Variations in CPU Power Consumption

Kistowski, Jóakim von; Block, Hansfried; Beckett, John; Spradling, Cloyce D.; Lange, Klaus-Dieter; Kounev, Samuel

doi:10.1145/2851553.2851567

Cited by 25 publications

(14 citation statements)

References 22 publications

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“…The CPU is one of the greatest consumers of power in the motherboard. Since modern CPUs are energy efficient, the momentary workload of the CPU directly affects the dynamic changes in its power consumption [50]. By regulating the workload of the CPU, it is possible to govern its power consumption, and hence control the current flow in the cable.…”

Section: A Signal Generationmentioning

confidence: 99%

PowerHammer: Exfiltrating Data From Air-Gapped Computers Through Power Lines

Guri

Zadov

Bykhovsky

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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In this paper we provide an implementation, evaluation, and analysis of PowerHammer, a malware (bridgeware [1]) that uses power lines to exfiltrate data from air-gapped computers. In this case, a malicious code running on a compromised computer can control the power consumption of the system by intentionally regulating the CPU utilization. Data is modulated, encoded, and transmitted on top of the current flow fluctuations, and then it is conducted and propagated through the power lines. This phenomena is known as a 'conducted emission'. We present two versions of the attack. Line level powerhammering: In this attack, the attacker taps the in-home power lines 1 that are directly attached to the electrical outlet. Phase level power-hammering: In this attack, the attacker taps the power lines at the phase level, in the main electrical service panel. In both versions of the attack, the attacker measures the emission conducted and then decodes the exfiltrated data. We describe the adversarial attack model and present modulations and encoding schemes along with a transmission protocol. We evaluate the covert channel in different scenarios and discuss signal-to-noise (SNR), signal processing, and forms of interference. We also present a set of defensive countermeasures. Our results show that binary data can be covertly exfiltrated from air-gapped computers through the power lines at bit rates of 1000 bit/sec for the line level power-hammering attack and 10 bit/sec for the phase level power-hammering attack. Power stationStep up transformerStep down transformerStep down transformer Residential building 22kV output 400 kV 132 kV 11 kV Fig. 1: A (simplified) schematic diagram of the electricity network C. Exfiltration through Power LinesIn this paper, we present a new type of electric (current flow) covert channel. The method, dubbed PowerHammer, enables attackers to exfiltrate information from air-gapped networks through AC power lines. We show that a malware running on a computer can regulate the power consumption of the system by controlling the workload of the CPU. Binary data can be modulated on the changes of the current flow, propagated through the power lines, and intercepted by an attacker. We present two versions of the attack. In line level power-hammering the adversary taps the power cables feeding the transmitting computer. In phase level power-hammering the adversary taps the power lines at the phase level, in the main electrical service panel. Using a non-invasive tap, the attacker measures the emission conducted on the power cables. Based on the signal received, the transmitted data is demodulated and decoded back to a binary form.

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Section: A Signal Generationmentioning

confidence: 99%

PowerHammer: Exfiltrating Data From Air-Gapped Computers Through Power Lines

Guri

Zadov

Bykhovsky

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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“…The CPU is one of the greatest consumers of power in the motherboard. Since modern CPUs are energy efficient, the momentary workload of the CPU directly affects the dynamic changes in its power consumption [51]. By regulating the workload of the CPU, it is possible to govern its power consumption, and hence control the magnetic field generated.…”

Section: A Signal Generationmentioning

confidence: 99%

ODINI: Escaping Sensitive Data From Faraday-Caged, Air-Gapped Computers via Magnetic Fields

Guri

Zadov

Elovici

2020

IEEE Trans.Inform.Forensic Secur.

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Air-gapped computers are computers which are kept isolated from the Internet, because they store and process sensitive information. When highly sensitive data is involved, an air-gapped computer might also be kept secluded in a Faraday cage. The Faraday cage prevents the leakage of electromagnetic signals emanating from various computer parts, which may be picked up by an eavesdropping adversary remotely. The air-gap separation, coupled with the Faraday shield, provides a high level of isolation, preventing the potential leakage of sensitive data from the system. In this paper, we show how attackers can bypass Faraday cages and air-gaps in order to leak data from highly secure computers. Our method is based on an exploitation of the magnetic field generated by the computer's CPU. Unlike electromagnetic radiation (EMR), low frequency magnetic radiation propagates though the air, penetrating metal shielding such as Faraday cages (e.g., compass still works inside Faraday cages). Since the CPU is an essential part of any computer, our covert channel is relevant to virtually any device with a CPU: desktop PCs, servers, laptops, embedded systems and Internet of Things (IoT) devices. We introduce a malware code-named 'ODINI' that can control the low frequency magnetic fields emitted from the infected computer by regulating the load of the CPU cores. Arbitrary data can be modulated and transmitted on top of the magnetic emission and received by a magnetic receiver ('bug') placed nearby. We provide technical background and examine the characteristics of the magnetic fields. We implement a malware prototype and discuss the design considerations along with the implementation details. We also show that the malicious code does not require special privileges (e.g., root) and can successfully operate from within isolated virtual machines (VMs) as well. We present signal generation, transmission algorithms, and discuss data encoding and modulation schemes. We analyze the covert channel and evaluate it on various types of computers. Finally, we propose different types of defensive countermeasures such as signal detection and signal jamming to cope with this type of threat.

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“…In our previous work, we studied an energy predictive model for servers in using corresponding indicators, such as system usage, hardware configurations, power management technologies applied, as well as environmental information. Many studies relating to real word empirical measurement found that beside IT load applied to the components (CPU, memory, network and storage), the power of servers can be affected by external factors, such as original fabrication process [13] [11], ambient temperature [14] [15] [16]. John C. McCullough et al [9] found that when applying a power model trained on Intel Core i5-540M-1 to an identical processor 540M-2, mean prediction errors could be increased from 10% to 23%.…”

Section: Related Workmentioning

confidence: 99%

“…The faster processors usually consume more than the slower ones. Jóakim et al [11] characterize the variation on CPU power consumption. Experiments are performed on three different platforms and different processors are picked for each platform.…”

Section: Related Workmentioning

confidence: 99%

“…They suggest using only power instrumentation for accurate power characterization. Among all of the components, processors are responsible for most of the power consumption and the variations [10] [11]. Recent experimental studies have identified several sources of variation among which are: frequency variation introduced by advanced performance enhancement technologies such as Turbo Boost and Multi-Threading [8] [7], within die parameter [12] and aging [10].…”

Section: Introductionmentioning

confidence: 99%

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Experimental Characterization of Variation in Power Consumption for Processors of Different Generations

Wang

Nörtershäuser

Masson

et al. 2019

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE C

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Data centers are energy-hungry facilities. Building energy consumption predictive models for servers is one of the solutions to use efficiently the resources. However, physical experiments have shown that even under the same conditions, identical processors consume different amount of energy to complete the same task. While this manufacturing variability has been observed and studied before, there is lack of evidence supporting the hypotheses due to limited sampling data, especially from the thermal characteristics. In this article, we compare the power consumption among identical processors for two Intel processors series with the same TDP (Thermal Design Power) but from different generations. The observed power variation of the processors in newer generation is much greater than the older one. Then, we propose our hypotheses for the underlying causes and validate them under precisely controlled environmental conditions. The experimental results show that, with the increase of transistor densities, difference of thermal characteristics becomes larger among processors, which has non-negligible contribution to the variation of power consumption for modern processors. This observation reminds us of re-calibrating the precision of the current energy predictive models. The manufacturing variability has to be considered when building energy predictive models for homogeneous clusters.

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Variations in CPU Power Consumption

Cited by 25 publications

References 22 publications

PowerHammer: Exfiltrating Data From Air-Gapped Computers Through Power Lines

PowerHammer: Exfiltrating Data From Air-Gapped Computers Through Power Lines

ODINI: Escaping Sensitive Data From Faraday-Caged, Air-Gapped Computers via Magnetic Fields

Experimental Characterization of Variation in Power Consumption for Processors of Different Generations

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