scholarly journals A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 530 ◽  
Author(s):  
Imtiaz Ullah ◽  
Qusay H. Mahmoud
Keyword(s):  
Network Flow ◽  
Detection System ◽  
Activity Detection ◽  
Malicious Activity ◽  
Proposed Model ◽  
Tree Classifier ◽  
Level 1 ◽  
Iot Devices ◽  
The Impact ◽  
Level 2

The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifies the category or subcategory of detected malicious activity. When the network flow classified as an anomaly by the level-1 model, then the level-1 model forwards the stream to the level-2 model for further investigation to find the category or subcategory of the detected anomaly. Our proposed model constructed on flow-based features of the IoT network. Flow-based detection methodologies only inspect packet headers to classify the network traffic. Flow-based features extracted from the IoT Botnet dataset and various machine learning algorithms were investigated and tested via different cross-fold validation tests to select the best algorithm. The decision tree classifier yielded the highest predictive results for level-1, and the random forest classifier produced the highest predictive results for level-2. Our proposed model Accuracy, Precision, Recall, and F score for level-1 were measured as 99.99% and 99.90% for level-2. A two-level anomalous activity detection system for IoT networks we proposed will provide a robust framework for the development of malicious activity detection system for IoT networks. It would be of interest to researchers in academia and industry.

Fair value's effects on closed-end funds' discounts and premia: is level 3 the sole perpetrator?

2020 ◽  
Vol 46 (8) ◽  
pp. 1001-1022 ◽  
Author(s):  
Steve Fortin ◽  
Ahmad Hammami ◽  
Michel Magnan
Keyword(s):  
Relevant Information ◽  
Fair Value ◽  
Content Type ◽  
Level 3 ◽  
Closed End Funds ◽  
Level 1 ◽  
Value Hierarchy ◽  
Valuation Uncertainty ◽  
The Impact ◽  
Level 2

PurposeThis study examines the long-term link between fair valuation uncertainty and discounts/premia in closed-end funds. This study argues that, in exploring the close-end funds puzzle, prior research generally omits to consider the uncertainty surrounding the measurement of funds' financial disclosure, as reflected in the fair value hierarchy, when investment specialty differs across funds.Design/methodology/approachRegressions were employed to explore how the fair value hierarchy affects closed-end funds' discounts/premia when investment specialty differs. The authors also examine the effects pre- and post-2012 to explore if that relationship changes due to the additional disclosure requirements enacted at the end of 2011.FindingsThe authors find that the three levels of the fair value hierarchy have effects that vary according to a fund's specialty. For equity specialized funds, Level 3 significantly increases discounts and decreases premia, suggesting the impact of valuation uncertainty that underlies Level 3 estimates; this relationship disappears (decreases in severity) for premia (discount) experiencing funds post-2012. In contrast, Level 1 and Level 2 do not have any significant effect on discounts or premia except that post-2012, Level 2 begins to display discount decreasing effects. For bond specialized funds, no significant association was noted between premia and any of the fair value levels except that post-2012, Level 3 begins to display premium increasing effects. However, results are different for discounts. The authors note that Level 1 valuations significantly increase discounts, but only post-2012; Level 2 valuations significantly decrease discounts (pre- and post-2012), consistent with such estimates incorporating unique and relevant information; and Level 3 valuations do not have a significant effect on discounts.Originality/valueThe results of this study revisit prior evidence and indicate that results about the effects of fair value measurement and the closed-end funds' puzzle are sensitive to the period length being considered and the investment specialty of the fund. The authors also note that additional disclosure regarding Level 3 valuation inputs decreases market concern for valuation uncertainty and increases the liquidity benefits of investing in Level 3 carrying funds.

Managerial ability and fair value accounting: evidence from nonfinancial firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wray Bradley ◽  
Li Sun
Keyword(s):  
Design Methodology ◽  
Fair Value ◽  
Managerial Ability ◽  
Fair Value Accounting ◽  
Content Type ◽  
Level 3 ◽  
Level 1 ◽  
The Impact ◽  
Nonfinancial Firms ◽  
Level 2

Purpose The purpose of this study is to examine the relation between managerial ability and fair value inputs (measured as fair value intensity) for nonfinancial firms. Design/methodology/approach This study uses regression analysis to investigate the impact of managerial ability on the level of fair value inputs. Findings This study finds significant and positive relations between managerial ability and use of Level 1 and Level 2 fair value inputs. On the other hand, this study finds an insignificant relation between managerial ability and Level 3 inputs. Originality/value The findings contribute to two research streams. To the best of the author’s knowledge, this is perhaps the first study that directly examines the link between managerial ability and fair value inputs.

scholarly journals Comparison between the assimilation of IASI Level 2 retrievals and Level 1 radiances for ozone reanalyses

2019 ◽  
Author(s):  
Emanuele Emili ◽  
Brice Barret ◽  
Eric Le Flochmoën ◽  
Daniel Cariolle
Keyword(s):  
Transport Model ◽  
Atmospheric Composition ◽  
Chemical Transport Model ◽  
Atmospheric Sounding ◽  
Level 1 ◽  
The Tropics ◽  
L1 And L2 ◽  
The Impact ◽  
Level 2

Abstract. The prior information used for Level 2 (L2) retrievals in the thermal infrared can influence the quality of the retrievals themselves and, therefore, their further assimilation in atmospheric composition models. In this study we evaluate the differences between assimilating L2 ozone profiles and Level 1 (L1) radiances from the Infrared Atmospheric Sounding Interferometer (IASI). We minimized potential differences between the two approaches by employing the same radiative transfer code (RTTOV) and a very similar setup for both the L2 retrievals (1D-Var) and the L1 assimilation (3D-Var). We computed hourly 3D-Var reanalyses assimilating respectively L1 and L2 data in the chemical transport model MOCAGE and compared the resulting O3 fields among each other and against ozonesondes. We also evaluated the joint assimilation of limb measurements from the Microwave Limb Sounder (MLS) on top of IASI to assess the impact of stratospheric O3 on tropospheric reanalyses. Results indicate that significant differences can arise between L2 and L1 assimilation, especially in regions where the L2 prior is biased (at the tropics and in the southern hemisphere in this study). L1 and L2 assimilation give instead very similar results in the northern hemisphere, especially when MLS measurements are used to constrain the stratospheric O3 column. We conclude this study by listing remaining issues that are common to both the L1 and L2 approaches and that deserve further research.

scholarly journals OLCI A/B Tandem Phase Analysis, Part 2: Benefits of Sensors Harmonisation for Level 2 Products

2020 ◽  
Vol 12 (17) ◽  
pp. 2702 ◽  
Author(s):  
Nicolas Lamquin ◽  
Alexis Déru ◽  
Sébastien Clerc ◽  
Ludovic Bourg ◽  
Craig Donlon
Keyword(s):  
Phase Analysis ◽  
Temporal Stability ◽  
Ground Truth ◽  
Vicarious Calibration ◽  
Requirements Specifications ◽  
Level 1 ◽  
Cross Calibration ◽  
The Individual ◽  
The Impact ◽  
Level 2

This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the tandem phase analysis. Sentinel-3B was manoeuvred into a tandem configuration with its operational twin Sentinel-3A already in orbit few weeks after its launch, which was followed by a short drift phase during which Sentinel-3B was progressively moved to a specific orbit phasing of 140° separation from the sentinel-3A. Harmonisation is performed at Level 1 for the radiometric alignment of the OLCI-A TOA radiances to the ones of OLCI-B, considering the slight spectral differences between the two instruments. The benefits of this harmonisation for the main Level 2 products are assessed in the present manuscript for both land and water products. The results validate such benefits showing accuracy between the two sensors after harmonisation better than the products requirements specifications. For the water processing branch, this accuracy opens a path toward an ensemble Sentinel-3 system vicarious calibration with ground-truth measurements. For land products, the tandem phase analysis is also an opportunity to demonstrate that the terrestrial chlorophyll index product requires improvements of the preliminary spectral adjustment of the red-edge channel at 709 nm. As comparisons from the measurements acquired over the tandem phase provides confidence in the alignment of the OLCI-A and OLCI-B series of products, preliminary analysis of the measurements acquired over the drift phase provides the first insights into the sensitivity of the processing algorithms to the geometry of acquisition as well as to calibration residuals of the OLCI field-of-view. As the harmonisation currently performs a radiometric alignment of OLCI-A to OLCI-B, the question of the reference sensor for operational implementation of the harmonisation raises concerns on the individual quality of the calibration of each sensor, notably their across-track consistency. Following the investigations performed at Level 1, where relatively strong calibration residuals are shown between the OLCI cameras and very similarly for both instruments; we discuss the impact of these residuals at L2 using an empirical correction and further conclude with the need to address these problematics with more attention in the future. We conclude with the extreme usefulness of the tandem phase analysis, presently for Level 2 products, and the need to further monitor the temporal stability of the cross-calibration of the OLCI payloads with a view to implementing their harmonisation at operational level.

Illiquid Assets and the Opacity Discount in Banks’ Valuation

2021 ◽  
Author(s):  
Giulio Anselmi
Keyword(s):  
Negative Relationship ◽  
Relative Weight ◽  
Fair Value ◽  
Book Value ◽  
Valuation Technique ◽  
Level 3 ◽  
Level 1 ◽  
The Impact ◽  
Level 2 ◽  
Illiquid Assets

The paper investigates the impact of fair value accounting for illiquid assets (so-called ‘Level 2’ and ‘Level 3’ assets by accounting rules) on banks’ valuation and focuses on the change in relative weight of Level 3 (the most opaque and illiquid assets) with respect to Level 2 assets. The boundary between Level 3 and Level 2 assets is blurred and less clear than the one between Level 1 and Level 2 assets. Such unclear borderline entails corporate governance issues and provides room for opportunistic behavior by managers to opt for less transparent instruments. The paper proposes the change in Level 3-to-Level 2 assets ratio as a new measure to capture deviations in the opacity of bank assets and suggests a negative relationship between this ratio and bank’s price-to-book value. The rationale behind this relationship is that market participants interpret growth in Level 3-to-Level 2 assets ratio as an increase in bank’s opacity, since Level 3 assets might be as illiquid as Level 2 assets with the benefit of a less transparent model-based valuation technique. Based on a sample of 33 European banks from 2009 to 2018, I find that an increase of 100[Formula: see text]bps in Level 3-to-Level 2 assets ratio is linked to a decrease of about 74[Formula: see text]bps in the price-to-book value. Results are robust for different measures of firm relative valuation and using a different measure of illiquidity in fair value assets holdings (Level 2-to-Level 1 assets ratio).

Value Relevance of FAS No. 157 Fair Value Hierarchy Information and the Impact of Corporate Governance Mechanisms

2010 ◽  
Vol 85 (4) ◽  
pp. 1375-1410 ◽  
Author(s):  
Chang Joon Song ◽  
Wayne B. Thomas ◽  
Han Yi
Keyword(s):  
Corporate Governance ◽  
Value Relevance ◽  
Fair Value ◽  
Governance Mechanisms ◽  
Corporate Governance Mechanisms ◽  
Level 3 ◽  
Level 1 ◽  
The Impact ◽  
Fair Value Measurements ◽  
Level 2

ABSTRACT: Statement of Financial Accounting Standards No. 157 (FAS No. 157), Fair Value Measurements, prioritizes the source of information used in fair value measurements into three levels: (1) Level 1 (observable inputs from quoted prices in active markets), (2) Level 2 (indirectly observable inputs from quoted prices of comparable items in active markets, identical items in inactive markets, or other market-related information), and (3) Level 3 (unobservable, firm-generated inputs). Using quarterly reports of banking firms in 2008, we find that the value relevance of Level 1 and Level 2 fair values is greater than the value relevance of Level 3 fair values. In addition, we find evidence that the value relevance of fair values (especially Level 3 fair values) is greater for firms with strong corporate governance. Overall, our results support the relevance of fair value measurements under FAS No. 157, but weaker corporate governance mechanisms may reduce the relevance of these measures.

scholarly journals Secure and Authenticated Naming Scheme for IoT Devices in Heterogeneous Networks

2019 ◽  
Vol 8 (10) ◽  
pp. 3815-3820
Keyword(s):  
Performance Metrics ◽  
Detection System ◽  
Sensor Nodes ◽  
Heterogeneous Model ◽  
Diffie Hellman ◽  
Main Challenge ◽  
Proposed Model ◽  
Diffie Hellman Key Exchange ◽  
Iot Devices ◽  
Hyper Elliptic Curve

Naming the device is the main challenge in IoT and getting authentication from the sensors is possible only with secured naming services. Naming a form of addressing the sensor nodes should be readable or understandable by humans or M2M communications. Existing naming schemes have scalability and security limitations. Hence, this paper proposes an efficient and secure distributed naming scheme for IoT in heterogeneous network. Hyper -Elliptic Curve Diffie - Hellman Key Exchange (HECDH) is used in the naming scheme for the exchange of keys. This technique reduces the communication and computation complexity. The heterogeneous model of the proposed technique is simulated and the analysis has been carried out. Digital Signature (i.e., Private Key) is used to verify the node authencity. Monitoring of the node in the network is implemented using IP based Position monitoring and Intruder Detection system (IDS) is implemented for detecting the attacks. The performance metrics such Energy consumption, PDR, Total Traffic, Delay, Average Response Time and Throughput of the proposed model was simulated using Contiki / Cooja version 2.7 simulator.

A malicious activity detection system utilizing predictive modeling in complex environments

2014 ◽  
Author(s):  
Abdullah Almaatouq ◽  
Ahmad Alabdulkareem ◽  
Mariani Nouh ◽  
Mansour Alsaleh ◽  
Abdulrahman Alarifi ◽  
...  
Keyword(s):  
Predictive Modeling ◽  
Detection System ◽  
Activity Detection ◽  
Complex Environments ◽  
Malicious Activity

scholarly journals Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps

2018 ◽  
Vol 7 (10) ◽  
pp. 408 ◽  
Author(s):  
Elif Sertel ◽  
Raziye Topaloğlu ◽  
Betül Şallı ◽  
Irmak Yay Algan ◽  
Gül Aksu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landscape Metrics ◽  
Observation Data ◽  
Landscape Characteristics ◽  
Object Based ◽  
Level 3 ◽  
Level 1 ◽  
The Impact ◽  
Level 2

This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes.

scholarly journals Comparison between the assimilation of IASI Level 2 ozone retrievals and Level 1 radiances in a chemical transport model

2019 ◽  
Vol 12 (7) ◽  
pp. 3963-3984
Author(s):  
Emanuele Emili ◽  
Brice Barret ◽  
Eric Le Flochmoën ◽  
Daniel Cariolle
Keyword(s):  
Radiative Transfer ◽  
Prior Information ◽  
Transport Model ◽  
Chemical Transport ◽  
Atmospheric Composition ◽  
Chemical Transport Model ◽  
Level 1 ◽  
L1 And L2 ◽  
The Impact ◽  
Level 2

Abstract. The prior information used for Level 2 (L2) retrievals in the thermal infrared can influence the quality of the retrievals themselves and, therefore, their further assimilation in atmospheric composition models. In this study we evaluate the differences between assimilating L2 ozone profiles and Level 1 (L1) radiances from the Infrared Atmospheric Sounding Interferometer (IASI). We minimized potential differences between the two approaches by employing the same radiative transfer code (Radiative Transfer for TOVS, RTTOV) and a very similar setup for both the L2 retrievals (1D-Var) and the L1 assimilation (3D-Var). We computed hourly 3D-Var analyses assimilating L1 and L2 data in the chemical transport model MOCAGE and compared the resulting O3 fields among each other and against ozonesondes. We also evaluated the joint assimilation of limb measurements from the Microwave Limb Sounder (MLS) in combination with IASI to assess the impact of stratospheric O3 on tropospheric analyses. Results indicate that significant differences can arise between L2 and L1 assimilation, especially in regions where the L2 prior information is strongly biased (at low latitudes in this study). In these regions the L1 assimilation provides a better variability of the free-troposphere ozone column. L1 and L2 assimilation instead give very similar results at high latitudes, especially when MLS measurements are used to constrain the stratospheric O3 column. A critical analysis of the potential benefits and drawbacks of L1 assimilation is given in the conclusions. We also list remaining issues that are common to both the L1 and L2 approaches and that deserve further research.

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