A Software Security Optimization Architecture (SoSOA) and its Adaptation for Mobile Applications

Considering that application security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to trying to spot insecurity apps, by analyzing user feedback on the Google Play platform and using sentiment analysis to determine the apps level of security. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application or not. But unfortunately, not all of these reviews are real, and as is known, the fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews to be the result is accurate and correct. This study is useful for both users wanting to install android apps and for developers interested in app optimization.

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“…Step 1: The collection of user reviews, after search and discussion we concluded to use the dataset from [32][33][34].…”

Section: Methodsmentioning

confidence: 99%

Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

Aljumah

Altuwijri

Alsuhaibani

et al. 2021

Int. J. Interact. Mob. Technol.

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“…The IoT comprising WSNs (Wireless Sensor Networks) WBANs (Wireless Body Area Networks) are playing a very essential role in numerous health-related issues and healthcare atmospheres [3][4][5][6], [29][30][31]. Smart homes, automated and hyper-connected homes, intelligent household systems, and devices including light bulbs, power outlets, thermostats, and other numerous applications are connected through the Internet which allows operating remotely.…”

Section: Over Of Iot Scope and Iot Architecturementioning

confidence: 99%

“…Whereas, agricultural IoT devices include green-house sensor systems, monitoring fields, and irrigation controllers. The streetlights water distribution systems are applications for city IoT domains [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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IoT-based Application of Information Security Triad

Reshan

2021

Int. J. Interact. Mob. Technol.

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Information Security is the foremost concern for IoT (Internet of things) devices and applications. Since the advent of IoT, its applications and devices have experienced an exponential increase in numerous applications which are utilized. Nowadays we people are becoming smart because we started using smart devices like a smartwatch, smart TV, smart home appliances. These devices are part of the IoT devices. The IoT device differs widely in capacity storage, size, computational power, and supply of energy. With the rapid increase of IoT devices in different IoT fields, information security, and privacy are not addressed well. Most IoT devices having constraints in computational and operational capabilities are a threat to security and privacy, also prone to cyber-attacks. This study presents a CIA triad-based information security implementation for the four-layer architecture of the IoT devices. An overview of layer-wise threats to the IoT devices and finally suggest CIA triad-based security techniques for securing the IoT devices.

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“…In particular, the Android OS has emerged as the top mobile OS with a significant global market value and presence. Records have shown that more than a billion Android devices have been purchased, with Google Play alone accounting for an estimated 65 billion mobile software application downloads [3,4]. Consequentially, Android's increasing popularity and usage, as well as the development of third-party app stores, has rendered it vulnerable to a variety of malware [5].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Akintola¹,

Balogun

Mojeed³

et al. 2022

Int. J. Interact. Mob. Technol.

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Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection.

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A Software Security Optimization Architecture (SoSOA) and its Adaptation for Mobile Applications

Cited by 4 publications

References 12 publications

Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

IoT-based Application of Information Security Triad

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

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