Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.

Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communi-cations. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features.

The wide popularity of Android systems has been accompanied by increase in the number of malware targeting these systems. This is largely due to the open nature of the Android framework that facilitates the incorporation of third-party applications running on top of any Android device. Inter-process communication is one of the most notable features of the Android framework as it allows the reuse of components across process boundaries. This mechanism is used as gateway to access different sensitive services in the Android framework. In the Android platform, this communication system is usually driven by a late runtime binding messaging object known as Intent. In this paper, we evaluate the effectiveness of Android Intents (explicit and implicit) as a distinguishing feature for identifying malicious applications . We show that Intents are semantically rich features that are able to encode the intentions of malware when compared to other well-studied features such as permissions. We also argue that this type of feature is not the ultimate solution. It should be used in conjunction with other known features. We conducted experiments using a dataset containing 7406 applications that comprise 1846 clean and 5560 infected applications. The results show detection rate of 91% using Android Intent against 83% using Android permission. Additionally, experiment on combination of both features results in detection rate of 95.5%.

Abstract Cloud computing represents the most recent enterprise trend in information technology and refers to the virtualization of computing resources that are available on demand. Cloud computing saves cost and time for businesses. Moreover, this computing process reflects a radical technological revolution in how companies develop, deploy, and manage enterprise applications over the Internet. Virtualized cloud computing mainly offers cloud‐computing delivery models such as software as a service, platform as a service, and infrastructure as a service. Security and privacy are presently considered critical factors in the adaptation of any cloud‐service delivery model. Cloud computing leverages several technologies; in the process, this model can inherit potential security threats. Thus far, security issues in cloud computing have rarely been addressed at the service delivery level. Key security concerns include Web application security, network security, data security, integration, vulnerabilities in the virtualized environment, and physical security. The aim of this research is to comprehensively present the security threats with respect to their cloud service deliver models. This study also determines how service delivery models differ from existing enterprise applications, classify these models, and investigate the inherent security challenges. This study primarily focuses on the security concerns of each layer of the cloud‐service delivery model, as well as on existing solutions and approaches. In addition, countermeasures to potential security threats are also presented for each cloud model. Copyright © 2016 John Wiley & Sons, Ltd.

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.

ABSTRACT Intrusion response systems aim to provide a systematic procedure to respond to incidents. However, with different type of response options, an automatic response system is designed to select appropriate response options automatically in order to act fast to respond to only true and critical incidents as well as minimise their impact. In addition, incidents also can be prioritised into different level of priority where some incidents may cause a serious impact (i.e. high priority) and other may not (i.e. low priority). The existing strategies inherit some limitation such as using complex approaches and less efficient in mapping appropriate response based upon incidents' priority. Therefore, this study introduces a model called response strategy model to address the aforementioned limitation. In order to validate, it was evaluated using two datasets: DARPA 2000 and private dataset. The case study results have shown a significant relationship between the incident classification and incident priorities where false incidents are likely to be categorised as low priority and true incidents are likely to be categorised as the high priority. In particular, with response strategy model, an average of 92.68% of the false incidents was prioritised as the lowest priority is better compared with only 67.07% with Snort priority. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT The landscape of security threats continues to evolve, with attacks becoming more serious and the number of vulnerabilities rising. For these threats to be managed, many security studies have been undertaken in recent years, mainly focusing on improving detection, prevention and response efficiency. This paper proposes an incident prioritisation model, the Risk Index Model (RIM), which is based on risk assessment and the analytic hierarchy process. For incidents to be prioritised, the model uses indicators, such as criticality, as decision factors to calculate incidents' risk index. The model also adopts different strategies to enhance the prioritisation process. To evaluate the model, two stages of evaluation study were conducted. The first stage aims to validate the model by comparing its results with the Common Vulnerability Scoring System and Snort. The second stage aims to enhance RIM by analysing the effect of using different strategies in the model. The experimental results in the first stage have shown that 100% of incidents could be rated with RIM, compared with only 17.23% with the Common Vulnerability Scoring System. The experiments in the second stage have shown significant changes in the resultant risk index as well as some of the top‐priority incidents. Copyright © 2012 John Wiley & Sons, Ltd.