2019.  On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). :13–24.
Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.
Ye, S., Feigh, K., Howard, A..  2020.  Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :1186—1189.

Embodiment of actions and tasks has typically been analyzed from the robot's perspective where the robot's embodiment helps develop and maintain trust. However, we ask a similar question looking at the interaction from the human perspective. Embodied cognition has been shown in the cognitive science literature to produce increased social empathy and cooperation. To understand how human embodiment can help develop and increase trust in human-robot interactions, we created conducted a study where participants were tasked with memorizing greek letters associated with dance motions with the help of a humanoid robot. Participants either performed the dance motion or utilized a touch screen during the interaction. The results showed that participants' trust in the robot increased at a higher rate during human embodiment of motions as opposed to utilizing a touch screen device.

Taib, Abidah Mat, Othman, Nor Arzami, Hamid, Ros Syamsul, Halim, Iman Hazwam Abd.  2019.  A Learning Kit on IPv6 Deployment and Its Security Challenges for Neophytes. 2019 21st International Conference on Advanced Communication Technology (ICACT). :419–424.
Understanding the IP address depletion and the importance of handling security issues in IPv6 deployment can make IT personnel becomes more functional and helpful to the organization. It also applied to the management people who are responsible for approving the budget or organization policy related to network security. Unfortunately, new employees or fresh graduates may not really understand the challenge related to IPv6 deployment. In order to be equipped with appropriate knowledge and skills, these people may require a few weeks of attending workshops or training. Thus, of course involving some implementation cost as well as sacrificing allocated working hours. As an alternative to save cost and to help new IT personnel become quickly educated and familiar with IPv6 deployment issues, this paper presented a learning kit that has been designed to include self-learning features that can help neophytes to learn about IPv6 at their own pace. The kit contains some compact notes, brief security model and framework as well as a guided module with supporting quizzes to maintain a better understanding of the topics. Since IPv6 is still in the early phase of implementation in most of developing countries, this kit can be an additional assisting tool to accelerate the deployment of IPv6 environment in any organization. The kit also can be used by teachers and trainers as a supporting tool in the classroom. The pre-alpha testing has attracted some potential users and the findings proved their acceptance. The kit has prospective to be further enhanced and commercialized.
Lim, Yeon-sup, Srivatsa, Mudhakar, Chakraborty, Supriyo, Taylor, Ian.  2018.  Learning Light-Weight Edge-Deployable Privacy Models. 2018 IEEE International Conference on Big Data (Big Data). :1290–1295.
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.
Lim, Yeon-sup, Srivatsa, Mudhakar, Chakraborty, Supriyo, Taylor, Ian.  2018.  Learning Light-Weight Edge-Deployable Privacy Models. 2018 IEEE International Conference on Big Data (Big Data). :1290–1295.
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.
Dam, Khanh Huu The, Touili, Tayssir.  2018.  Learning Malware Using Generalized Graph Kernels. Proceedings of the 13th International Conference on Availability, Reliability and Security. :28:1–28:6.
Machine learning techniques were extensively applied to learn and detect malware. However, these techniques use often rough abstractions of programs. We propose in this work to use a more precise model for programs, namely extended API call graphs, where nodes correspond to API function calls, edges specify the execution order between the API functions, and edge labels indicate the dependence relation between API functions parameters. To learn such graphs, we propose to use Generalized Random Walk Graph Kernels (combined with Support Vector Machines). We implemented our techniques and obtained encouraging results for malware detection: 96.73% of detection rate with 0.73% of false alarms.
Jiang, Ruituo, Li, Xu, Gao, Ang, Li, Lixin, Meng, Hongying, Yue, Shigang, Zhang, Lei.  2019.  Learning Spectral and Spatial Features Based on Generative Adversarial Network for Hyperspectral Image Super-Resolution. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. :3161—3164.

Super-resolution (SR) of hyperspectral images (HSIs) aims to enhance the spatial/spectral resolution of hyperspectral imagery and the super-resolved results will benefit many remote sensing applications. A generative adversarial network for HSIs super-resolution (HSRGAN) is proposed in this paper. Specifically, HSRGAN constructs spectral and spatial blocks with residual network in generator to effectively learn spectral and spatial features from HSIs. Furthermore, a new loss function which combines the pixel-wise loss and adversarial loss together is designed to guide the generator to recover images approximating the original HSIs and with finer texture details. Quantitative and qualitative results demonstrate that the proposed HSRGAN is superior to the state of the art methods like SRCNN and SRGAN for HSIs spatial SR.

Carpineto, Claudio, Romano, Giovanni.  2017.  Learning to Detect and Measure Fake Ecommerce Websites in Search-engine Results. Proceedings of the International Conference on Web Intelligence. :403–410.

When searching for a brand name in search engines, it is very likely to come across websites that sell fake brand's products. In this paper, we study how to tackle and measure this problem automatically. Our solution consists of a pipeline with two learning stages. We first detect the ecommerce websites (including shopbots) present in the list of search results and then discriminate between legitimate and fake ecommerce websites. We identify suitable learning features for each stage and show through a prototype system termed RI.SI.CO. that this approach is feasible, fast, and highly effective. Experimenting with one goods sector, we found that RI.SI.CO. achieved better classification accuracy than that of non-expert humans. We next show that the information extracted by our method can be used to generate sector-level 'counterfeiting charts' that allow us to analyze and compare the counterfeit risk associated with different brands in a same sector. We also show that the risk of coming across counterfeit websites is affected by the particular web search engine and type of search query used by shoppers. Our research offers new insights and some very practical and useful means for analyzing and measuring counterfeit ecommerce websites in search-engine results, thus enabling targeted anti-counterfeiting actions.

Zhang, L., Restuccia, F., Melodia, T., Pudlewski, S. M..  2017.  Learning to detect and mitigate cross-layer attacks in wireless networks: Framework and applications. 2017 IEEE Conference on Communications and Network Security (CNS). :1–9.

Security threats such as jamming and route manipulation can have significant consequences on the performance of modern wireless networks. To increase the efficacy and stealthiness of such threats, a number of extremely challenging, next-generation cross-layer attacks have been recently unveiled. Although existing research has thoroughly addressed many single-layer attacks, the problem of detecting and mitigating cross-layer attacks still remains unsolved. For this reason, in this paper we propose a novel framework to analyze and address cross-layer attacks in wireless networks. Specifically, our framework consists of a detection and a mitigation component. The attack detection component is based on a Bayesian learning detection scheme that constructs a model of observed evidence to identify stealthy attack activities. The mitigation component comprises a scheme that achieves the desired trade-off between security and performance. We specialize and evaluate the proposed framework by considering a specific cross-layer attack that uses jamming as an auxiliary tool to achieve route manipulation. Simulations and experimental results obtained with a testbed made up by USRP software-defined radios demonstrate the effectiveness of the proposed methodology.

Palacio, David N., McCrystal, Daniel, Moran, Kevin, Bernal-Cárdenas, Carlos, Poshyvanyk, Denys, Shenefiel, Chris.  2019.  Learning to Identify Security-Related Issues Using Convolutional Neural Networks. 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). :140–144.
Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.