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.

How (In)Consistent is the Court of Justice in the Application of the Market Access Test?”

Invited panel speaker at a Jean Monnet Chair funded research workshop organised by the Europa Institute, School of Law, University of Edinburgh (9 December 2011), http://www.pol.ed.ac.uk/research_themes/index/jean_monnet_centre_of_excellence/principles_of_market_access_workshop

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.

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.

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.

Technology and the architecture of markets: re-configuring the Canadian equity market

This paper examines the relation between technical possibilities, liberal logics, and the concrete reconfiguration of markets. It focuses on the enrolling of innovations in communication and information technologies into the markets traditionally dominated by stock exchanges. With the development of capacities to trade on-screen, the power of incumbent market makers has been challenged as a less stable array of competing quasi-public and private marketplaces emerges. Developing a case study of the Toronto Stock Exchange, I argue that narrative emphasis on the performative power of sociotechnical innovations, the deterritorialisation of financial relations, and the erosion of state capacities needs qualification. A case is made for the importance of developing an understanding of: the spaces of encounter between emerging social technologies and property rights, rules of exchange, and structures of governance; and the interplay of orderings of different institutional composition and spatial reach in the reconfiguration of market architectures. Only then can a better grasp be gained of the evolving dynamics between making markets, the regulatory powers of the state, and their delimitations.

Transmission loss allocation in a deregulated electrical energy market

This paper presents a new method for transmission loss allocation in a deregulated electrical power market. The proposed method is based on physical flow through transmission lines. The contributions of individual loads to the line flows are used as basis for allocating transmission losses to different loads. With minimum assumptions, that sound to be reasonable and cannot be rejected, a novel loss allocation formula is derived. The assumptions made are: a number of currents sharing a transmission line distribute themselves over the cross section in the same manner; that distribution causes the minimum possible power loss. Application of the proposed method is straightforward. It requires only a solved power flow and any simple algorithm for power flow tracing. Both active and reactive powers are considered in the loss allocation procedure. Results of application show the accuracy of the proposed method compared with the commonly used procedures.

A survey of sewer rehabilitation in Malaysia: application of trenchless technologies

This work presents a review of applicable sewer rehabilitation options using trenchless technology in Malaysia. The typical problems faced in wastewater collection systems are analysed and factors that determine the selection method are outlined. This study also highlights the necessary steps to be taken prior to the rehabilitation work. The trenchless technology reviewed here comprises repair, renovation and replacement options. The cost-effectiveness of different rehabilitation methods was identified to assess the economic viability of various options in the Malaysian context. This study reveals that not all the trenchless technologies available in the market are suitable for use in Malaysia, mainly due to incompatibility of the rehabilitation materials used. Furthermore, as trenchless rehabilitation generally involves higher capital outlay than open-cut methods, the choice of rehabilitation method has to be made on a case-to-case basis.

Science for sale in a free market economy: But at what price? ABA and the treatment of autism in Europe.

Educating the public accurately about Applied Behavior Analysis (ABA) is an important undertaking, not least because misconceptions and myths about ABA abound. In this paper we argue that, unfortunately, the efforts of many dedicated professionals and parents to disseminate accurate information about the benefits of ABA for children diagnosed with autism spectrum disorder (ASD) are damaged by a few behavior analysts whose focus seems to be more on monetary gains than social responsibility. We cite examples of the resulting harm to the public image of behavior analysis from a number of European countries. We conclude by calling upon fellow scientists to unite in their opposition to unscrupulous abuses of free market forces for short-term monetary gains that damage the dissemination of the science of behavior analysis and thereby ultimately disadvantage those who should benefit primarily from our science, i.e., some of the most vulnerable citizens of society.

The application of Reporter Gene Assays for the detection of endocrine disruptors in sport supplements.

The increasing availability and use of sports supplements is of concern as highlighted by a number of studies reporting endocrine disruptor contamination in such products. The health food supplement market, including sport supplements, is growing across the Developed World. Therefore, the need to ensure the quality and safety of sport supplements for the consumer is essential. The development and validation of two reporter gene assays coupled with solid phase sample preparation enabling the detection of estrogenic and androgenic constituents in sport supplements is reported. Both assays were shown to be of high sensitivity with the estrogen and androgen reporter gene assays having an EC50 of 0.01 ng mL-1 and 0.16 ng mL-1 respectively. The developed assays were applied in a survey of 63 sport supplements samples obtained across the Island of Ireland with an additional seven reference samples previously investigated using LC–MS/MS. Androgen and estrogen bio-activity was found in 71% of the investigated samples. Bio-activity profiling was further broken down into agonists, partial agonists and antagonists. Supplements (13) with the strongest estrogenic bio-activity were chosen for further investigation. LC–MS/MS analysis of these samples determined the presence of phytoestrogens in seven of them. Supplements (38) with androgen bio-activity were also selected for further investigation. Androgen agonist bio-activity was detected in 12 supplements, antagonistic bio-activity was detected in 16 and partial antagonistic bio-activity was detected in 10. A further group of supplements (7) did not present androgenic bio-activity when tested alone but enhanced the androgenic agonist bio-activity of dihydrotestosterone when combined. The developed assays offer advantages in detection of known, unknown and low-level mixtures of endocrine disruptors over existing analytical screening techniques. For the detection and identification of constituent hormonally active compounds the combination of biological and physio-chemical techniques is optimal.

Combining preference ordering and contingent valuation methods to assess non market benefit of alternative afforestation projects

The application of the contingent valuation method (CVM) in this paper incorporates a prior preference ordering of several alternative future afforestation programmes which could be implemented in Ireland over the next decade. This particular experimental design is thereby shown to reveal the potentially conflicting preferences of different groups within society. These findings are used to devise appropriate CVM scenarios to take account, not only of the efficiency gains of choosing a single policy alternative over others, but also the effects on the distribution of non market benefit between different groups within society, arising from choice between alternatives. (C) 1998 Elsevier Science Ltd. All rights reserved.

Employer occupation of regulatory space of the Employee Information and Consultation (I&C) Directive in liberal market economies

This article shows how both employers and the state have influenced macro-level processes and structures concerning the content and transposition of the European Union (EU) Employee Information and Consultation (I&C) Directive. It argues that the processes of regulation occupied by employers reinforce a voluntarism which marginalizes rather than shares decision-making power with workers. The contribution advances the conceptual lens of ‘regulatory space’ by building on Lukes’ multiple faces of power to better understand how employment regulation is determined across transnational, national and enterprise levels. The research proposes an integrated analytical framework on which ‘occupancy’ of regulatory space can be evaluated in comparative national contexts.