Power-aware anomaly detection in smartphones: An analysis of on-platform versus externalized operation

Suárez-Tangil, Guillermo; Tapiador, Juan E.; Peris-López, Pedro; Pastrana, Sergio

doi:10.1016/j.pmcj.2014.10.007

Cited by 26 publications

(13 citation statements)

References 31 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the measurement is performed for three popular applications. These applications are selected from three categories of popular activities: games, online social networking, and multimedia [52].…”

Section: Efficiencymentioning

confidence: 99%

AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection

Feizollah

Anuar

Salleh

et al. 2017

Computers & Security

Self Cite

210

View full text Add to dashboard Cite

The wide popularity of Android systems has been accompanied by increase in the number of malware targeting these systems. This is largely due to the open nature of the Android framework that facilitates the incorporation of third-party applications running on top of any Android device. Inter-process communication is one of the most notable features of the Android framework as it allows the reuse of components across process boundaries. This mechanism is used as gateway to access different sensitive services in the Android framework. In the Android platform, this communication system is usually driven by a late runtime binding messaging object known as Intent. In this paper, we evaluate the effectiveness of Android Intents (explicit and implicit) as a distinguishing feature for identifying malicious applications. We show that Intents are semantically rich features that are able to encode the intentions of malware when compared to other well-studied features such as permissions. We also argue that this type of feature is not the ultimate solution. It should be used in conjunction with other known features. We conducted experiments using a dataset containing 7,406 applications that comprise of 1,846 clean and 5,560 infected applications. The results show detection rate of 91% using Android Intent against 83% using Android permission. Additionally, experiment on combination of both features results in detection rate of 95.5%.

show abstract

Section: Efficiencymentioning

confidence: 99%

AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection

Feizollah

Anuar

Salleh

et al. 2017

Computers & Security

Self Cite

210

View full text Add to dashboard Cite

show abstract

“…For these scenarios, it is impractical to wait until location data corresponding to a full trajectory is sampled, given that the immediate detection of relevant events would be discarded. Moreover, such issue is framed in a more global dilemma about the optimality of MCC oriented strategies for solving any mobile computing need [22]. Several guidelines for defining when to perform computation offloading have been proposed and studied [23,24], which raises the question of whether an MCC approach is optimal for any combination of computation, latency and tracking accuracy requirements.…”

Section: Introductionmentioning

confidence: 99%

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Pérez-Torres¹,

Torres-Huitzil²,

Galeana-Zapién³

2016

Sensors

View full text Add to dashboard Cite

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

show abstract

“…More importantly, they come with a limited power supply, which incited developers to study the battery life [5], construct energy and power consumption models [6][7][8], and closely follow the influence of user actions and applications on power consumption [9]. Furthermore, studying the embedded systems' power profile has led to improving energy efficiency [10], reliability [11,12], and also monitoring and detecting anomalies [13][14][15] and energy hogs [16].…”

Section: Power Consumption Modeling In Embedded Socsmentioning

confidence: 99%

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Djedidi

Djeziri

2020

Processes

View full text Add to dashboard Cite

This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.

show abstract

Power-aware anomaly detection in smartphones: An analysis of on-platform versus externalized operation

Cited by 26 publications

References 31 publications

AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection

AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Contact Info

Product

Resources

About