Visible to the public Biblio

Found 123 results

Filters: Keyword is Data models  [Clear All Filters]
2019-10-22
Alzahrani, Ahmed, Johnson, Chris, Altamimi, Saad.  2018.  Information security policy compliance: Investigating the role of intrinsic motivation towards policy compliance in the organisation. 2018 4th International Conference on Information Management (ICIM). :125–132.
Recent behavioral research in information security has focused on increasing employees' motivation to enhance the security performance in an organization. This empirical study investigated employees' information security policy (ISP) compliance intentions using self-determination theory (SDT). Relevant hypotheses were developed to test the proposed research model. Data obtained via a survey (N=3D407) from a Fortune 600 organization in Saudi Arabia provides empirical support for the model. The results confirmed that autonomy, competence and the concept of relatedness all positively affect employees' intentions to comply. The variable 'perceived value congruence' had a negative effect on ISP compliance intentions, and the perceived legitimacy construct did not affect employees' intentions. In general, the findings of this study suggest that SDT has value in research into employees' ISP compliance intentions.
2019-10-15
Pan, Y., He, F., Yu, H..  2018.  An Adaptive Method to Learn Directive Trust Strength for Trust-Aware Recommender Systems. 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD)). :10–16.

Trust Relationships have shown great potential to improve recommendation quality, especially for cold start and sparse users. Since each user trust their friends in different degrees, there are numbers of works been proposed to take Trust Strength into account for recommender systems. However, these methods ignore the information of trust directions between users. In this paper, we propose a novel method to adaptively learn directive trust strength to improve trust-aware recommender systems. Advancing previous works, we propose to establish direction of trust strength by modeling the implicit relationships between users with roles of trusters and trustees. Specially, under new trust strength with directions, how to compute the directive trust strength is becoming a new challenge. Therefore, we present a novel method to adaptively learn directive trust strengths in a unified framework by enforcing the trust strength into range of [0, 1] through a mapping function. Our experiments on Epinions and Ciao datasets demonstrate that the proposed algorithm can effectively outperform several state-of-art algorithms on both MAE and RMSE metrics.

2019-09-26
Torkura, K. A., Sukmana, M. I. H., Meinig, M., Cheng, F., Meinel, C., Graupner, H..  2018.  A Threat Modeling Approach for Cloud Storage Brokerage and File Sharing Systems. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1-5.
Cloud storage brokerage systems abstract cloud storage complexities by mediating technical and business relationships between cloud stakeholders, while providing value-added services. This however raises security challenges pertaining to the integration of disparate components with sometimes conflicting security policies and architectural complexities. Assessing the security risks of these challenges is therefore important for Cloud Storage Brokers (CSBs). In this paper, we present a threat modeling schema to analyze and identify threats and risks in cloud brokerage brokerage systems. Our threat modeling schema works by generating attack trees, attack graphs, and data flow diagrams that represent the interconnections between identified security risks. Our proof-of-concept implementation employs the Common Configuration Scoring System (CCSS) to support the threat modeling schema, since current schemes lack sufficient security metrics which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two attacks commonly launched against cloud storage systems: Cloud sStorage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then combined with CVSS based metrics to assign probabilities in an Attack Tree. Thus, we show the possibility combining CVSS and CCSS for comprehensive threat modeling, and also show that our schemas can be used to improve cloud security.
2019-09-09
Almohaimeed, A., Asaduzzaman, A..  2019.  A Novel Moving Target Defense Technique to Secure Communication Links in Software-Defined Networks. 2019 Fifth Conference on Mobile and Secure Services (MobiSecServ). :1–4.
Software-defined networking (SDN) is a recently developed approach to computer networking that brings a centralized orientation to network control, thereby improving network architecture and management. However, as with any communication environment that involves message transmission among users, SDN is confronted by the ongoing challenge of protecting user privacy. In this “Work in Progress (WIP)” research, we propose an SDN security model that applies the moving target defense (MTD) technique to protect communication links from sensitive data leakages. MTD is a security solution aimed at increasing complexity and uncertainty for attackers by concealing sensitive information that may serve as a gateway from which to launch different types of attacks. The proposed MTD-based security model is intended to protect user identities contained in transmitted messages in a way that prevents network intruders from identifying the real identities of senders and receivers. According to the results from preliminary experiments, the proposed MTD model has potential to protect the identities contained in transmitted messages within communication links. This work will be extended to protect sensitive data if an attacker gets access to the network device.
2019-09-05
Sun, Y., Zhang, L., Zhao, C..  2018.  A Study of Network Covert Channel Detection Based on Deep Learning. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :637-641.
Information security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.
2019-08-05
Ahmad, F., Adnane, A., KURUGOLLU, F., Hussain, R..  2019.  A Comparative Analysis of Trust Models for Safety Applications in IoT-Enabled Vehicular Networks. 2019 Wireless Days (WD). :1-8.
Vehicular Ad-hoc NETwork (VANET) is a vital transportation technology that facilitates the vehicles to share sensitive information (such as steep-curve warnings and black ice on the road) with each other and with the surrounding infrastructure in real-time to avoid accidents and enable comfortable driving experience.To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.
2019-07-01
Amjad, N., Afzal, H., Amjad, M. F., Khan, F. A..  2018.  A Multi-Classifier Framework for Open Source Malware Forensics. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :106-111.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

2019-06-17
Yang, J., Jeong, J. P..  2018.  An Automata-based Security Policy Translation for Network Security Functions. 2018 International Conference on Information and Communication Technology Convergence (ICTC). :268–272.

This paper proposes the design of a security policy translator in Interface to Network Security Functions (I2NSF) framework. Also, this paper shows the benefits of designing security policy translations. I2NSF is an architecture for providing various Network Security Functions (NSFs) to users. I2NSF user should be able to use NSF even if user has no overall knowledge of NSFs. Generally, policies which are generated by I2NSF user contain abstract data because users do not consider the attributes of NSFs when creating policies. Therefore, the I2NSF framework requires a translator that automatically finds the NSFs which is required for policy when Security Controller receives a security policy from the user and translates it for selected NSFs. We satisfied the above requirements by modularizing the translator through Automata theory.

2019-06-10
Nathezhtha, T., Yaidehi, V..  2018.  Cloud Insider Attack Detection Using Machine Learning. 2018 International Conference on Recent Trends in Advance Computing (ICRTAC). :60-65.

Security has always been a major issue in cloud. Data sources are the most valuable and vulnerable information which is aimed by attackers to steal. If data is lost, then the privacy and security of every cloud user are compromised. Even though a cloud network is secured externally, the threat of an internal attacker exists. Internal attackers compromise a vulnerable user node and get access to a system. They are connected to the cloud network internally and launch attacks pretending to be trusted users. Machine learning approaches are widely used for cloud security issues. The existing machine learning based security approaches classify a node as a misbehaving node based on short-term behavioral data. These systems do not differentiate whether a misbehaving node is a malicious node or a broken node. To address this problem, this paper proposes an Improvised Long Short-Term Memory (ILSTM) model which learns the behavior of a user and automatically trains itself and stores the behavioral data. The model can easily classify the user behavior as normal or abnormal. The proposed ILSTM not only identifies an anomaly node but also finds whether a misbehaving node is a broken node or a new user node or a compromised node using the calculated trust factor. The proposed model not only detects the attack accurately but also reduces the false alarm in the cloud network.

Vaseer, G., Ghai, G., Ghai, D..  2018.  Distributed Trust-Based Multiple Attack Prevention for Secure MANETs. 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). :108–113.

Mobile ad hoc networks (MANETs) are self-configuring, dynamic networks in which nodes are free to move. These nodes are susceptible to various malicious attacks. In this paper, we propose a distributed trust-based security scheme to prevent multiple attacks such as Probe, Denial-of-Service (DoS), Vampire, User-to-Root (U2R) occurring simultaneously. We report above 95% accuracy in data transmission and reception by applying the proposed scheme. The simulation has been carried out using network simulator ns-2 in a AODV routing protocol environment. To the best of the authors' knowledge, this is the first work reporting a distributed trust-based prevention scheme for preventing multiple attacks. We also check the scalability of the technique using variable node densities in the network.

Basomingera, R., Choi, Y..  2019.  Route Cache Based SVM Classifier for Intrusion Detection of Control Packet Attacks in Mobile Ad-Hoc Networks. 2019 International Conference on Information Networking (ICOIN). :31–36.

For the security of mobile ad-hoc networks (MANETs), a group of wireless mobile nodes needs to cooperate by forwarding packets, to implement an intrusion detection system (IDS). Some of the current IDS implementations in a clustered MANET have designed mobile nodes to wait until the cluster head is elected before scanning the network and thus nodes may be, unfortunately, exposed to several control packet attacks by which nodes identify falsified routes to reach other nodes. In order to detect control packet attacks such as route falsification, we design a route cache sharing mechanism for a non-clustered network where all one-hop routing data are collected by each node for a cooperative host-based detection. The cooperative host-based detection system uses a Support Vector Machine classifier and achieves a detection rate of around 95%. By successfully detecting the route falsification attacks, nodes are given the capability to avoid other attacks such as black-hole and gray-hole, which are in many cases a result of a successful route falsification attack.

2019-05-20
Frolov, A. B., Vinnikov, A. M..  2018.  Modeling Cryptographic Protocols Using the Algebraic Processor. 2018 IV International Conference on Information Technologies in Engineering Education (Inforino). :1–5.

We present the IT solution for remote modeling of cryptographic protocols and other cryptographic primitives and a number of education-oriented capabilities based on them. These capabilities are provided at the Department of Mathematical Modeling using the MPEI algebraic processor, and allow remote participants to create automata models of cryptographic protocols, use and manage them in the modeling process. Particular attention is paid to the IT solution for modeling of the private communication and key distribution using the processor combined with the Kerberos protocol. This allows simulation and studying of key distribution protocols functionality on remote computers via the Internet. The importance of studying cryptographic primitives for future IT specialists is emphasized.

Caminha, J., Perkusich, A., Perkusich, M..  2018.  A smart middleware to detect on-off trust attacks in the Internet of Things. 2018 IEEE International Conference on Consumer Electronics (ICCE). :1–2.

Security is a key concern in Internet of Things (IoT) designs. In a heterogeneous and complex environment, service providers and service requesters must trust each other. On-off attack is a sophisticated trust threat in which a malicious device can perform good and bad services randomly to avoid being rated as a low trust node. Some countermeasures demands prior level of trust knowing and time to classify a node behavior. In this paper, we introduce a Smart Middleware that automatically assesses the IoT resources trust, evaluating service providers attributes to protect against On-off attacks.

2019-05-01
Li, P., Liu, Q., Zhao, W., Wang, D., Wang, S..  2018.  Chronic Poisoning against Machine Learning Based IDSs Using Edge Pattern Detection. 2018 IEEE International Conference on Communications (ICC). :1-7.

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). Poisoning attack, which is one of the most recognized security threats towards machine learning- based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.

2019-04-05
Dong, X., Hu, J., Cui, Y..  2018.  Overview of Botnet Detection Based on Machine Learning. 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :476-479.

With the rapid development of the information industry, the applications of Internet of things, cloud computing and artificial intelligence have greatly affected people's life, and the network equipment has increased with a blowout type. At the same time, more complex network environment has also led to a more serious network security problem. The traditional security solution becomes inefficient in the new situation. Therefore, it is an important task for the security industry to seek technical progress and improve the protection detection and protection ability of the security industry. Botnets have been one of the most important issues in many network security problems, especially in the last one or two years, and China has become one of the most endangered countries by botnets, thus the huge impact of botnets in the world has caused its detection problems to reset people's attention. This paper, based on the topic of botnet detection, focuses on the latest research achievements of botnet detection based on machine learning technology. Firstly, it expounds the application process of machine learning technology in the research of network space security, introduces the structure characteristics of botnet, and then introduces the machine learning in botnet detection. The security features of these solutions and the commonly used machine learning algorithms are emphatically analyzed and summarized. Finally, it summarizes the existing problems in the existing solutions, and the future development direction and challenges of machine learning technology in the research of network space security.

2019-03-06
Li, W., Li, S., Zhang, X., Pan, Q..  2018.  Optimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network. 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). :60-63.

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.

Man, Y., Ding, L., Xiaoguo, Z..  2018.  Nonlinear System Identification Method Based on Improved Deep Belief Network. 2018 Chinese Automation Congress (CAC). :2379-2383.

Accurate model is very important for the control of nonlinear system. The traditional identification method based on shallow BP network is easy to fall into local optimal solution. In this paper, a modeling method for nonlinear system based on improved Deep Belief Network (DBN) is proposed. Continuous Restricted Boltzmann Machine (CRBM) is used as the first layer of the DBN, so that the network can more effectively deal with the actual data collected from the real systems. Then, the unsupervised training and supervised tuning were combine to improve the accuracy of identification. The simulation results show that the proposed method has a higher identification accuracy. Finally, this improved algorithm is applied to identification of diameter model of silicon single crystal and the simulation results prove its excellent ability of parameters identification.

Cuzzocrea, A., Damiani, E..  2018.  Pedigree-Ing Your Big Data: Data-Driven Big Data Privacy in Distributed Environments. 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). :675-681.
This paper introduces a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings. The proposed framework can be viewed as an alternative to classical approaches where the privacy of big data is ensured via security-inspired protocols that check several (protocol) layers in order to achieve the desired privacy. Unfortunately, this injects considerable computational overheads in the overall process, thus introducing relevant challenges to be considered. Our approach instead tries to recognize the "pedigree" of suitable summary data representatives computed on top of the target big data repositories, hence avoiding computational overheads due to protocol checking. We also provide a relevant realization of the framework above, the so-called Data-dRIven aggregate-PROvenance privacypreserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest.
2019-02-13
Prakash, A., Priyadarshini, R..  2018.  An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :585–589.

Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are Naive Bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.

2019-01-21
Isakov, M., Bu, L., Cheng, H., Kinsy, M. A..  2018.  Preventing Neural Network Model Exfiltration in Machine Learning Hardware Accelerators. 2018 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :62–67.

Machine learning (ML) models are often trained using private datasets that are very expensive to collect, or highly sensitive, using large amounts of computing power. The models are commonly exposed either through online APIs, or used in hardware devices deployed in the field or given to the end users. This provides an incentive for adversaries to steal these ML models as a proxy for gathering datasets. While API-based model exfiltration has been studied before, the theft and protection of machine learning models on hardware devices have not been explored as of now. In this work, we examine this important aspect of the design and deployment of ML models. We illustrate how an attacker may acquire either the model or the model architecture through memory probing, side-channels, or crafted input attacks, and propose (1) power-efficient obfuscation as an alternative to encryption, and (2) timing side-channel countermeasures.

Kos, J., Fischer, I., Song, D..  2018.  Adversarial Examples for Generative Models. 2018 IEEE Security and Privacy Workshops (SPW). :36–42.
We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.
Venkatesan, S., Sugrim, S., Izmailov, R., Chiang, C. J., Chadha, R., Doshi, B., Hoffman, B., Newcomb, E. Allison, Buchler, N..  2018.  On Detecting Manifestation of Adversary Characteristics. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :431–437.

Adversaries are conducting attack campaigns with increasing levels of sophistication. Additionally, with the prevalence of out-of-the-box toolkits that simplify attack operations during different stages of an attack campaign, multiple new adversaries and attack groups have appeared over the past decade. Characterizing the behavior and the modus operandi of different adversaries is critical in identifying the appropriate security maneuver to detect and mitigate the impact of an ongoing attack. To this end, in this paper, we study two characteristics of an adversary: Risk-averseness and Experience level. Risk-averse adversaries are more cautious during their campaign while fledgling adversaries do not wait to develop adequate expertise and knowledge before launching attack campaigns. One manifestation of these characteristics is through the adversary's choice and usage of attack tools. To detect these characteristics, we present multi-level machine learning (ML) models that use network data generated while under attack by different attack tools and usage patterns. In particular, for risk-averseness, we considered different configurations for scanning tools and trained the models in a testbed environment. The resulting model was used to predict the cautiousness of different red teams that participated in the Cyber Shield ‘16 exercise. The predictions matched the expected behavior of the red teams. For Experience level, we considered publicly-available remote access tools and usage patterns. We developed a Markov model to simulate usage patterns of attackers with different levels of expertise and through experiments on CyberVAN, we showed that the ML model has a high accuracy.

Fahrbach, M., Miller, G. L., Peng, R., Sawlani, S., Wang, J., Xu, S. C..  2018.  Graph Sketching against Adaptive Adversaries Applied to the Minimum Degree Algorithm. 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS). :101–112.
Motivated by the study of matrix elimination orderings in combinatorial scientific computing, we utilize graph sketching and local sampling to give a data structure that provides access to approximate fill degrees of a matrix undergoing elimination in polylogarithmic time per elimination and query. We then study the problem of using this data structure in the minimum degree algorithm, which is a widely-used heuristic for producing elimination orderings for sparse matrices by repeatedly eliminating the vertex with (approximate) minimum fill degree. This leads to a nearly-linear time algorithm for generating approximate greedy minimum degree orderings. Despite extensive studies of algorithms for elimination orderings in combinatorial scientific computing, our result is the first rigorous incorporation of randomized tools in this setting, as well as the first nearly-linear time algorithm for producing elimination orderings with provable approximation guarantees. While our sketching data structure readily works in the oblivious adversary model, by repeatedly querying and greedily updating itself, it enters the adaptive adversarial model where the underlying sketches become prone to failure due to dependency issues with their internal randomness. We show how to use an additional sampling procedure to circumvent this problem and to create an independent access sequence. Our technique for decorrelating interleaved queries and updates to this randomized data structure may be of independent interest.
2018-12-10
Versluis, L., Neacsu, M., Iosup, A..  2018.  A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters. 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). :223–232.
To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated autoscaling policies, that is, policies that dynamically provision resources for the customer. Although selecting and tuning autoscaling policies is a challenging task for datacenter operators, so far relatively few studies investigate the performance of autoscaling for workloads of workflows. Complementing previous knowledge, in this work we propose the first comprehensive performance study in the field. Using trace-based simulation, we compare state-of-the-art autoscaling policies across multiple application domains, workload arrival patterns (e.g., burstiness), and system utilization levels. We further investigate the interplay between autoscaling and regular allocation policies, and the complexity cost of autoscaling. Our quantitative study focuses not only on traditional performance metrics and on state-of-the-art elasticity metrics, but also on time-and memory-related autoscaling-complexity metrics. Our main results give strong and quantitative evidence about previously unreported operational behavior, for example, that autoscaling policies perform differently across application domains and allocation and provisioning policies should be co-designed.
2018-11-19
Serey, J., Ternero, R., Soto, I., Quezada, L..  2017.  A Competency Model to Help Selecting the Information Security Method for Platforms of Communication by Visible Light (VLC). 2017 First South American Colloquium on Visible Light Communications (SACVLC). :1–6.
It is challenging in Security information and Platforms of Communication by Visible Light (VLC), solutions are made to manage the right Security problems. Several solutions have been developed and evolved constantly to meet complex and ever-changing business needs in the world. In the business context, people who are responsible for a project or an organization undergo professional and emotional stress. This research project has developed a new model which can help decision makers evaluating these alternative methods in relation to articulating different types of Security problems, formulating Security criteria, and simulating expectations of adopting the chosen method for Platforms of Communication by Visible Light (VLC).