Research article Special Issues

Exfiltrating data from an air-gapped system through a screen-camera covert channel

  • In recent years, many methods of exfiltrating information from air-gapped systems, including electromagnetic, thermal, acoustic and optical covert channels, have been proposed. However, as a typical optical channel, the screen-camera method has rarely been considered; it is less covert because it is visible to humans. In this paper, inspired by the rapid upgrades of cameras and monitors, we propose an air-gapped screen-camera covert channel with decreased perceptibility that is suitable for complex content. Our method exploits the characteristics of the human vision system (HVS) and embeds quick response (QR) codes containing sensitive data in the displayed frames. This slight modification of the frames cannot be sensed by the HVS but can be recorded by the cameras. Then, using certain image processing techniques, we reconstruct the QR codes to some degree and extract the secret data with a certain level of robustness due to the error correction capacity of QR codes. In the scenario to which our method applies, we assume that a program has been installed in the target system and has the authority to modify the frames without affecting the normal operations of valid users. Cameras, such as web cameras, surveillance cameras and smartphone cameras, can be receivers in our method. We illustrate the applicability of our method to frames with complex content using several different cover images. Experiments involving different angles between the screen and the camera were conducted to highlight the feasibility of our method with angles of 0°,15° and 30° .

    Citation: Longlong Li, Yuliang Lu, Xuehu Yan, Dingwei Tan. Exfiltrating data from an air-gapped system through a screen-camera covert channel[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 7458-7476. doi: 10.3934/mbe.2019374

    Related Papers:

    [1] David Ludwig, Christian Breyer, A.A. Solomon, Robert Seguin . Evaluation of an onsite integrated hybrid PV-Wind power plant. AIMS Energy, 2020, 8(5): 988-1006. doi: 10.3934/energy.2020.5.988
    [2] Tilahun Nigussie, Wondwossen Bogale, Feyisa Bekele, Edessa Dribssa . Feasibility study for power generation using off- grid energy system from micro hydro-PV-diesel generator-battery for rural area of Ethiopia: The case of Melkey Hera village, Western Ethiopia. AIMS Energy, 2017, 5(4): 667-690. doi: 10.3934/energy.2017.4.667
    [3] Mulualem T. Yeshalem, Baseem Khan . Design of an off-grid hybrid PV/wind power system for remote mobile base station: A case study. AIMS Energy, 2017, 5(1): 96-112. doi: 10.3934/energy.2017.1.96
    [4] Virendra Sharma, Lata Gidwani . Recognition of disturbances in hybrid power system interfaced with battery energy storage system using combined features of Stockwell transform and Hilbert transform. AIMS Energy, 2019, 7(5): 671-687. doi: 10.3934/energy.2019.5.671
    [5] Virendra Sharma, Lata Gidwani . Optimistic use of battery energy storage system to mitigate grid disturbances in the hybrid power system. AIMS Energy, 2019, 7(6): 688-709. doi: 10.3934/energy.2019.6.688
    [6] Aaron St. Leger . Demand response impacts on off-grid hybrid photovoltaic-diesel generator microgrids. AIMS Energy, 2015, 3(3): 360-376. doi: 10.3934/energy.2015.3.360
    [7] K. M. S. Y. Konara, M. L. Kolhe, Arvind Sharma . Power dispatching techniques as a finite state machine for a standalone photovoltaic system with a hybrid energy storage. AIMS Energy, 2020, 8(2): 214-230. doi: 10.3934/energy.2020.2.214
    [8] Rashid Al Badwawi, Mohammad Abusara, Tapas Mallick . Speed control of synchronous machine by changing duty cycle of DC/DC buck converter. AIMS Energy, 2015, 3(4): 728-739. doi: 10.3934/energy.2015.4.728
    [9] Nadwan Majeed Ali, Handri Ammari . Design of a hybrid wind-solar street lighting system to power LED lights on highway poles. AIMS Energy, 2022, 10(2): 177-190. doi: 10.3934/energy.2022010
    [10] Essam A. Al-Ammar, Ghazi A. Ghazi, Wonsuk Ko, Hamsakutty Vettikalladi . Temperature impact assessment on multi-objective DGs and SCBs placement in distorted radial distribution systems. AIMS Energy, 2020, 8(2): 320-338. doi: 10.3934/energy.2020.2.320
  • In recent years, many methods of exfiltrating information from air-gapped systems, including electromagnetic, thermal, acoustic and optical covert channels, have been proposed. However, as a typical optical channel, the screen-camera method has rarely been considered; it is less covert because it is visible to humans. In this paper, inspired by the rapid upgrades of cameras and monitors, we propose an air-gapped screen-camera covert channel with decreased perceptibility that is suitable for complex content. Our method exploits the characteristics of the human vision system (HVS) and embeds quick response (QR) codes containing sensitive data in the displayed frames. This slight modification of the frames cannot be sensed by the HVS but can be recorded by the cameras. Then, using certain image processing techniques, we reconstruct the QR codes to some degree and extract the secret data with a certain level of robustness due to the error correction capacity of QR codes. In the scenario to which our method applies, we assume that a program has been installed in the target system and has the authority to modify the frames without affecting the normal operations of valid users. Cameras, such as web cameras, surveillance cameras and smartphone cameras, can be receivers in our method. We illustrate the applicability of our method to frames with complex content using several different cover images. Experiments involving different angles between the screen and the camera were conducted to highlight the feasibility of our method with angles of 0°,15° and 30° .


    Buildings energy efficiency is generally regarded in terms of energy consumption, energy costs and GHG emissions reduction in line with Europe 2020 targets. To date, in order to increase energy efficiency, deep retrofitting have been set in place [1]. At the same time, solar photovoltaic energy systems have been widely installed on homes in Europe and all over the world. In Europe and in Italy investments in domestic photovoltaic power plants (PV) were boosted by generous feed-in tariffs that made these investments extremely attractive for small private investors, such as homeowners [2,3]. It is commonly agreed that the greater the building energy efficiency, the greater the property market value. It is of paramount importance to determine the value that PV systems may add to home sale transactions [4]. The real estate hedonics literature explores how different housing attributes are capitalized into home prices. Solar installation can be regarded as a quality improvement in the home and solar homes can be considered as one of the better-known 'green product' sold in the market [5]. Although the residential solar home market is continuously growing, there is little direct evidence on the market capitalization effect. Recent contributions in the literature provide some capitalization estimates of the sales value of homes with PV systems installed relative to comparable homes without solar panels. These contributions document evidence of a solar home price premium in the US real estate market [4,5,6,7,8] and find that this premium is larger in environmentalist communities. They found that solar panels adds from 3% to 4% to the sale price of a home and the premium is on average equal to 4 $/W for an average-sized 3.6-kW PV system. Specifically according to [4] average market premiums across the sample of 22,822 homes analyzed are about 4 $/W or $15,000 for an average-sized 3.6-kW PV system.

    Although PV systems are widespread, their installation was not able to foster consumers to change their energy consumption patterns and increase efficient energy management. Therefore overall cost-savings by PV-generation systems resulted in a marginal impact on buildings energy efficiency increase and real estate market values.

    The aim of the paper is to investigate whether smart grids innovation can increase market values due to higher production and consumption flexibility. A smart grid (SG) gives de facto producers and consumers, the opportunity to be active in the energy market and strategically decide their optimal production/consumption pattern.

    In this paper, we provide a model based on the real option theory to determine the value of this flexibility and the related market value increase. We model the homeowner decision to invest in a PV plant and connect to an SG by comparison to the decision to invest in a PV plant not connected to an SG. We determine the property potential market value increase due to the opportunity to perform active energy management given by SGs and we compare this value increase to the PV plant value per se. To capture the value of managerial flexibility we implement a real option approach. The greater the flexibility, the greater the property market value.

    The remainder of the paper is organized as follows. In Section 2, we provide the basic model and we derive the value of flexibility of being connected to an SG. In Section 3, we calibrate the model and provides numerical simulations to test the model theoretical results with respect to the Italian electricity market and discuss results. Section 4 concludes.

    According to hedonic price modelling, property prices depends on their inherent attributes. These attributes usually include structural attributes (e.g., dwelling age and floor area, number of rooms and bedrooms), socio-economic characteristics of the surrounding neighbourhood (e.g., unemployed rate, racial diversity and occupations of the inhabitants) and locational attributes (e.g., proximity to services and pleasant landscapes).

    At the simplest, a hedonic equation is a regression of expenditure values on housing characteristics, where the independent variables represent the individual characteristics of the property, and the regression coefficients may be transferred into estimates of the implicit prices of these characteristics:

    V=ni=1Cipi (1)

    where Ci is the i-th characteristisc or attribute and pi is its implicit marginal price.

    Once a PV plant is installed, the value of the attribute CPV should capture the value of energy savings (in KWh) generated during the PV plant production life. Nonetheless, usually PV plants' size rather depends on peak demand/consumption energy quotas than on average daily average consumption quotas. In other words, the plant size is set to satisfy the peak end-user demand when solar radiation is maximum. This in turn allows homeowners to save energy costs by solar energy production and to trade in the market energy quotas that are not prosumed.

    When the PV plant is connected to an SG, the value of the attribute CPV + SG captures the value of energy savings due to solar energy production plus the value of flexibility to switch between the two following regimes: a) the homeowner can self-consume the energy produced by the PV plant and satisfy the rest of its demand by buying energy from the national grid at a fixed contractual price; and b) the homeowner can buy energy from the national grid at a fixed contractual price to satisfy its demand and sell totally the energy produced in the local market at its market (spot) price.

    Consequently, CPV and CPV+SG can be defined as follows:

    CPV=maxTPVE[erTPV(NPVPV(TPV))] (2)
    CPV+SG=maxTPV+SGE[erTPV+SG(NPVPV+SG(TPV+SG))] (3)

    where r is the risk adjusted rate of return, NPVPV and NPVPV + SG are the investment net present values once the PV system is installed and is not connected to an SG in the former case, and the PV plan is connected to an SG in the latter case respectively, and TPV and TPV+SG are the relative investment timings.

    Then, indicating by VPV the property value where a PV is installed and by VPV+SG the property value where a PV plant is installed and connected to an SG, by (1) we obtain:

    VPV=n1i=1Cipi+CPVpPV (4)
    VPV+SG=n1i=1Cipi+CPV+SGpPV+SG (5)

    where the last terms represent the increase in the property value attributable to the PV-th attribute and the PV + SG-th attribute respectively.

    In order to determine the effect of SGs on solar homes sale price, we base our analysis on the contribution by [9] and we introduce the following assumptions.

    ⅰ) The homeowner's energy demand per unit of time is normalized to 1 (i.e., 1 MWh). Energy demand can be represented as follows:

    1=ξα1+α2 (6)

    where α1 > 0 is the PV production per unit of time, ξ ∈ [0, 1] is the production quota used for prosumption and 0 < α2 ≤ 1 is the energy quota bought from the national grid. Storage is not possible, i.e. no batteries are included in the PV plant, and energy must be used as long as it is produced.

    ⅱ) The homeowner receives information on the selling price at the beginning of each time interval t and, based on this information, he makes the decision on the quota of the produced energy to be prosumed and on the quota to be sold in the local market.

    ⅲ) The homeowner cannot buy energy from the local market and can only contribute to the balancing of the electric system when demand is greater than supply by selling a quota of energy produced by the PV plant.

    By the above assumptions, net present values of PV plant not connected and connected to an SG respectively are:

    NPVPV=ξα1cr+(1ξ)α1v(t)rγI(α1) (7)
    NPVPV+SG=ξα1cr+(1ξ)α1v(t)rγˆAv(t)β1I(α1)if v(t)<cα1v(t)rγˆBv(t)β2I(α1)if v(t)>c (8)

    where ˆA=ξα1c1(rγ)cβ1rγβ1r(β2β1), ˆB=ξα1c1(rγ)cβ2rγβ2r(β2β1), c is the fixed buying price of energy, v(t) is the stochastic selling price of energy, β1 and β2 are the negative and the positive roots of the characteristic equation Φ(β) = 0.5σ2β(β-1) + γβ-r respectively. In other words ˆAv(t)β1 is the option value to switch from prosumption to energy selling in the local market when v(t) increases whereas ˆBv(t)β2 is the option value to switch back to prosumption when v(t) decreases.

    It can be demonstrated that the selling price of energy is driven by a following Geometric Brownian Motion [9,10]:

    dv(t)=γv(t)dt+σv(t)dz(t)     with v(0)=ν0 (9)

    where dz(t) is the increment of a Wiener process, σ is the istanataneous volatility and γ is the drift term lower than the market discount rate r ≥ γ.

    The value of flexibility of being connected to an SG is therefore given by:

    VPV+SGVPV=(CPV+SG)pPV+SG(CPV)pPV. (10)

    In order to test the model theoretical results we performed numerical simulations and used to calculate (9) parameter estimates provided by [9,10,11,12,13,14] with respect to the Italian electricity market, whereas we used to calculate (10), marginal prices (i.e., premiums) provided by [4,5] with respect to the US real estate market1. In what follows:

    1 As a caveat for our simulations, we outline that, to our knowledge, the only data available in the literature on implicit marginal prices refer to the US real estate market. We are conscious that such estimates should refer to the local market. Our estimate on Italian premiums is therefore a proxy to be further investigated in future research.

    1) c is the fixed buying price of energy as homeowners are connected to the national grid via a flat contract where the price is fixed over the contract length. It is representative of the average price paid by household consumers. The average basic energy price paid by household consumers over the period 2014–2018 can be set to c = 160 Euro/MWh net of taxes and levies [15];

    2) v(t) is the stochastic selling price of energy and it coincides with the price paid by the local Transmission System Operator (TSO) to procure the resources needed to manage, operate an control the power system. The Italian electric system is divided into different zones [9,10,11,15], therefore we use as a proxy for v(t) Italian zonal prices recorded over the period from 2010 through 20182. We verified that they are distributed as a Geometric Brownian Motion by testing for lognormality and the presence of unit root. The estimated parameters, for the geographical areas North and South [9,11] are reported in Table 1 (see Table 1):

    2 The Italian electric system is divided into different zones, among which physical energy exchanges are limited due to system security needs. These zones are grouped into: a) geographical zones; b) national virtual zones; c) foreign virtual zones; and d) market zones. Geographical zones represent a geographical portion of the national grid and are respectively classified into northern area, northern-central area, southern area, southern-central area, Sicily and Sardinia. Differences in zonal prices are determined by differences in transmission capacity, consumers' behavior [16] and different distributed production patterns.

    Table 1.  Estimated values for γ and σ.
    Geographical Areas γ σ
    North 0.5439% 41.88%
    South 0.5526% 45.69%

     | Show Table
    DownLoad: CSV

    As starting value v0 in each zone we took the yearly average selling prices recorded in the time interval January2016–December 2018 [9,17] as summarized in Table 2 (see Table 2):

    Table 2.  Average zonal prices over the period January 2016 - December 2018.
    Average zonal prices (€/MWh)
    January 2016-December 2018 North South
    55.42 49.82

     | Show Table
    DownLoad: CSV

    3) T (i.e., the plant's useful life) is equal to 20 years and 25 years;

    4) r (i.e., the risk adjusted rate of return) is equal to 4% and it is determined according to the Capital Asset Pricing Model r = rf + β·MRP, where rf is the risk-free interest rate, MRP is the market risk premium and β measures the systematic risk. According to [18,19], the Italian market risk premium is 5%. The risk-free interest rate is assumed as the average of interest rates on Italian Treasury Bonds (BTPs) with a maturity of 20 and 25 years [20] and the systematic risk of the photovoltaic sector ranges between 0.5 and 0.6 [9,10,11];

    5) α (i.e., the energy quota that can be prosumed by the homeowner during the photovoltaic day) is equal to 30% and 50%. The smaller value represents the actual average percentage of daily energy usage [21], whereas the greater value is meant to consider the effect of being connected to a smart grid in terms of energy management;

    6) I (i.e., the PV plant costs) are determined according to the Levelized Cost of Electricity, namely LCOE [21,22,23,24]. LCOE is equal to 110 Euro/MWh and 180 Euro/MWh respectively3.

    3 These value are consistent with the results of the analysis on LCOE 2018 by Lazard (https://www.lazard.com/perspective/levelized-cost-of-energy-and-levelized-cost-of-storage-2018/), according to which LCOE for residential solar PV (rooftop) plants ranges from 160$ to 267$.

    We implemented our analysis for two geographical zones in Italy: North and South. We considered a residential 3.6-kWp PV plant, which is the average nominal power of residential PV plants in Italy4. We performed simulations for two different scenarios: a) the homeowner decides to invest in a home with the opportunity to install a 3.6-kWp PV system connected to an SG; b) the homeowner decides to invest in a home with the opportunity to install a 3.6-kWp PV system not connected to an SG. We consider both a 3.6-kWp PV plant located in the North of Italy, which produces about 4,680 kWh/year, and a 3.6-kWp PV plant located in the South of Italy, which produces about 5,760 kWh/year.

    4 This installed power can satisfy the average demand of a household of four people (http://www.fotovoltaiconorditalia.it/idee/impianto-fotovoltaico-3-kwdimensioni-rendimenti). It is worth noting that on average, in Northern Italy, a 1-KWp plant produces about 1100–1500 KWh/year, whereas in the South, due to more favorable weather conditions, the average is 1500–1800 KWh/year (www.fotovoltaicoenergia.com; http://re.jrc.ec.europa.eu/pvgis/).

    In order to determine the premium for solar homes connected to an SG we should calculate the marginal price of the CPV+SG characteristic. This is not possible at present since SGs are not implemented yet in Italy. We can estimate the premium's lower bound by assuming that pPV+SG is equal to pPV multiplied by the percentage increase in the investment value due to the connection to an SG. In accordance with [4,5] we assume that the marginal price of the CPV characteristics, pPV is equal to 3.6%, that is solar houses add 3.6% to the sale price of a home.

    Tables 3 and 4 (see Tables 3 and 4) display the PV investment's values when the system is connected to an SG, the increase in the investment value due to the connection to an SG and the relative premiums in the North and the South of Italy respectively.

    Table 3.  Investment value of PV plants connected to an SG, investment value increase and relative premiums in the North of Italy for different LCOE, T and α.
    North
    T α C PV + SG C PV + SG/CPV P PV + SG
    LCOE = 110 20 0.3 10,577.22 1.05 3.77%
    25 0.3 10,687.75 1.05 3.78%
    20 0.5 15,649.12 1.09 3.93%
    25 0.5 16,058.13 1.09 3.94%
    LCOE = 180 20 0.3 9,654.65 1.05 3.80%
    25 0.3 9,341.81 1.06 3.80%
    20 0.5 15,025.02 1.10 3.96%
    25 0.5 14,712.19 1.10 3.97%

     | Show Table
    DownLoad: CSV
    Table 4.  Investment value of PV plants connected to an SG, investment value increase and relative premiums in the South of Italy for different LCOE, T and α.
    South
    T α C PV + SG C PV + SG/C PV P PV + SG
    LCOE=110 20 0.3 13,393.17 1.05 3.77%
    25 0.3 12,881.38 1.05 3.77%
    20 0.5 20,331.49 1.09 3.92%
    25 0.5 19,819.71 1.09 3.93%
    LCOE=180 20 0.3 11,848.54 1.05 3.79%
    25 0.3 11,535.78 1.05 3.79%
    20 0.5 18,786.86 1.10 3.95%
    25 0.5 18,474.11 1.10 3.95%

     | Show Table
    DownLoad: CSV

    The remarkable result is that in the North and in the South being connected to an SG increases by about 5–10% the PV investment's value and this quota increases (as expected) as energy savings and flexibility increase. Improvements in household energy management i.e., increasing α, induce homeowners to invest in bigger plants, whereas an increase in the plant useful life T reduces the optimal plant size. As in [9], the optimal invest strategy does not differ in the North and South: energy markets in Italy are sufficiently stable and correlated to show common performances. In the South, the plant size is larger and the investment value is greater, due to more favorable weather conditions. Both in the North and in the South, it is always optimal to wait to invest. Most of the plant's value is captured by the flexibility embedded in the SG. The increase in the property value due to flexibility and SGs is small both in the North and in the South. Consequently, price premiums of solar homes connected to an SG (pPV+SG) are greater than those of solar homes not connected to an SG (pPV). Price premiums increase on average by 5–10% due to the connection to an SG. Nonetheless, in the North and in the South the property value increase is due to the flexibility of waiting to invest rather than to the flexibility of being connected to an SG.

    We modeled the homeowner decision to invest in a PV plant and connect to an SG by comparison to the decision to invest in a solar home not connected to an SG. We determined the property potential market value increase due to the opportunity to perform active energy management given by SGs, and we compared this value increase to the PV plant value per se. Results of simulations performed according to zonal prices' trend and volatility in the North and South of Italy show that in the North and in the South being connected to an SG increases by about 5-10% the PV investment's value and this quota increases as energy savings and flexibility increase. The increase in the property value due to flexibility is small both in the North and in the South. Nonetheless, premiums of solar homes connected to an SG (pPV+SG) are greater than those of solar homes not connected to an SG (pPV): price premiums increase on average by 5–10% due to the connection to an SG.

    We acknowledge financial support by the University of Padova under the projects CPDA133332/13 and BIRD173594.

    All authors declare no conflicts of interest in this paper.



    [1] M. G. Kuhn and R. J. Anderson, Soft tempest: Hidden data transmission using electromagnetic emanations, International Workshop on Information Hiding, 1998, 124–142. Available from: https://link.springer.com/chapter/10.1007/3-540-49380-8 10.
    [2] M. Guri, G. Kedma, A. Kachlon, et al., Air hopper: Bridging the air-gap between isolated networks and mobile phones using radio frequencies, Proceedings of the 9th IEEE International Conference on Malicious and Unwanted Software: The Americas (MALWARE), 2014, 58–67. Available from: https://ieeexplore.ieee.org/abstract/document/6999418/.
    [3] M. Guri, A. Kachlon, O. Hasson, et al., GSMem: Data exfiltration from air-gapped computers over GSM frequencies, 24th USENIX Security Symposium (USENIX Security 15), 2015, 849–864. Available from: https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/guri.
    [4] M. Guri, M. Monitz and Y. Elovici, USBee: Air-gap covert-channel via electromagnetic emission from USB, 2016 14th Annual Conference on Privacy, Security and Trust (PST), 2016, 264–268. Available from: https://ieeexplore.ieee.org/abstract/document/7906972.
    [5] S. O'Malley and K.-K. Choo, Bridging the air gap: Inaudible data exfiltration by insiders, 20th Americas Conference on Information Systems (AMCIS 2014), 2014. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract id=2431593.
    [6] E. Lee, H. Kim and W. Y. Ji, Various threat models to circumvent air-gapped systems for preventing network attack, International workshop on information security applications, 2015. Available from: https://link.springer.com/chapter/10.1007/978-3-319-31875-2 16citeas.
    [7] M. Guri, Y. Solewicz, A. Daidakulov, et al., Fansmitter: Acoustic data exfiltration from (speakerless) air-gapped computers, arXiv preprint arXiv, (2016).
    [8] M. Guri, Y. A. Solewicz, A. Daidakulov, et al., Diskfiltration: Data exfiltration from speakerless air-gapped computers via covert hard drive noise, 98–115. arXiv preprint arXiv: 1608.03431, (2016).
    [9] M. Guri, M. Monitz, Y. Mirski, et al., Bitwhisper: Covert signaling channel between air- gapped computers using thermal manipulations, 2015 IEEE 28th Computer Security Foundations Symposium, 2015. Available from: https://ieeexplore.ieee.org/abstract/document/7243739.
    [10] Y. Mirsky, M. Guri and Y. Elovici, Hvacker: Bridging the air-gap by manipulating the environment temperature, Magdeburger J. zur Sicherheitsforschung, 14 (2017), 815–829.
    [11] V. Sepetnitsky, M. Guri and Y. Elovici, Exfiltration of information from air-gapped machines using monitor's LED indicator, 2014 IEEE Joint Intelligence and Security Informatics Conference,IEEE, 2014, 264–267. Available from: https://ieeexplore.ieee.org/abstract/document/6975588.
    [12] A. Lopes and D. Aranha, Platform-agnostic low-intrusion optical data exfiltration, 3rd International Conference on Information Systems Security and Privacy (ICISSP 2017), 2017, 474–480. Available from: http://dx.doi.org/10.5220/0006211504740480.
    [13] M. Guri, B. Zadov and Y. Elovici, LED-it-GO: Leaking (a lot of) data from air-gapped computers via the (small) hard drive LED, International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, 2017, 161–184. Available from: http://arxiv.org/abs/1702.06715.
    [14] M. Guri, B. Zadov, A. Daidakulov, et al., xLED: Covert data exfiltration from air-gapped networks via router leds, arXiv preprint arXiv, (2017).
    [15] Z. Zheng, W. Zhang, Z. Yang et al., Exfiltration of data from air-gapped networks via unmodulated led status indicators, arXiv preprint arXiv, (2017).
    [16] M. Guri, D. Bykhovsky and Y. Elovici, Air-jumper: Covert air-gap exfiltration/infiltration via security cameras & infrared (IR), Comput. Secur., 82 (2019), 15–29.
    [17] K. Jo, M. Gupta and S. K. Nayar, DisCo: Display-Camera Communication Using Rolling Shutter Sensors, ACM Trans. Graphics., 35 (2016), 1–13.
    [18] H. Hao, L. Rujun, Q. Guolei et al., Covert-optical transmission channel based on LED display, Commun. Technol., 51 (2018), 1689–1693.
    [19] M. Guri, O. Hasson, G. Kedma, et al., An optical covert-channel to leak data through an air-gap 2016 14th Annual Conference on Privacy, Security and Trust (PST), IEEE, 2016. Available from: https://ieeexplore.ieee.org/document/7906933.
    [20] Kolb Helga, Much of the construction of an image takes place in the retina itself through the use of specialized neural circuits, in How the Retina Works, American Scientist, (2003), 28–35.
    [21] J. L. Ecker, G. S. Lall, S. Haq, et al., Melanopsin cells are the principal conduits for rod cone input to non-image-forming vision, Nature, 7191 (2008), 102–106.
    [22] G. Buchsbaum, An Analytical Derivation of Visual Nonlinearity IEEE Trans. Biomed. Eng.,5(1980), 237–242.
    [23] D. Mandal, K. Panetta and S. Agaian, Human visual system inspired object detection and recognition, 2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), IEEE, 2012, 145–150. Available from:http://dx.doi.org/10.1109/TePRA.2012.6215669.
    [24] E. Simonson and J. Brozek, Flicker fusion frequency; background and applications, Physiol. Rev., 32 (1952), 349–378.
    [25] S. D. Perli, N. Ahmed and D. Katabi, PixNet: Interference-free wireless links using LCD-camera pairs, 16th Annual Conference on Mobile Computing and Networking, MobiCom 2010 (2010), 1952, 137–148. Available from: http://dx.doi.org 10.1145/1859995.1860012.
    [26] T. Hao, R. Zhou and G. Xing, COBRA: Color barcode streaming for smartphone systems, Proceedings of the 10th international conference on Mobile systems, applications, and services, ACM, 2012, 85–98. Available from: http://dx.doi.org/10.1145/2307636.2307645.
    [27] W. Hu, Lightsync: Unsynchronized visual communication over screen-camera links, Proceedings of the 19th annual international conference on Mobile computing & networking, ACM, 2013, 15–26. Available from: http://dx.doi.org/10.1145/2500423.2500437.
    [28] T. Li, C. An, X. Xiao, et al., Real-time screen-camera communication behind any scene Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2015, 197–211. Available from: http://dx.doi.org/10.1145/2742647.2742667.
    [29] A. Wang, C. Peng, O. Zhang, et al., InFrame: Multiflexing full-frame visible communication channel for humans and devices, Proceedings of the 13th ACM Workshop on Hot Topics in Networks, ACM, 2014. Available from: http://dx.doi.org/10.1145/2670518.2673867.
    [30] A. Wang, Z. Li, C. Peng, et al., Inframe++: Achieve simultaneous screen-human viewing and hidden screen-camera communication, Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2015, 181-195. Available from: http://dx.doi.org/10.1145/2742647.2742652.
    [31] A. Costin, Security of cctv and video surveillance systems: Threats, vulnerabilities, attacks, and mitigations, Proceedings of the 6th international workshop on trustworthy embedded devices, ACM, 2016.Available from: https://dl.acm.org/citation.cfm?id=2995290.
  • This article has been cited by:

    1. Fangyuan Chang, Tao Chen, Wencong Su, Qais Alsafasfeh, 2019, Charging Control of an Electric Vehicle Battery Based on Reinforcement Learning, 978-1-7281-0140-8, 1, 10.1109/IREC.2019.8754518
    2. Aristide Tolok Nelem, Pierre Ele, Papa Alioune Ndiaye, Salomé Ndjakomo Essiane, Mathieu Jean Pierre Pesdjock, Dynamic Optimization of Switching States of an Hybrid Power Network, 2021, 1598-6446, 10.1007/s12555-020-0088-3
    3. Hamza Alnawafah, Ahmad Harb, 2021, Modeling and Control for Hybrid Renewable Energy System in Smart Grid Scenario - A Case Study Part of Jordan Grid, 978-1-6654-3290-0, 1, 10.1109/IREC52758.2021.9624739
    4. Jay Kumar Pandey, 2023, Prediction for Solar Energy Different Climatic Conditions to Harvest Maximum Energy, 979-8-3503-3600-9, 1, 10.1109/ICNWC57852.2023.10127523
    5. William Brown, Qais Alsafasfeh, 2023, Transient Recovery Voltage Analysis Based on Wind Farms Integration Location, 979-8-3503-9651-5, 1, 10.1109/ICPS57144.2023.10142093
    6. Isaac Ortega-Romero, Xavier Serrano-Guerrero, Antonio Barragán-Escandón, Chistopher Ochoa-Malhaber, Optimal Integration of Distributed Generation in Long Medium-Voltage Electrical Networks, 2023, 10, 23524847, 2865, 10.1016/j.egyr.2023.09.057
    7. Kharisma Bani Adam, Jangkung Raharjo, Desri Kristina Silalahi, Bandiyah Sri Aprilia, , Integrative analysis of diverse hybrid power systems for sustainable energy in underdeveloped regions: A case study in Indonesia, 2024, 12, 2333-8334, 304, 10.3934/energy.2024015
    8. Rami Al-Hajj, Mohamad M. Fouad, Ali Assi Smieee, Emad Mabrouk, 2023, Ultra-Short-Term Forecasting of Wind Speed Using Lightweight Features and Machine Learning Models, 979-8-3503-3793-8, 93, 10.1109/ICRERA59003.2023.10269374
    9. Arben Gjukaj, Rexhep Shaqiri, Qamil Kabashi, Vezir Rexhepi, Renewable energy integration and distributed generation in Kosovo: Challenges and solutions for enhanced energy quality, 2024, 12, 2333-8334, 686, 10.3934/energy.2024032
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5179) PDF downloads(463) Cited by(1)

Figures and Tables

Figures(9)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog