76,101 research outputs found

    A Benefit-Cost Analysis of Korea\u27s Advance Passenger Information System

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    Since the tragic 9/11 terrorist attack on the United States in 2001, many countries have been following the US\u27s decisions on the introduction of an air security program. At the heart of the program is the Advance Passenger Information System or the API system. Considering major developed countries\u27 stances and related international organizations\u27 efforts toward the API system\u27s advance, it seems that the introduction of the API system is expected to be a necessity or a kind of obligation both to border control agencies and air flight companies in the near future. However, this does not mean that the introduction of the API system is not without criticism or controversy. The controversy boils down to the questions of Is it really proven to be effective in increasing air security or Do the expected benefits exceed the total related costs?”. This paper is aimed at trying to answer these questions for the Korean API system. Three major benefits relating to departure, entry and transit management activities will be identified and estimated when possible. As will be shown in detail in the following sections, benefits from consumer convenience make up major share of total calculated benefits. Other values relating to \u27qualitative\u27 benefits are hard to calculate; for this reason, those benefits will not be counted into total numerical values. For the cost side of this analysis, personnel costs and system-related costs like user fees and maintenance costs will be considered. Besides these costs, API system establishment costs could also be counted. However, it seems to be hard to separate the API system establishment costs from all other immigration efficiency systems costs. In addition, the initial establishment costs do not look big on the annual basis, since its introduction was more than ten years ago. Private air carriers should also pay their shares of burden for establishing and operating the system. However, getting access to these private business data is limited and technically making calculation works too complex. Therefore, for balanced analysis, this paper only concerns about costs and benefits in the public sectors. Based upon this analysis, the total net values of the Korean API system reach up to $62,600 in the year of 2015. (The exchange rate-1,200won/dollar- between Korean won and dollar is applied on the basis of March of 2016 when this paper is written)

    Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

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    Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page

    Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

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    Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead
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