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Journal Article
Cao, L., Kang, Y., Wu, Q., Wu, R., Guo, X., Feng, T..  2020.  Searchable encryption cloud storage with dynamic data update to support efficient policy hiding. China Communications. 17:153–163.
Ciphertext policy attribute based encryption (CP-ABE) can provide high finegrained access control for cloud storage. However, it needs to solve problems such as property privacy protection, ciphertext search and data update in the application process. Therefore, based on CP-ABE scheme, this paper proposes a dynamically updatable searchable encryption cloud storage (DUSECS) scheme. Using the characteristics of homomorphic encryption, the encrypted data is compared to achieve efficient hiding policy. Meanwhile, adopting linked list structure, the DUSECS scheme realizes the dynamic data update and integrity detection, and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm. The analysis of security and performance shows that the scheme is secure and efficient.
Kloft, Marius, Laskov, Pavel.  2012.  Security Analysis of Online Centroid Anomaly Detection. J. Mach. Learn. Res.. 13:3681–3724.

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). In such cases, learning algorithms may have to cope with manipulated data aimed at hampering decision making. Although some previous work addressed the issue of handling malicious data in the context of supervised learning, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method–online centroid anomaly detection–in the presence of adversarial noise. Our analysis addresses the following security-related issues: formalization of learning and attack processes, derivation of an optimal attack, and analysis of attack efficiency and limitations. We derive bounds on the effectiveness of a poisoning attack against centroid anomaly detection under different conditions: attacker's full or limited control over the traffic and bounded false positive rate. Our bounds show that whereas a poisoning attack can be effectively staged in the unconstrained case, it can be made arbitrarily difficult (a strict upper bound on the attacker's gain) if external constraints are properly used. Our experimental evaluation, carried out on real traces of HTTP and exploit traffic, confirms the tightness of our theoretical bounds and the practicality of our protection mechanisms.

Ali, Sk Subidh, Ibrahim, Mohamed, Sinanoglu, Ozgur, Chakrabarty, Krishnendu, Karri, Ramesh.  2016.  Security Assessment of Cyberphysical Digital Microfluidic Biochips. IEEE/ACM Trans. Comput. Biol. Bioinformatics. 13:445–458.

A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we provide the first assessment of the security vulnerabilities of DMFBs. We identify result-manipulation attacks on a DMFB that maliciously alter the assay outcomes. Two practical result-manipulation attacks are shown on a DMFB platform performing enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the attacker tampers with the calibration curve of the assay operation. We then identify denial-of-service attacks, where the attacker can disrupt the assay operation by tampering either with the droplet-routing algorithm or with the actuation sequence. We demonstrate these attacks using a digital microfluidic synthesis simulator. The results show that the attacks are easy to implement and hard to detect. Therefore, this work highlights the need for effective protections against malicious modifications in DMFBs.

Kebin Liu, Qiang Ma, Wei Gong, Xin Miao, Yunhao Liu.  2014.  Self-Diagnosis for Detecting System Failures in Large-Scale Wireless Sensor Networks. Wireless Communications, IEEE Transactions on. 13:5535-5545.

Existing approaches to diagnosing sensor networks are generally sink based, which rely on actively pulling state information from sensor nodes so as to conduct centralized analysis. First, sink-based tools incur huge communication overhead to the traffic-sensitive sensor networks. Second, due to the unreliable wireless communications, sink often obtains incomplete and suspicious information, leading to inaccurate judgments. Even worse, it is always more difficult to obtain state information from problematic or critical regions. To address the given issues, we present a novel self-diagnosis approach, which encourages each single sensor to join the fault decision process. We design a series of fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. Fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the diagnosis by transiting the detector's current state to a new state based on local evidences and then passing the detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs a final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100-node indoor testbed and conduct field studies in the GreenOrbs system, which is an operational sensor network with 330 nodes outdoor.
 

Luo, Chao, Fei, Yunsi, Kaeli, David.  2019.  Side-Channel Timing Attack of RSA on a GPU. ACM Transactions on Architecture and Code Optimization (TACO). 16:32:1-32:18.
To increase computation throughput, general purpose Graphics Processing Units (GPUs) have been leveraged to accelerate computationally intensive workloads. GPUs have been used as cryptographic engines, improving encryption/decryption throughput and leveraging the GPU's Single Instruction Multiple Thread (SIMT) model. RSA is a widely used public-key cipher and has been ported onto GPUs for signing and decrypting large files. Although performance has been significantly improved, the security of RSA on GPUs is vulnerable to side-channel timing attacks and is an exposure overlooked in previous studies. GPUs tend to be naturally resilient to side-channel attacks, given that they execute a large number of concurrent threads, performing many RSA operations on different data in parallel. Given the degree of parallel execution on a GPU, there will be a significant amount of noise introduced into the timing channel given the thousands of concurrent threads executing concurrently. In this work, we build a timing model to capture the parallel characteristics of an RSA public-key cipher implemented on a GPU. We consider optimizations that include using Montgomery multiplication and sliding-window exponentiation to implement cryptographic operations. Our timing model considers the challenges of parallel execution, complications that do not occur in single-threaded computing platforms. Based on our timing model, we launch successful timing attacks on RSA running on a GPU, extracting the private key of RSA. We also present an effective error detection and correction mechanism. Our results demonstrate that GPU acceleration of RSA is vulnerable to side-channel timing attacks. We propose several countermeasures to defend against this class of attacks.
Pearce, Hammond, Pinisetty, Srinivas, Roop, Partha S., Kuo, Matthew M. Y., Ukil, Abhisek.  2020.  Smart I/O Modules for Mitigating Cyber-Physical Attacks on Industrial Control Systems. IEEE Transactions on Industrial Informatics. 16:4659—4669.

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.

Keivanloo, Iman, Rilling, Juergen.  2014.  Software Trustworthiness 2.0-A Semantic Web Enabled Global Source Code Analysis Approach. J. Syst. Softw.. 89:33–50.

There has been an ongoing trend toward collaborative software development using open and shared source code published in large software repositories on the Internet. While traditional source code analysis techniques perform well in single project contexts, new types of source code analysis techniques are ermerging, which focus on global source code analysis challenges. In this article, we discuss how the Semantic Web, can become an enabling technology to provide a standardized, formal, and semantic rich representations for modeling and analyzing large global source code corpora. Furthermore, inference services and other services provided by Semantic Web technologies can be used to support a variety of core source code analysis techniques, such as semantic code search, call graph construction, and clone detection. In this paper, we introduce SeCold, the first publicly available online linked data source code dataset for software engineering researchers and practitioners. Along with its dataset, SeCold also provides some Semantic Web enabled core services to support the analysis of Internet-scale source code repositories. We illustrated through several examples how this linked data combined with Semantic Web technologies can be harvested for different source code analysis tasks to support software trustworthiness. For the case studies, we combine both our linked-data set and Semantic Web enabled source code analysis services with knowledge extracted from StackOverflow, a crowdsourcing website. These case studies, we demonstrate that our approach is not only capable of crawling, processing, and scaling to traditional types of structured data (e.g., source code), but also supports emerging non-structured data sources, such as crowdsourced information (e.g., StackOverflow.com) to support a global source code analysis context.

Krupp, B., Sridhar, N., Zhao, W..  2017.  SPE: Security and Privacy Enhancement Framework for Mobile Devices. IEEE Transactions on Dependable and Secure Computing. 14:433–446.

In this paper, we present a security and privacy enhancement (SPE) framework for unmodified mobile operating systems. SPE introduces a new layer between the application and the operating system and does not require a device be jailbroken or utilize a custom operating system. We utilize an existing ontology designed for enforcing security and privacy policies on mobile devices to build a policy that is customizable. Based on this policy, SPE provides enhancements to native controls that currently exist on the platform for privacy and security sensitive components. SPE allows access to these components in a way that allows the framework to ensure the application is truthful in its declared intent and ensure that the user's policy is enforced. In our evaluation we verify the correctness of the framework and the computing impact on the device. Additionally, we discovered security and privacy issues in several open source applications by utilizing the SPE Framework. From our findings, if SPE is adopted by mobile operating systems producers, it would provide consumers and businesses the additional privacy and security controls they demand and allow users to be more aware of security and privacy issues with applications on their devices.

Onufer, J., Ziman, J., Duranka, P., Kravčák, J..  2019.  The Study of Closure Domain Structure Dynamics in Bistable Microwires Using the Technique of Three-Level Field Pulses. IEEE Transactions on Magnetics. 55:1–6.

The process of release of a single domain wall from the closure domain structure at the microwire ends and the process of nucleation of the reversed domain in regions far from the microwire ends were studied using the technique that consists in determining the critical parameters of the rectangular magnetic field pulse (magnitude-Hpc and length-τc) needed for free domain wall production. Since these processes can be influenced by the magnitude of the magnetic field before or after the application of the field pulse (Hi, τ), we propose a modified experiment in which the so-called three-level pulse is used. The three-level pulse starts from the first level, then continues with the second measuring rectangular pulse (Hi, τ), which ends at the third field level. Based on the results obtained in experiments using three-level field pulses, it has been shown that reversed domains are not present in the remanent state in regions far from the microwire ends. Some modification of the theoretical model of a single domain wall trapped in a potential well will be needed for an adequate description of the depinning processes.

Shin, Youngjoo, Koo, Dongyoung, Hur, Junbeom.  2017.  A Survey of Secure Data Deduplication Schemes for Cloud Storage Systems. ACM Comput. Surv.. 49:74:1–74:38.

Data deduplication has attracted many cloud service providers (CSPs) as a way to reduce storage costs. Even though the general deduplication approach has been increasingly accepted, it comes with many security and privacy problems due to the outsourced data delivery models of cloud storage. To deal with specific security and privacy issues, secure deduplication techniques have been proposed for cloud data, leading to a diverse range of solutions and trade-offs. Hence, in this article, we discuss ongoing research on secure deduplication for cloud data in consideration of the attack scenarios exploited most widely in cloud storage. On the basis of classification of deduplication system, we explore security risks and attack scenarios from both inside and outside adversaries. We then describe state-of-the-art secure deduplication techniques for each approach that deal with different security issues under specific or combined threat models, which include both cryptographic and protocol solutions. We discuss and compare each scheme in terms of security and efficiency specific to different security goals. Finally, we identify and discuss unresolved issues and further research challenges for secure deduplication in cloud storage.

Kaur, R., Singh, M..  2014.  A Survey on Zero-Day Polymorphic Worm Detection Techniques. Communications Surveys Tutorials, IEEE. 16:1520-1549.

Zero-day polymorphic worms pose a serious threat to the Internet security. With their ability to rapidly propagate, these worms increasingly threaten the Internet hosts and services. Not only can they exploit unknown vulnerabilities but can also change their own representations on each new infection or can encrypt their payloads using a different key per infection. They have many variations in the signatures of the same worm thus, making their fingerprinting very difficult. Therefore, signature-based defenses and traditional security layers miss these stealthy and persistent threats. This paper provides a detailed survey to outline the research efforts in relation to detection of modern zero-day malware in form of zero-day polymorphic worms.

Kirsch, J., Goose, S., Amir, Y., Dong Wei, Skare, P..  2014.  Survivable SCADA Via Intrusion-Tolerant Replication. Smart Grid, IEEE Transactions on. 5:60-70.

Providers of critical infrastructure services strive to maintain the high availability of their SCADA systems. This paper reports on our experience designing, architecting, and evaluating the first survivable SCADA system-one that is able to ensure correct behavior with minimal performance degradation even during cyber attacks that compromise part of the system. We describe the challenges we faced when integrating modern intrusion-tolerant protocols with a conventional SCADA architecture and present the techniques we developed to overcome these challenges. The results illustrate that our survivable SCADA system not only functions correctly in the face of a cyber attack, but that it also processes in excess of 20 000 messages per second with a latency of less than 30 ms, making it suitable for even large-scale deployments managing thousands of remote terminal units.

Kaci, A., Kamwa, I., Dessaint, L.A., Guillon, S..  2014.  Synchrophasor Data Baselining and Mining for Online Monitoring of Dynamic Security Limits. Power Systems, IEEE Transactions on. 29:2681-2695.

When the system is in normal state, actual SCADA measurements of power transfers across critical interfaces are continuously compared with limits determined offline and stored in look-up tables or nomograms in order to assess whether the network is secure or insecure and inform the dispatcher to take preventive action in the latter case. However, synchrophasors could change this paradigm by enabling new features, the phase-angle differences, which are well-known measures of system stress, with the added potential to increase system visibility. The paper develops a systematic approach to baseline the phase-angles versus actual transfer limits across system interfaces and enable synchrophasor-based situational awareness (SBSA). Statistical methods are first used to determine seasonal exceedance levels of angle shifts that can allow real-time scoring and detection of atypical conditions. Next, key buses suitable for SBSA are identified using correlation and partitioning around medoid (PAM) clustering. It is shown that angle shifts of this subset of 15% of the network backbone buses can be effectively used as features in ensemble decision tree-based forecasting of seasonal security margins across critical interfaces.
 

Saeed, Imtithal A., Selamat, Ali, Rohani, Mohd Foad, Krejcar, Ondrej, Chaudhry, Junaid Ahsenali.  2020.  A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion Detection. IEEE Access. 8:180184–180209.
Multi-agent architectures have been successful in attaining considerable attention among computer security researchers. This is so, because of their demonstrated capabilities such as autonomy, embedded intelligence, learning and self-growing knowledge-base, high scalability, fault tolerance, and automatic parallelism. These characteristics have made this technology a de facto standard for developing ambient security systems to meet the open and dynamic nature of today's online communities. Although multi-agent architectures are increasingly studied in the area of computer security, there is still not enough empirical evidence on their performance in intrusions and attacks detection. The aim of this paper is to report the systematic literature review conducted in the context of specific research questions, to investigate multi-agent IDS architectures to highlight the issues that affect their performance in terms of detection accuracy and response time. We used pertinent keywords and terms to search and retrieve the most recent research studies, on multi-agent IDS architectures, from the major research databases and digital libraries such as SCOPUS, Springer, and IEEE Explore. The search processes resulted in a number of studies; among them, there were journal articles, book chapters, conference papers, dissertations, and theses. The obtained studies were assessed and filtered out, and finally, there were over 71 studies chosen to answer the research questions. The results of this study have shown that multi-agent architectures include several advantages that can help in the development of ambient IDS. However, it has been found that there are several issues in the current multi-agent IDS architectures that may degrade the accuracy and response time of intrusions and attacks detection. Based on our findings, the issues of multi-agent IDS architectures include limitations in the techniques, mechanisms, and schemes used for multi-agent IDS adaptation and learning, load balancing, scalability, fault-tolerance, and high communication overhead. It has also been found that new measurement metrics are required for evaluating multi-agent IDS architectures.
Kuang, Liwei, Yang, Laurence T., Rho, Seungmin(Charlie), Yan, Zheng, Qiu, Kai.  2016.  A Tensor-Based Framework for Software-Defined Cloud Data Center. ACM Trans. Multimedia Comput. Commun. Appl.. 12:74:1–74:23.

Multimedia has been exponentially increasing as the biggest big data, which consist of video clips, images, and audio files. Processing and analyzing them on a cloud data center have become a preferred solution that can utilize the large pool of cloud resources to address the problems caused by the tremendous amount of unstructured multimedia data. However, there exist many challenges in processing multimedia big data on a cloud data center, such as multimedia data representation approach, an efficient networking model, and an estimation method for traffic patterns. The primary purpose of this article is to develop a novel tensor-based software-defined networking model on a cloud data center for multimedia big-data computation and communication. First, an overview of the proposed framework is provided, in which the functions of the representative modules are briefly illustrated. Then, three models,—forwarding tensor, control tensor, and transition tensor—are proposed for management of networking devices and prediction of network traffic patterns. Finally, two algorithms about single-mode and multimode tensor eigen-decomposition are developed, and the incremental method is employed for efficiently updating the generated eigen-vector and eigen-tensor. Experimental results reveal that the proposed framework is feasible and efficient to handle multimedia big data on a cloud data center.

Solomonik, Edgar, Carson, Erin, Knight, Nicholas, Demmel, James.  2017.  Trade-Offs Between Synchronization, Communication, and Computation in Parallel Linear Algebra Computations. ACM Trans. Parallel Comput.. 3:3:1–3:47.

This article derives trade-offs between three basic costs of a parallel algorithm: synchronization, data movement, and computational cost. These trade-offs are lower bounds on the execution time of the algorithm that are independent of the number of processors but dependent on the problem size. Therefore, they provide lower bounds on the execution time of any parallel schedule of an algorithm computed by a system composed of any number of homogeneous processors, each with associated computational, communication, and synchronization costs. We employ a theoretical model that measures the amount of work and data movement as a maximum over that incurred along any execution path during the parallel computation. By considering this metric rather than the total communication volume over the whole machine, we obtain new insights into the characteristics of parallel schedules for algorithms with nontrivial dependency structures. We also present reductions from BSP and LogGP algorithms to our execution model, extending our lower bounds to these two models of parallel computation. We first develop our results for general dependency graphs and hypergraphs based on their expansion properties, and then we apply the theorem to a number of specific algorithms in numerical linear algebra, namely triangular substitution, Cholesky factorization, and stencil computations. We represent some of these algorithms as families of dependency graphs. We derive their communication lower bounds by studying the communication requirements of the hypergraph structures shared by these dependency graphs. In addition to these lower bounds, we introduce a new communication-efficient parallelization for stencil computation algorithms, which is motivated by results of our lower bound analysis and the properties of previously existing parallelizations of the algorithms.

Rani, Rinki, Kumar, Sushil, Dohare, Upasana.  2019.  Trust Evaluation for Light Weight Security in Sensor Enabled Internet of Things: Game Theory Oriented Approach. IEEE Internet of Things Journal. 6:8421–8432.
In sensor-enabled Internet of Things (IoT), nodes are deployed in an open and remote environment, therefore, are vulnerable to a variety of attacks. Recently, trust-based schemes have played a pivotal role in addressing nodes' misbehavior attacks in IoT. However, the existing trust-based schemes apply network wide dissemination of the control packets that consume excessive energy in the quest of trust evaluation, which ultimately weakens the network lifetime. In this context, this paper presents an energy efficient trust evaluation (EETE) scheme that makes use of hierarchical trust evaluation model to alleviate the malicious effects of illegitimate sensor nodes and restricts network wide dissemination of trust requests to reduce the energy consumption in clustered-sensor enabled IoT. The proposed EETE scheme incorporates three dilemma game models to reduce additional needless transmissions while balancing the trust throughout the network. Specially: 1) a cluster formation game that promotes the nodes to be cluster head (CH) or cluster member to avoid the extraneous cluster; 2) an optimal cluster formation dilemma game to affirm the minimum number of trust recommendations for maintaining the balance of the trust in a cluster; and 3) an activity-based trust dilemma game to compute the Nash equilibrium that represents the best strategy for a CH to launch its anomaly detection technique which helps in mitigation of malicious activity. Simulation results show that the proposed EETE scheme outperforms the current trust evaluation schemes in terms of detection rate, energy efficiency and trust evaluation time for clustered-sensor enabled IoT.
Khan, W. Z., Arshad, Q.-u-A., Hakak, S., Khan, M. K., Saeed-Ur-Rehman.  2020.  Trust Management in Social Internet of Things: Architectures, Recent Advancements and Future Challenges. IEEE Internet of Things Journal. :1—1.

Social Internet of Things (SIoT) is an extension of Internet of Things (IoT) that converges with Social networking concepts to create Social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human-to-human (H2H) and thing-to-thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “Social objects” that intelligently mimic the social behavior of human in the daily life. These social objects are equipped with social functionalities capable of discovering other social objects in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this paper, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state of the art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting social objects.

Pelechrinis, Konstantinos, Krishnamurthy, Prashant, Gkantsidis, Christos.  2014.  Trustworthy Operations in Cellular Networks: The Case of PF Scheduler. IEEE Trans. Parallel Distrib. Syst.. 25:292–300.

Cellular data networks are proliferating to address the need for ubiquitous connectivity. To cope with the increasing number of subscribers and with the spatiotemporal variations of the wireless signals, current cellular networks use opportunistic schedulers, such as the Proportional Fairness scheduler (PF), to maximize network throughput while maintaining fairness among users. Such scheduling decisions are based on channel quality metrics and Automatic Repeat reQuest (ARQ) feedback reports provided by the User's Equipment (UE). Implicit in current networks is the a priori trust on every UE's feedback. Malicious UEs can, thus, exploit this trust to disrupt service by intelligently faking their reports. This work proposes a trustworthy version of the PF scheduler (called TPF) to mitigate the effects of such Denial-of-Service (DoS) attacks. In brief, based on the channel quality reported by the UE, we assign a probability to possible ARQ feedbacks. We then use the probability associated with the actual ARQ report to assess the UE's reporting trustworthiness. We adapt the scheduling mechanism to give higher priority to more trusted users. Our evaluations show that TPF 1) does not induce any performance degradation under benign settings, and 2) it completely mitigates the effects of the activity of malicious UEs. In particular, while colluding attackers can obtain up to 77 percent of the time slots with the most sophisticated attack, TPF is able to contain this percentage to as low as 6 percent.

Anyfantis, D. I., Sarigiannidou, E., Rapenne, L., Stamatelatos, A., Ntemogiannis, D., Kapaklis, V., Poulopoulos, P..  2019.  Unexpected Development of Perpendicular Magnetic Anisotropy in Ni/NiO Multilayers After Mild Thermal Annealing. IEEE Magnetics Letters. 10:1–5.
We report on the significant enhancement of perpendicular magnetic anisotropy of Ni/NiO multilayers after mild annealing up to 90 min at 250 °C. Transmission electron microscopy shows that after annealing, a partial crystallization of the initially amorphous NiO layers occurs. This turns out to be the source of the anisotropy enhancement. Magnetic measurements reveal that even multilayers with Ni layers as thick as 7 nm, which in the as-deposited state showed inplane anisotropy with square hysteresis loops, show reduced in-plane remanence after thermal treatment. Hysteresis loops recorded with the field in the normal-to-film-plane direction provide evidence for perpendicular magnetic anisotropy with up and down magnetic domains at remanence. A plot of effective uniaxial magnetic anisotropy constant times individual Ni layer thickness as a function of individual Ni layer thickness shows a large change in the slope of the data attributed to a drastic change of volume anisotropy. Surface anisotropy showed a small decrease because of some layer roughening introduced by annealing.
Hernández, S., Lu, P. L., Granz, S., Krivosik, P., Huang, P. W., Eppler, W., Rausch, T., Gage, E..  2017.  Using Ensemble Waveform Analysis to Compare Heat Assisted Magnetic Recording Characteristics of Modeled and Measured Signals. IEEE Transactions on Magnetics. 53:1–6.

Ensemble waveform analysis is used to calculate signal to noise ratio (SNR) and other recording characteristics from micromagnetically modeled heat assisted magnetic recording waveforms and waveforms measured at both drive and spin-stand level. Using windowing functions provides the breakdown between transition and remanence SNRs. In addition, channel bit density (CBD) can be extracted from the ensemble waveforms using the di-bit extraction method. Trends in both transition SNR, remanence SNR, and CBD as a function of ambient temperature at constant track width showed good agreement between model and measurement. Both model and drive-level measurement show degradation in SNR at higher ambient temperatures, which may be due to changes in the down-track profile at the track edges compared with track center. CBD as a function of cross-track position is also calculated for both modeling and spin-stand measurements. The CBD widening at high cross-track offset, which is observed at both measurement and model, was directly related to the radius of curvature of the written transitions observed in the model and the thermal profiles used.

Koch, S., John, M., Worner, M., Muller, A., Ertl, T..  2014.  VarifocalReader #x2014; In-Depth Visual Analysis of Large Text Documents. Visualization and Computer Graphics, IEEE Transactions on. 20:1723-1732.

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

Conference Proceedings
Kim, Donghoon, Schaffer, Henry E., Vouk, Mladen A..  2015.  About PaaS Security. 3rd International IBM Cloud Academy Conference (ICACON 2015).

Platform as a Service (PaaS) provides middleware resources to cloud customers. As demand for PaaS services increases, so do concerns about the security of PaaS. This paper discusses principal PaaS security and integrity requirements, and vulnerabilities and the corresponding countermeasures. We consider three core cloud elements: multi-tenancy, isolation, and virtualization and how they relate to PaaS services and security trends and concerns such as user and resource isolation, side-channel vulnerabilities in multi-tenant environments, and protection of sensitive data

S. P. Rajamohana, K. Umamaheswari.  2017.  Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection.

With the development of the Internet, people are interested to share their views and opinions about the product on the web, forums, blogs etc. These online reviews are important for individual users and organization. Recently, it is a common tendency to the user to read the reviews or comments before purchasing some products or services. The online reviews are helpful for the business organizations in order to promote their product. However, in practice, these online reviews may be fake in order to promote or devalue the product. These fake reviews are called as opinion spam. Objective of the research paper is that, to select the best feature subset for detecting the fake review. To select a small subset of features out of the thousands of feature is important for accurate classification of review spam detection. Therefore, a good feature selection method is needed in order to speed up the processing rate, predictive accuracy. In this paper hybrid improved Binary Particle Swarm optimization (iBPSO) and cuckoo search (CS) is used for feature selection and Naive Bayes and k Nearest Neighbor classifier is used for classifying the review as spam and ham. Experimental results have shown that the proposed algorithm has yielded the best performance compared with the swarm intelligence techniques Binary Particle Swarm Optimization (BPSO) and Shuffled Frog Leaping (SFL).

Kim, Donghoon, Vouk, Mladen.  2015.  Securing Scientific Workflows. 2015 IEEE International Conference on Software Quality, Reliability and Security (QRS). :95-104.

This paper investigates security of Kepler scientificbworkflow engine. We are especially interested in Kepler-based scientific workflows that may operate in cloud environments. We find that (1) three security properties (i.e., input validation, remote access validation, and data integrity) are essential for making Kepler-based workflows more secure, and (2) that use of the Kepler provenance module may help secure Keplerbased workflows. We implemented a prototype security enhanced Kepler engine to demonstrate viability of use of the Kepler provenance module in provision and management of the desired security properties.