scholarly journals Game Theory based Cyber-Insurance to Cover Potential Loss from Mobile Malware Exploitation

2021 ◽  
Vol 2 (2) ◽  
pp. 1-24
Author(s):  
Li Wang ◽  
S. Sitharama Iyengar ◽  
Amith K. Belman ◽  
Paweł Śniatała ◽  
Vir V. Phoha ◽  
...  
Keyword(s):  
Mathematical Framework ◽  
Entertainment Education ◽  
Android Malware ◽  
Cyber Insurance ◽  
Monetary Value ◽  
Mobile Malware ◽  
Device User ◽  
Game Theoretic ◽  
Potential Loss ◽  
Malicious Apps

Potential for huge loss from malicious exploitation of software calls for development of principles of cyber-insurance. Estimating what to insure and for how much and what might be the premiums poses challenges because of the uncertainties, such as the timings of emergence and lethality of malicious apps, human propensity to favor ease by giving more privilege to downloaded apps over inconvenience of delay or functionality, the chance of infection determined by the lifestyle of the mobile device user, and the monetary value of the compromise of software, and so on. We provide a theoretical framework for cyber-insurance backed by game-theoretic formulation to calculate monetary value of risk and the insurance premiums associated with software compromise. By establishing the conditions for Nash equilibrium between strategies of an adversary and software we derive probabilities for risk, potential loss, gain to adversary from app categories, such as lifestyles, entertainment, education, and so on, and their prevalence ratios. Using simulations over a range of possibilities, and using publicly available malware statistics, we provide insights about the strategies that can be taken by the software and the adversary. We show the application of our framework on the most recent mobile malware data (2018 ISTR report—data for the year 2017) that consists of the top five Android malware apps: Malapp, Fakeinst, Premiumtext, Maldownloader , and Simplelocker and the resulting leaked phone number, location information, and installed app information. Uniqueness of our work stems from developing mathematical framework and providing insights of estimating cyber-insurance parameters through game-theoretic choice of strategies and by showing its efficacy on a recent real malicious app data . These insights will be of tremendous help to researchers and practitioners in the security community.

scholarly journals A Game Theoretic Approach for Eco-Design and Remanufacturing Considering Take-Back Policy

2020 ◽  
Vol 12 (17) ◽  
pp. 7174
Author(s):  
Xiaoxiao Chang ◽  
Guangye Xu ◽  
Qian Wang ◽  
Yongguang Zhong
Keyword(s):  
Environmental Impact ◽  
Stackelberg Game ◽  
Theoretic Approach ◽  
Subsidy Policy ◽  
Monetary Value ◽  
Game Equilibrium ◽  
Take Back ◽  
Game Theoretic ◽  
The Government ◽  
Game Theoretic Approach

This paper mainly aims at investigating the governments’ take-back policy of penalty or subsidy that motivates eco-design or remanufacturing. For this purpose, we consider a two-stage Stackelberg game between a government and a manufacturer. The government first decides to impose a take-back penalty or offer a take-back subsidy, and then the manufacturer selects to remanufacture or invest in eco-design as a response to the take-back policy. Upon analyzing and comparing game equilibrium, we find that the government prefers to offer a subsidy policy for eco-design and to impose a penalty policy for remanufacturing. The manufacturer will decide on investing in eco-design when the monetary value of the environmental impact of landfill and eco-design coefficient is medium. However, if the eco-design coefficient is high, the manufacturer practices remanufacturing instead of eco-design whether penalized and subsidized. The present study provides a set of guidelines in practical managerial recommendations for governments and manufacturers.

scholarly journals Blockchain Based Detection of Android Malware using Ranked Permissions

2021 ◽  
Vol 10 (5) ◽  
pp. 68-75
Author(s):  
Siddhant Gupta ◽  
Siddharth Sethi ◽  
Srishti Chaudhary ◽  
Anshul Arora
Keyword(s):  
Mobile Devices ◽  
Language Processing ◽  
High Reliability ◽  
Low Cost ◽  
Information Value ◽  
Third Party ◽  
Detection Accuracy ◽  
Financial Loss ◽  
Android Malware ◽  
Malicious Apps

Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.

scholarly journals Papers on pragmasemantics

2009 ◽  
Vol 51 ◽  
pp. 216
Author(s):  
Anton Benz ◽  
Reinhard Blutner
Keyword(s):  
Game Theory ◽  
Natural Language ◽  
Optimality Theory ◽  
Cognitive Skills ◽  
Research Programme ◽  
Strategic Interaction ◽  
Mathematical Framework ◽  
Game Theoretic ◽  
Bidirectional Optimality Theory ◽  
Game Theoretic Pragmatics

Optimality theory as used in linguistics (Prince & Smolensky, 1993/2004; Smolensky & Legendre, 2006) and cognitive psychology (Gigerenzer & Selten, 2001) is a theoretical framework that aims to integrate constraint based knowledge representation systems, generative grammar, cognitive skills, and aspects of neural network processing. In the last years considerable progress was made to overcome the artificial separation between the disciplines of linguistic on the one hand which are mainly concerned with the description of natural language competences and the psychological disciplines on the other hand which are interested in real language performance. The semantics and pragmatics of natural language is a research topic that is asking for an integration of philosophical, linguistic, psycholinguistic aspects, including its neural underpinning. Especially recent work on experimental pragmatics (e.g. Noveck & Sperber, 2005; Garrett & Harnish, 2007) has shown that real progress in the area of pragmatics isn’t possible without using data from all available domains including data from language acquisition and actual language generation and comprehension performance. It is a conceivable research programme to use the optimality theoretic framework in order to realize the integration. Game theoretic pragmatics is a relatively young development in pragmatics. The idea to view communication as a strategic interaction between speaker and hearer is not new. It is already present in Grice' (1975) classical paper on conversational implicatures. What game theory offers is a mathematical framework in which strategic interaction can be precisely described. It is a leading paradigm in economics as witnessed by a series of Nobel prizes in the field. It is also of growing importance to other disciplines of the social sciences. In linguistics, its main applications have been so far pragmatics and theoretical typology. For pragmatics, game theory promises a firm foundation, and a rigor which hopefully will allow studying pragmatic phenomena with the same precision as that achieved in formal semantics. The development of game theoretic pragmatics is closely connected to the development of bidirectional optimality theory (Blutner, 2000). It can be easily seen that the game theoretic notion of a Nash equilibrium and the optimality theoretic notion of a strongly optimal form-meaning pair are closely related to each other. The main impulse that bidirectional optimality theory gave to research on game theoretic pragmatics stemmed from serious empirical problems that resulted from interpreting the principle of weak optimality as a synchronic interpretation principle. In this volume, we have collected papers that are concerned with several aspects of game and optimality theoretic approaches to pragmatics.  

Does Congestion Always Hurt? Managing Discount Under Congestion in a Game-Theoretic Setting

2021 ◽  
Author(s):  
Rajib L. Saha ◽  
Sumanta Singha ◽  
Subodha Kumar
Keyword(s):  
Web Services ◽  
Lower Cost ◽  
End Users ◽  
Cloud Services ◽  
Key Factors ◽  
Amazon Web Services ◽  
Game Theoretic ◽  
Potential Loss ◽  
The Cost

Many firms buy cloud services from cloud vendors, such as Amazon Web Services to serve end users. One of the key factors that affect the quality of cloud services is congestion. Congestion leads to a potential loss of end users, resulting in lower demand for cloud services. Although discount can stimulate demand, its effect under congestion is ambiguous; a higher discount leads to higher demand, but it can further lead to higher congestion, thereby lowering demand. We explore how congestion moderates both cloud vendor pricing and the buyer’s fulfillment decisions. We seek to answer how the congestion sensitivity of the end users and the cost of technology impact buyer profitability and the cloud vendor’s choice of discount. We also examine how the cost of technology determines the buyer’s willingness to pass on savings to end users. Our results show that the buyer is not necessarily worse off even when the end users are more intolerant to congestion. In fact, when end users are more congestion sensitive, the demand for cloud services can sometimes increase, and the discount offered by the vendor can decrease. We also observe that a lower cost of technology can sometimes hurt the buyer, and the buyer can pass on lower benefits to end users.

scholarly journals Demographic noise can reverse the direction of deterministic selection

2016 ◽  
Vol 113 (32) ◽  
pp. E4745-E4754 ◽  
Author(s):  
George W. A. Constable ◽  
Tim Rogers ◽  
Alan J. McKane ◽  
Corina E. Tarnita
Keyword(s):  
Public Good ◽  
Selective Advantage ◽  
Reverse Direction ◽  
Mathematical Framework ◽  
General Effect ◽  
Demographic Stochasticity ◽  
The Public ◽  
Game Theoretic ◽  
Demographic Noise ◽  
Stochastic Selection

Deterministic evolutionary theory robustly predicts that populations displaying altruistic behaviors will be driven to extinction by mutant cheats that absorb common benefits but do not themselves contribute. Here we show that when demographic stochasticity is accounted for, selection can in fact act in the reverse direction to that predicted deterministically, instead favoring cooperative behaviors that appreciably increase the carrying capacity of the population. Populations that exist in larger numbers experience a selective advantage by being more stochastically robust to invasions than smaller populations, and this advantage can persist even in the presence of reproductive costs. We investigate this general effect in the specific context of public goods production and find conditions for stochastic selection reversal leading to the success of public good producers. This insight, developed here analytically, is missed by the deterministic analysis as well as by standard game theoretic models that enforce a fixed population size. The effect is found to be amplified by space; in this scenario we find that selection reversal occurs within biologically reasonable parameter regimes for microbial populations. Beyond the public good problem, we formulate a general mathematical framework for models that may exhibit stochastic selection reversal. In this context, we describe a stochastic analog to r−K theory, by which small populations can evolve to higher densities in the absence of disturbance.

Three Layer Game Theoretic Decision Framework for Cyber-Investment and Cyber-Insurance

2017 ◽  
pp. 519-532 ◽  
Author(s):  
Deepak K. Tosh ◽  
Iman Vakilinia ◽  
Sachin Shetty ◽  
Shamik Sengupta ◽  
Charles A. Kamhoua ◽  
...  
Keyword(s):  
Decision Framework ◽  
Cyber Insurance ◽  
Game Theoretic

scholarly journals Static and Dynamic Analysis of Android Malware and Goodware Written with Unity Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jaewoo Shim ◽  
Kyeonghwan Lim ◽  
Seong-je Cho ◽  
Sangchul Han ◽  
Minkyu Park
Keyword(s):  
Mobile Apps ◽  
Android Malware ◽  
Static And Dynamic Analysis ◽  
Android Apps ◽  
Development Framework ◽  
Analysis Technique ◽  
Executable File ◽  
Cross Platform ◽  
Malicious Apps ◽  
Java Code

Unity is the most popular cross-platform development framework to develop games for multiple platforms such as Android, iOS, and Windows Mobile. While Unity developers can easily develop mobile apps for multiple platforms, adversaries can also easily build malicious apps based on the “write once, run anywhere” (WORA) feature. Even though malicious apps were discovered among Android apps written with Unity framework (Unity apps), little research has been done on analysing the malicious apps. We propose static and dynamic reverse engineering techniques for malicious Unity apps. We first inspect the executable file format of a Unity app and present an effective static analysis technique of the Unity app. Then, we also propose a systematic technique to analyse dynamically the Unity app. Using the proposed techniques, the malware analyst can statically and dynamically analyse Java code, native code in C or C ++, and the Mono runtime layer where the C# code is running.

scholarly journals A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 185
Author(s):  
Vasileios Kouliaridis ◽  
Georgios Kambourakis
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Mobile Malware ◽  
Comprehensive Survey ◽  
Mobile Malware Detection

Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.

scholarly journals A game theoretic approach to deciphering the dynamics of amyloid-β aggregation along competing pathways

2019 ◽  
Author(s):  
Preetam Ghosh ◽  
Pratip Rana ◽  
Vijayaraghavan Rangachari ◽  
Jhinuk Saha ◽  
Edward Steen ◽  
...  
Keyword(s):  
Amyloid Β ◽  
Theoretic Model ◽  
Mathematical Framework ◽  
Theoretic Approach ◽  
Formation Pathway ◽  
Heterotypic Interactions ◽  
Game Theoretic ◽  
On And Off Pathways ◽  
And Behavior ◽  
Competing Pathways

AbstractAggregation of amyloidβ(Aβ) peptides is a significant event that underpins Alzheimer disease (AD). Aβaggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD pathogenesis. Therefore, there is increasing interest in understanding their formation and behavior. In this paper, we use our previously established investigations on heterotypic interactions between Aβand fatty acids (FAs) that adopt off-fibril formation pathway under the control ofFAconcentrations, to develop a mathematical framework in defining this complex mechanism. We bring forth the use of novel game theoretic framework based on the principles of Nash equilibria to define and simulate the competing on- and off-pathways of Aβaggregation. Together with detailed simulations and biophysical experiments, our mathematical models define the dynamics involved in the mechanisms of Aβaggregation in the presence ofFAs to adopt multiple pathways. Specifically, our game theoretic model indicates that the emergence of off- or on-pathway aggregates are tightly controlled by a narrow set of rate constant parameters, and one could alter such parameters to populate a particular oligomeric species. These models agree with the detailed simulations and experimental data on usingFAas a heterotypic partner to modulate temporal parameters. Predicting spatiotemporal landscape along competing pathways for a given heterotypic partner such as biological lipids is a first step towards simulating physiological scenarios in which the generation of specific conformeric strains of Aβcould be predicted. Such an approach could be profoundly significant in deciphering the biophysics of amyloid aggregation and oligomer generation, which is ubiquitously observed in many neurodegenerative diseases.

scholarly journals Less is More: A privacy-respecting Android malware classifier using federated learning

2021 ◽  
Vol 2021 (4) ◽  
pp. 96-116
Author(s):  
Rafa Gálvez ◽  
Veelasha Moonsamy ◽  
Claudia Diaz
Keyword(s):  
Security Analysis ◽  
Ground Truth ◽  
Classification Performance ◽  
Android Malware ◽  
Less Is More ◽  
Malware Classification ◽  
Classification Framework ◽  
Cloud Server ◽  
Inference Attacks ◽  
Malicious Apps

Abstract In this paper we present LiM (‘Less is More’), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users’ devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly identify whether an app is malicious). To tackle this, LiM uses a safe semi-supervised ensemble that maximizes classification accuracy with respect to a baseline classifier trained by the service provider (i.e. the cloud). We implement LiM and show that the cloud server has F1 score of 95%, while clients have perfect recall with only 1 false positive in > 100 apps, using a dataset of 25K clean apps and 25K malicious apps, 200 users and 50 rounds of federation. Furthermore, we conduct a security analysis and demonstrate that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server. Further experiments with Ma-MaDroid’s dataset confirm resistance against poisoning attacks and a performance improvement due to the federation.

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