A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Smartphone and GPS technology for freeroaming dog population surveillance -a methodological study (Studio metodologico sull’applicazione di smartphone e tecnologia GPS alla sorveglianza delle popolazioni di cani vaganti)

Free-roaming dogs (FRD) represent a potential threat to the quality of life in cities from an ecological, social and public health point of view. One of the most urgent concerns is the role of uncontrolled dogs as reservoirs of infectious diseases transmittable to humans and, above all, rabies. An estimate of the FRD population size and characteristics in a given area is the first step for any relevant intervention programme. Direct count methods are still prominent because of their non-invasive approach, information technologies can support such methods facilitating data collection and allowing for a more efficient data handling. This paper presents a new framework for data collection using a topological algorithm implemented as ArcScript in ESRI® ArcGIS software, which allows for a random selection of the sampling areas. It also supplies a mobile phone application for Android® operating system devices which integrates Global Positioning System (GPS) and Google Maps™. The potential of such a framework was tested in 2 Italian regions. Coupling technological and innovative solutions associated with common counting methods facilitate data collection and transcription. It also paves the way to future applications, which could support dog population management systems.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Examining the Neurocognitive Validity of Commercially Available, Smartphone-Based Puzzle Games

Cognitive assessment typically involves assessing a person’s cognitive performance in unfamiliar and ar-guably unnatural clinical surroundings. User-centred approaches to assessment and monitoring, driven by issues such as enjoyability and familiarity, are largely absent. Everyday technologies, for example, smartphones represent an opportunity to obtain an objective assessment of a person’s cognitive capabili-ties in a non-threatening, discreet and familiar way, e.g. by everyday puzzle games undertaken as a leisure activity at home. We examined the strength of relationships that exist between performance on common puzzle games and standard measures of neuropsychological performance. Twenty-nine participants, aged 50 - 65 years, completed a comprehensive neuropsychological test battery and played three smart-phone-based puzzle games in triplicate: a picture puzzle [Matches Plus], a word puzzle [Jumbline] and a number puzzle [Sudoku]. As anticipated, a priori, significant correlations were observed between scores on a picture puzzle and visual memory test (r = 0.49; p = 0.007); a word puzzle and estimated verbal IQ (r = 0.53; p = 0.003) and verbal learning (r = 0.30; p = 0.039) tests; and a number puzzle and reason-ing/problem solving test (r = 0.42; p = 0.023). Further analyses making allowance for multiple compari-sons identified a significant unanticipated correlation (r = 0.49; p = 0.007) between number puzzle scores and a measure of nonverbal working memory. Performance on these smartphone-based games was in-dicative of relative cognitive ability across several cognitive domains at a fixed time point. Smart-phone-based, everyday puzzle games may offer a valid, portable measure of assessing and monitoring cognition in older adults.

La governance degli enti locali sulle aziende di gestione dei servizi pubblici. Strumenti di programmazione e controllo in ottica di gruppo

Lo scopo del presente lavoro è delineare un nuovo modello inerente l'organizzazione, i processi e gli strumenti di programmazione e controllo a supporto della governance degli enti locali sulle loro aziende di gestione dei servizi pubblici, con particolare attenzione per la variabile strumentale. E' stata adottata una metodologia mista, deduttivo-induttiva. Nella fase deduttiva è stata analizzata la normativa italiana nonché la dottrina economico aziendale nazionale ed internazionale in tema di gestione dei servizi pubblici locali: in tal modo è stato estrapolato un modello normativo-dottrinale inerente l'organizzazione, i processi e gli strumenti di programmazione e controllo a supporto della governance degli enti locali sulle loro aziende di gestione dei servizi pubblici. Nella fase induttiva è stata realizzata un'indagine empirica che ha coinvolto i comuni capoluogo di Emilia-Romagna e Toscana, in modo tale da testare il livello di utilizzo del modello normativo-dottrinale precedentemente estrapolato Nella fase di feedback sono stati delineati i punti di forza e di debolezza del succitato modello emergenti dalla ricerca. Si è così cercato di proporre un nuovo modello, con particolare attenzione per la variabile strumentale, in grado di porre rimedio ai punti di debolezza e di potenziare i punti di forza del modello normativo-dottrinale.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Belfast Soundwalks: Experiences in Sound and Place through Locative Media

This article outlines the ongoing development of a locative smartphone app for iPhone and Android phones entitled The Belfast Soundwalks Project. Drawing upon a method known as soundwalking, the aim of this app is to engage the public in sonic art through the creation of up to ten soundwalks within the city of Belfast. This paper discusses the use of GPS enabled mobile devices in the creation of soundwalks in other cities. The authors identify various strategies for articulating an experience of listening in place as mediated by mobile technologies. The project aims to provide a platform for multiple artists to develop site-specific sound works which highlight the relationship between sound, place and community. The development of the app and the app interface are discussed, as are the methods employed to test and evaluate the project.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Chronic smartphone gaming consumption and positive coping practice

Purpose - Chronic consumption practice has been greatly accelerated by mobile, interactive and smartphone gaming technology devices. This study explores how chronic consumption of smartphone gaming produces positive coping practice. Design/methodology/approach - Underpinned by cognitive framing theory, empirical insights from eleven focus groups (n=62) reveal how smartphone gaming enhances positive coping amongst gamers and non-gamers. Findings - The findings reveal how the chronic consumption of games allows technology to act with privileged agency that resolves tensions between individuals and collectives. Consumption narratives of smartphone games, even when play is limited, lead to the identification of three cognitive frames through which positive coping processes operate: (a) the market generated frame, (b) the social being frame, and (c) the citizen frame. Research limitations/implications – This paper adds to previous research by providing an understanding of positive coping practice in the smartphone chronic gaming consumption. Originality/value - In smartphone chronic gaming consumption, cognitive frames enable positive coping by fostering appraisal capacities in which individuals confront, hegemony, culture and alterity-morality concerns. 

Towards automated Android app collusion detection

Android OS supports multiple communication methods between apps. This opens the possibility to carry out threats in a collaborative fashion, c.f. the Soundcomber example from 2011. In this paper we provide a concise definition of collusion and report on a number of automated detection approaches, developed in co-operation with Intel Security.

N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.