Visible to the public Biblio

Found 12094 results

Conference Paper
Chhillar, Dheeraj, Sharma, Kalpana.  2019.  ACT Testbot and 4S Quality Metrics in XAAS Framework. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :503–509.

The purpose of this paper is to analyze all Cloud based Service Models, Continuous Integration, Deployment and Delivery process and propose an Automated Continuous Testing and testing as a service based TestBot and metrics dashboard which will be integrated with all existing automation, bug logging, build management, configuration and test management tools. Recently cloud is being used by organizations to save time, money and efforts required to setup and maintain infrastructure and platform. Continuous Integration and Delivery is in practice nowadays within Agile methodology to give capability of multiple software releases on daily basis and ensuring all the development, test and Production environments could be synched up quickly. In such an agile environment there is need to ramp up testing tools and processes so that overall regression testing including functional, performance and security testing could be done along with build deployments at real time. To support this phenomenon, we researched on Continuous Testing and worked with industry professionals who are involved in architecting, developing and testing the software products. A lot of research has been done towards automating software testing so that testing of software product could be done quickly and overall testing process could be optimized. As part of this paper we have proposed ACT TestBot tool, metrics dashboard and coined 4S quality metrics term to quantify quality of the software product. ACT testbot and metrics dashboard will be integrated with Continuous Integration tools, Bug reporting tools, test management tools and Data Analytics tools to trigger automation scripts, continuously analyze application logs, open defects automatically and generate metrics reports. Defect pattern report will be created to support root cause analysis and to take preventive action.

Zhang, H., Ma, J., Wang, Y., Pei, Q..  2009.  An Active Defense Model and Framework of Insider Threats Detection and Sense. 2009 Fifth International Conference on Information Assurance and Security. 1:258—261.
Insider attacks is a well-known problem acknowledged as a threat as early as 1980s. The threat is attributed to legitimate users who take advantage of familiarity with the computational environment and abuse their privileges, can easily cause significant damage or losses. In this paper, we present an active defense model and framework of insider threat detection and sense. Firstly, we describe the hierarchical framework which deal with insider threat from several aspects, and subsequently, show a hierarchy-mapping based insider threats model, the kernel of the threats detection, sense and prediction. The experiments show that the model and framework could sense the insider threat in real-time effectively.
Schulz, A., Kotson, M., Meiners, C., Meunier, T., O’Gwynn, D., Trepagnier, P., Weller-Fahy, D..  2017.  Active Dependency Mapping: A Data-Driven Approach to Mapping Dependencies in Distributed Systems. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :84–91.

We introduce Active Dependency Mapping (ADM), a method for establishing dependency relations among a set of interdependent services. The approach is to artificially degrade network performance to infer which assets on the network support a particular process. Artificial degradation of the network environment could be transparent to users; run continuously it could identify dependencies that are rare or occur only at certain timescales. A useful byproduct of this dependency analysis is a quantitative assessment of the resilience and robustness of the system. This technique is intriguing for hardening both enterprise networks and cyber physical systems. We present a proof-of-concept experiment executed on a real-world set of interrelated software services. We assess the efficacy of the approach, discuss current limitations, and suggest options for future development of ADM.

Huang, Lulu, Matwin, Stan, de Carvalho, Eder J., Minghim, Rosane.  2017.  Active Learning with Visualization for Text Data. Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics. :69–74.

Labeled datasets are always limited, and oftentimes the quantity of labeled data is a bottleneck for data analytics. This especially affects supervised machine learning methods, which require labels for models to learn from the labeled data. Active learning algorithms have been proposed to help achieve good analytic models with limited labeling efforts, by determining which additional instance labels will be most beneficial for learning for a given model. Active learning is consistent with interactive analytics as it proceeds in a cycle in which the unlabeled data is automatically explored. However, in active learning users have no control of the instances to be labeled, and for text data, the annotation interface is usually document only. Both of these constraints seem to affect the performance of an active learning model. We hypothesize that visualization techniques, particularly interactive ones, will help to address these constraints. In this paper, we implement a pilot study of visualization in active learning for text classification, with an interactive labeling interface. We compare the results of three experiments. Early results indicate that visualization improves high-performance machine learning model building with an active learning algorithm.

Lai, J., Duan, B., Su, Y., Li, L., Yin, Q..  2017.  An active security defense strategy for wind farm based on automated decision. 2017 IEEE Power Energy Society General Meeting. :1–5.

With the development of smart grid, information and energy integrate deeply. For remote monitoring and cluster management, SCADA system of wind farm should be connected to Internet. However, communication security and operation risk put forward a challenge to data network of the wind farm. To address this problem, an active security defense strategy combined whitelist and security situation assessment is proposed. Firstly, the whitelist is designed by analyzing the legitimate packet of Modbus on communication of SCADA servers and PLCs. Then Knowledge Automation is applied to establish the Decision Requirements Diagram (DRD) for wind farm security. The D-S evidence theory is adopted to assess operation situation of wind farm and it together with whitelist offer the security decision for wind turbine. This strategy helps to eliminate the wind farm owners' security concerns of data networking, and improves the integrity of the cyber security defense for wind farm.

Yap, B. L., Baskaran, V. M..  2016.  Active surveillance using depth sensing technology \#8212; Part I: Intrusion detection. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). :1–2.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Vasile, D. C., Svasta, P., Codreanu, N., Safta, M..  2017.  Active Tamper Detection Circuit Based on the Analysis of Pulse Response in Conductive Mesh. 2017 40th International Spring Seminar on Electronics Technology (ISSE). :1–6.

Tamper detection circuits provide the first and most important defensive wall in protecting electronic modules containing security data. A widely used procedure is to cover the entire module with a foil containing fine conductive mesh, which detects intrusion attempts. Detection circuits are further classified as passive or active. Passive circuits have the advantage of low power consumption, however they are unable to detect small variations in the conductive mesh parameters. Since modern tools provide an upper leverage over the passive method, the most efficient way to protect security modules is thus to use active circuits. The active tamper detection circuits are typically probing the conductive mesh with short pulses, analyzing its response in terms of delay and shape. The method proposed in this paper generates short pulses at one end of the mesh and analyzes the response at the other end. Apart from measuring pulse delay, the analysis includes a frequency domain characterization of the system, determining whether there has been an intrusion or not, by comparing it to a reference (un-tampered with) spectrum. The novelty of this design is the combined analysis, in time and frequency domains, of the small variations in mesh characteristic parameters.

Krishnan, Sanjay, Franklin, Michael J., Goldberg, Ken, Wang, Jiannan, Wu, Eugene.  2016.  ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning. Proceedings of the 2016 International Conference on Management of Data. :2117–2120.

Databases can be corrupted with various errors such as missing, incorrect, or inconsistent values. Increasingly, modern data analysis pipelines involve Machine Learning, and the effects of dirty data can be difficult to debug.Dirty data is often sparse, and naive sampling solutions are not suited for high-dimensional models. We propose ActiveClean, a progressive framework for training Machine Learning models with data cleaning. Our framework updates a model iteratively as the analyst cleans small batches of data, and includes numerous optimizations such as importance weighting and dirty data detection. We designed a visual interface to wrap around this framework and demonstrate ActiveClean for a video classification problem and a topic modeling problem.

Gul Agha, University of Illinois at Urbana-Champaign.  2014.  Actors Programming for the Mobile Cloud. IEEE 13th International Symposium on Parallel and Distributed Computing,.

Abstract—Actor programming languages provide the kind of inherent parallelism that is needed for building applications in the mobile cloud. This is because the Actor model provides encapsulation (isolation of local state), fair scheduling, location transparency, and locality of reference. These properties facilitate building secure, scalable concurrent systems. Not surprisingly, very large-scale applications such as Facebook chat service and Twitter have been written in actor languages. The paper introduces the basics of the actor model and gives a high-level overview of the problem of coordination in actor systems. It then describes several novel methods for reasoning about concurrent systems that are both effective and scalable.

Livshitz, Ilva I., Lontsikh, Pawel A., Lontsiklr, Natalia P., Karascv, Sergey, Golovina, Elena.  2019.  The Actual Problems of IT-Security Process Assurance. 2019 International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT QM IS). :140–144.

The article deals with the aspects of IT-security of business processes, using a variety of methodological tools, including Integrated Management Systems. Currently, all IMS consist of at least 2 management systems, including the IT-Security Management System. Typically, these IMS cover biggest part of the company business processes, but in practice, there are examples of different scales, even within a single facility. However, it should be recognized that the total number of such projects both in the Russian Federation and in the World is small. The security of business processes will be considered on the example of the incident of Norsk Hydro. In the article the main conclusions are given to confirm the possibility of security, continuity and recovery of critical business processes on the example of this incident.

Han, K., Li, S., Wang, Z., Yang, X..  2018.  Actuator deception attack detection and estimation for a class of nonlinear systems. 2018 37th Chinese Control Conference (CCC). :5675–5680.
In this paper, an novel active safety monitoring system is constructed for a class of nonlinear discrete-time systems. The considered nonlinear system is subjected to unknown inputs, external disturbances, and possible unknown deception attacks, simultaneously. In order to secure the safety of control systems, an active attack estimator composed of state/output estimator, attack detector and attack/attacker action estimator is constructed to monitor the system running status. The analysis and synthesis of attack estimator is performed in the H∞performance optimization manner. The off-line calculation and on-line application of active attack estimator are summarized simultaneously. The effectiveness of the proposed results is finally verified by an numerical example.
Gracy, S., Milošević, J., Sandberg, H..  2020.  Actuator Security Index for Structured Systems. 2020 American Control Conference (ACC). :2993–2998.
Given a network with a set of vulnerable actuators (and sensors), the security index of an actuator equals the minimum number of sensors and actuators that needs to be compromised so as to conduct a perfectly undetectable attack using the said actuator. This paper deals with the problem of computing actuator security indices for discrete-time LTI network systems, using a structured systems framework. We show that the actuator security index is generic, that is for almost all realizations the actuator security index remains the same. We refer to such an index as generic security index (generic index) of an actuator. Given that the security index quantifies the vulnerability of a network, the generic index is quite valuable for large scale energy systems. Our second contribution is to provide graph-theoretic conditions for computing the generic index. The said conditions are in terms of existence of linkings on appropriately-defined directed (sub)graphs. Based on these conditions, we present an algorithm for computing the generic index.
C. H. Hsieh, C. M. Lai, C. H. Mao, T. C. Kao, K. C. Lee.  2015.  "AD2: Anomaly detection on active directory log data for insider threat monitoring". 2015 International Carnahan Conference on Security Technology (ICCST). :287-292.

What you see is not definitely believable is not a rare case in the cyber security monitoring. However, due to various tricks of camouflages, such as packing or virutal private network (VPN), detecting "advanced persistent threat"(APT) by only signature based malware detection system becomes more and more intractable. On the other hand, by carefully modeling users' subsequent behaviors of daily routines, probability for one account to generate certain operations can be estimated and used in anomaly detection. To the best of our knowledge so far, a novel behavioral analytic framework, which is dedicated to analyze Active Directory domain service logs and to monitor potential inside threat, is now first proposed in this project. Experiments on real dataset not only show that the proposed idea indeed explores a new feasible direction for cyber security monitoring, but also gives a guideline on how to deploy this framework to various environments.

John C. Mace, Newcastle University, Nipun Thekkummal, Newcastle University, Charles Morisset, Newcastle University, Aad Van Moorsel, Newcastle University.  2017.  ADaCS: A Tool for Analysing Data Collection Strategies. European Workshop on Performance Engineering (EPEW 2017).

Given a model with multiple input parameters, and multiple possible sources for collecting data for those parameters, a data collection strategy is a way of deciding from which sources to sample data, in order to reduce the variance on the output of the model. Cain and Van Moorsel have previously formulated the problem of optimal data collection strategy, when each arameter can be associated with a prior normal distribution, and when sampling is associated with a cost. In this paper, we present ADaCS, a new tool built as an extension of PRISM, which automatically analyses all possible data collection strategies for a model, and selects the optimal one. We illustrate ADaCS on attack trees, which are a structured approach to analyse the impact and the likelihood of success of attacks and defenses on computer and socio-technical systems. Furthermore, we introduce a new strategy exploration heuristic that significantly improves on a brute force approach.

Villarreal-Vasquez, M., Bhargava, B., Angin, P..  2017.  Adaptable Safety and Security in V2X Systems. 2017 IEEE International Congress on Internet of Things (ICIOT). :17–24.

With the advances in the areas of mobile computing and wireless communications, V2X systems have become a promising technology enabling deployment of applications providing road safety, traffic efficiency and infotainment. Due to their increasing popularity, V2X networks have become a major target for attackers, making them vulnerable to security threats and network conditions, and thus affecting the safety of passengers, vehicles and roads. Existing research in V2X does not effectively address the safety, security and performance limitation threats to connected vehicles, as a result of considering these aspects separately instead of jointly. In this work, we focus on the analysis of the tradeoffs between safety, security and performance of V2X systems and propose a dynamic adaptability approach considering all three aspects jointly based on application needs and context to achieve maximum safety on the roads using an Internet of vehicles. Experiments with a simple V2V highway scenario demonstrate that an adaptive safety/security approach is essential and V2X systems have great potential for providing low reaction times.

Hui Lin, University of Illinois at Urbana-Champaign, Adam Slagell, University of Illinois at Urbana-Champaign, Catello Di Marino, University of Illinois at Urbana-Champaugn, Zbigniew Kalbarczyk, University of Illinois at Urbana-Champaign, Ravishankar K. Iyer, University of Illinois at Urbana-Champaign.  2013.  Adapting Bro into SCADA: Building a Specification-based Instrusion Detection System for the DNP3 Protocol. Eighth Annual Security and Information Intelligence Research Workshop (CSIRRW 2013).

When SCADA systems are exposed to public networks, attackers can more easily penetrate the control systems that operate electrical power grids, water plants, and other critical infrastructures. To detect such attacks, SCADA systems require an intrusion detection technique that can understand the information carried by their usually proprietary network protocols.

To achieve that goal, we propose to attach to SCADA systems a specification-based intrusion detection framework based on Bro [7][8], a runtime network traffic analyzer. We have built a parser in Bro to support DNP3, a network protocol widely used in SCADA systems that operate electrical power grids. This built-in parser provides a clear view of all network events related to SCADA systems. Consequently, security policies to analyze SCADA-specific semantics related to the network events can be accurately defined. As a proof of concept, we specify a protocol validation policy to verify that the semantics of the data extracted from network packets conform to protocol definitions. We performed an experimental evaluation to study the processing capabilities of the proposed intrusion detection framework.

Alom, Md. Zulfikar, Carminati, Barbara, Ferrari, Elena.  2019.  Adapting Users' Privacy Preferences in Smart Environments. 2019 IEEE International Congress on Internet of Things (ICIOT). :165—172.
A smart environment is a physical space where devices are connected to provide continuous support to individuals and make their life more comfortable. For this purpose, a smart environment collects, stores, and processes a massive amount of personal data. In general, service providers collect these data according to their privacy policies. To enhance the privacy control, individuals can explicitly express their privacy preferences, stating conditions on how their data have to be used and managed. Typically, privacy checking is handled through the hard matching of users' privacy preferences against service providers' privacy policies, by denying all service requests whose privacy policies do not fully match with individual's privacy preferences. However, this hard matching might be too restrictive in a smart environment because it denies the services that partially satisfy the individual's privacy preferences. To cope with this challenge, in this paper, we propose a soft privacy matching mechanism, able to relax, in a controlled way, some conditions of users' privacy preferences such to match with service providers' privacy policies. At this aim, we exploit machine learning algorithms to build a classifier, which is able to make decisions on future service requests, by learning which privacy preference components a user is prone to relax, as well as the relaxation tolerance. We test our approach on two realistic datasets, obtaining promising results.
Qiao, B., Jin, L., Yang, Y..  2016.  An Adaptive Algorithm for Grey Image Edge Detection Based on Grey Correlation Analysis. 2016 12th International Conference on Computational Intelligence and Security (CIS). :470—474.

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

Yagoub, Mohammed Amine, Laouid, Abdelkader, Kazar, Okba, Bounceur, Ahcène, Euler, Reinhardt, AlShaikh, Muath.  2018.  An Adaptive and Efficient Fully Homomorphic Encryption Technique. Proceedings of the 2Nd International Conference on Future Networks and Distributed Systems. :35:1–35:6.

The huge amount of generated data offers special advantages mainly in dynamic and scalable systems. In fact, the data generator entities need to share the generated data with each other which leads to the use of cloud services. A cloud server is considered as an untrusted entity that offers many advantages such as large storing space, computation speed... etc. Hence, there is a need to cope with how to protect the stored data in the cloud server by proposing adaptive solutions. The main objective is how to provide an encryption scheme allowing the user to maintains some functions such as addition, multiplication and to preserve the order on the encrypted cloud data. Many algorithms and techniques are designed to manipulate the stored encrypted cloud data. This paper presents an adaptive and efficient fully homomorphic encryption technique to protect the user's data stored in the cloud, where the cloud server executes simple operations.

Renners, Leonard, Heine, Felix, Kleiner, Carsten, Rodosek, Gabi Dreo.  2019.  Adaptive and Intelligible Prioritization for Network Security Incidents. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.
Incident prioritization is nowadays a part of many approaches and tools for network security and risk management. However, the dynamic nature of the problem domain is often unaccounted for. That is, the prioritization is typically based on a set of static calculations, which are rarely adjusted. As a result, incidents are incorrectly prioritized, leading to an increased and misplaced effort in the incident response. A higher degree of automation could help to address this problem. In this paper, we explicitly consider flaws in the prioritization an unalterable circumstance. We propose an adaptive incident prioritization, which allows to automate certain tasks for the prioritization model management in order to continuously assess and improve a prioritization model. At the same time, we acknowledge the human analyst as the focal point and propose to keep the human in the loop, among others by treating understandability as a crucial requirement.
Abdeslam, W. Oulad, Tabii, Y., El Kadiri, K. E..  2017.  Adaptive Appearance Model in Particle Filter Based Visual Tracking. Proceedings of the 2Nd International Conference on Big Data, Cloud and Applications. :85:1–85:5.

Visual Tracking methods based on particle filter framework uses frequently the state space information of the target object to calculate the observation model, However this often gives a poor estimate if unexpected motions happen, or under conditions of cluttered backgrounds illumination changes, because the model explores the state space without any additional information of current state. In order to avoid the tracking failure, we address in this paper, Particle filter based visual tracking, in which the target appearance model is represented through an adaptive conjunction of color histogram, and space based appearance combining with velocity parameters, then the appearance models is estimated using particles whose weights, are incrementally updated for dynamic adaptation of the cue parametrization.

Ye, Hui, Ma, Xiaopeng, Pan, Qingfeng, Fang, Huaqiang, Xiang, Hang, Shao, Tongzhen.  2019.  An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series. Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. :1–7.
The anomaly detection of time series is a hotspot of time series data mining. The own characteristics of different anomaly detectors determine the abnormal data that they are good at. There is no detector can be optimizing in all types of anomalies. Moreover, it still has difficulties in industrial production due to problems such as a single detector can't be optimized at different time windows of the same time series. This paper proposes an adaptive model based on time series characteristics and selecting appropriate detector and run-time parameters for anomaly detection, which is called ATSDLN(Adaptive Time Series Detector Learning Network). We take the time series as the input of the model, and learn the time series representation through FCN. In order to realize the adaptive selection of detectors and run-time parameters according to the input time series, the outputs of FCN are the inputs of two sub-networks: the detector selection network and the run-time parameters selection network. In addition, the way that the variable layer width design of the parameter selection sub-network and the introduction of transfer learning make the model be with more expandability. Through experiments, it is found that ATSDLN can select appropriate anomaly detector and run-time parameters, and have strong expandability, which can quickly transfer. We investigate the performance of ATSDLN in public data sets, our methods outperform other methods in most cases with higher effect and better adaptation. We also show experimental results on public data sets to demonstrate how model structure and transfer learning affect the effectiveness.
Sharma, V., Renu, Shree, T..  2020.  An adaptive approach for Detecting Blackhole using TCP Analysis in MANETs. 2nd International Conference on Data, Engineering and Applications (IDEA). :1—5.

From recent few years, need of information security is realized by society amd researchers specially in multi-path, unstructured networks as Mobile Ad-hoc Network. Devices connected in such network are self-configuring and small in size and can communicate in infra less environment. Architecture is very much dynamic and absence of central controlling authority puts challenges to the network by making more vulnerable for various threats and attacks in order to exploit the function of the network. The paper proposes, TCP analysis against very popular attack i.e. blackhole attack. Under different circumstance, reliable transport layer protocol TCP is analyzed for the effects of the attack on adhoc network. Performance has been measured using metrics of average throughput, normalized routing load and end to end delay and conclusions have been drawn based on that.

Akhtar, U., Lee, S..  2018.  Adaptive Cache Replacement in Efficiently Querying Semantic Big Data. 2018 IEEE International Conference on Web Services (ICWS). :367–370.
This paper addresses the problem of querying Knowledge bases (KBs) that store semantic big data. For efficiently querying data the most important factor is cache replacement policy, which determines the overall query response. As cache is limited in size, less frequently accessed data should be removed to provide more space to hot triples (frequently accessed). So, to achieve a similar performance to RDBMS, we proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Moreover, performance bottleneck of triplestore, makes realworld application difficult. To achieve a closer performance similar to RDBMS, we have proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Our proposed algorithm effectively replaces cache with high accuracy. To implement cache replacement policy, we have applied exponential smoothing, a forecast method, to collect most frequently accessed triples. The evaluation result shows that the proposed scheme outperforms the existing cache replacement policies, such as LRU (least recently used) and LFU (least frequently used), in terms of higher hit rates and less time overhead.
Nguyen, Q. N., Lopez, J., Tsuda, T., Sato, T., Nguyen, K., Ariffuzzaman, M., Safitri, C., Thanh, N. H..  2020.  Adaptive Caching for Beneficial Content Distribution in Information-Centric Networking. 2020 International Conference on Information Networking (ICOIN). :535–540.
Currently, little attention has been carried out to address the feasibility of in-network caching in Information-Centric Networking (ICN) for the design and real-world deployment of future networks. Towards this line, in this paper, we propose a beneficial caching scheme in ICN by storing no more than a specific number of replicas for each content. Particularly, to realize an optimal content distribution for deploying caches in ICN, a content can be cached either partially or as a full-object corresponding to its request arrival rate and data traffic. Also, we employ a utility-based replacement in each content node to keep the most recent and popular content items in the ICN interconnections. The evaluation results show that the proposal improves the cache hit rate and cache diversity considerably, and acts as a beneficial caching approach for network and service providers in ICN. Specifically, the proposed caching mechanism is easy to deploy, robust, and relevant for the content-based providers by enabling them to offer users high Quality of Service (QoS) and gain benefits at the same time.