This research paper presents MLDroid—a web-based framework—which helps to detect malware from Android devices. Due to increase in the popularity of Android devices, malware developers develop malware ...on daily basis to threaten the system integrity and user’s privacy. The proposed framework detects malware from Android apps by performing its dynamic analysis. To detect malware from real-world apps, we trained our proposed framework by selecting features which are gained by implementing feature selection approaches. Further, these selected features help to build a model by considering different machine learning algorithms. Experiment was performed on 5,00,000 plus Android apps. Empirical result reveals that model developed by considering all the four distinct machine learning algorithms parallelly (i.e., deep learning algorithm, farthest first clustering, Y-MLP and nonlinear ensemble decision tree forest approach) and rough set analysis as a feature subset selection algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.
With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can ...steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature.
With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has ...become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time of their installation. In this study, we consider permission and API calls as features that help in developing a model for malware detection. To select appropriate features or feature sets from thirty different categories of Android apps, we implemented ten distinct feature selection approaches. With the help of selected feature sets we developed distinct models by using five different unsupervised machine learning algorithms. We conduct an experiment on 5,00,000 distinct Android apps which belongs to thirty distinct categories. Empirical results reveals that the model build by considering rough set analysis as a feature selection approach, and farthest first as a machine learning algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.
Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ...ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve the performance of the malware detection models. The proposed malware detection framework is then validated by considering two distinct performance parameters, i.e., accuracy and F-measure as a benchmark to detect malware from real-world apps. We performed an empirical study on thirty different categories of Android apps. The experimental data set consists of 1,94,659 benign apps and 67,538 malware apps that are collected from different promised repositories. Empirical results reveal that the models developed by using the proposed feature selection framework are able to detect more malware-infected apps when compared to all extracted feature sets. Moreover, the malware detection model build by using nonlinear ensemble decision tree forest (NDTF) approach is achieved a detection rate of 98.8%. In addition to that, the proposed malware detection framework is more effective in detecting malware-infected apps as compared to different anti-virus scanners and different frameworks or approaches developed in the literature.
Cloud computing is a computer science paradigm that has grown significantly in recent years. It provides on-demand access to a diverse set of software, infrastructure and platform services through ...the internet. However, due to their diversity and functional similarities, selecting trustworthy cloud services is a challenge. The absence of adequate trust evaluation methods for cloud services has hampered the widespread adoption of cloud computing. To address this issue and assist customers in selecting trustworthy cloud services, this paper presents a framework Selection of Trustworthy Cloud Services (SelTCS). SelTCS selects services by combining objective and subjective trust. A novel objective trust assessment approach has been presented that prioritizes quality-of-service attributes according to user preferences. Also, a novel subjective trust assessment approach is proposed which evaluates trust as a combination of reputation assessment based on aggregated user feedback that employs a modified hypertext induced topic search-based algorithm for identifying and removing malicious users, and direct trust based on users’ own experiences gained through direct interactions. Experiments using the Quality of Web Services (QWS) version 1.0 and Epinions datasets reveal that SelTCS greatly enhances the accuracy of trust evaluation and is more effective than existing approaches at detecting malicious user ratings.
Android has gained its popularity due to its open-source and number of freely available apps in its official play store. Appropriate functioning of Android apps depends upon the permission or set of ...permissions which an app demands at the time of installation and run-time. By taking the advantage of these permissions or set of permissions, cybercriminals are developing malware-infected apps daily. In this study, we proposed a framework named as “SOMDROID”, that work on the principle of unsupervised machine learning algorithm. To develop an effective and efficient Android malware detection model, we collect 5,00,000 distinct Android apps from promised repositories and extract 1844 unique features. Further, to select significant features or feature sets, we applied six different feature ranking approaches in this study. With the selected feature or feature sets, we implement the Self-Organizing Map (SOM) algorithm of Kohonen and measure four distinct performance parameters, i.e., Intra-cluster distance, Inter-cluster distance, Accuracy and F-measure. Empirical result reveals that our proposed framework is able to detect 98.7% malware that belongs to unknown families and in addition to that the detection rate is higher by 2% when compared to commercial anti-virus scanners and frameworks proposed in the literature.
This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm ...(SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.
Software refactoring is a commonly accepted means of improving the software quality without affecting its observable behaviour. It has gained significant attention from both academia and software ...industry. Therefore, numerous approaches have been proposed to automate refactoring that consider software quality maximization as their prime objective. However, this objective is not enough to generate good and efficient refactoring sequences as refactoring also involves several other uncertainties related to smell severity, history of applied refactoring activities and class severity. To address these concerns, we propose a multi-objective optimization technique to generate refactoring solutions that maximize the (1) software quality, (2) use of smell severity and (3) consistency with class importance. To this end, we provide a brief review on multi-objective search-based software refactoring and use a multi-objective spotted hyena optimizer (MOSHO) to obtain the best compromise between these three objectives. The proposed approach is evaluated on five open source datasets and its performance is compared with five different well-known state-of-the-art meta-heuristic and non-meta-heuristic approaches. The experimental results exhibit that the refactoring solutions provided by MOSHO are significantly better than other algorithms when class importance and code smell severity scores are used.
•RQ1: What are the various indicators for successful SPI in software SMEs? Can these indicators be grouped under various categories, so-called enablers?•RQ2: What is the hierarchical association ...among these enablers?•RQ3: What is the influencing dynamics, i.e., the degree of dependence among various SPI enablers?
In the present study authors developed a mixed approach based on qualitative and interpretive approaches to model and analyze software process improvement (SPI) enablers in software small and medium-sized enterprises (SMEs). In phase-I after listing the research questions, the data is synthesized using the observations of stakeholders in their work domain followed by semi-structured interviews. The results of the qualitative analysis, as key learning's formed the basis for grouping of SPI indicators into nine key SPI enablers. Open, focused and theoretical coding procedures in grounded theory were used to create categories, subcategories, and thematic relationships among these enablers. Further, in phase-II, the hierarchical structure model of SPI enablers is build using the interpretive structural modeling (ISM) approach and influencing dynamics among the enablers is analyzed using MICMAC analysis. From the results of the study, it is concluded that organization, people, and technology issues are important drivers, on which SMEs shall proactively focus for successful implementation of SPI initiatives.
In the present computer era, the vulnerabilities inherent in the Internet architecture enable various kinds of attacks. Distributed Denial of Service (DDoS) is one of such prominent attack that is a ...lethal threat to Internet domain that harnesses its computing and communication resources. The increase in network traffic rates of legitimate traffic and its flow similarity with attack traffic has made the DDoS detection very difficult despite deployment of diversified defense solutions. The ISPs are bound to invest heavily to counter such problems which has a significant impact on company finances. To provide uninterrupted quality services to the end users, ISPs needs to deploy a distributed solution for timely detection and discrimination of attack and behaviorally similar flash events (FE) traffic. Such distributed defense systems can be deployed at source-end, intermediate network-end or at the victim-end location. Since the volume of traffic to be analyzed is very large, the detection accuracy and low computational complexity of the proposed defense solution is always a challenging problem. This paper proposes an ISP level distributed, collaborative and automated (D-CAD) defense system for detecting DDoS attacks and FEs, and has the capability to effectively distinguishing the two. Additionally, D-CAD defense system is also capable of categorizing FE traffic and has low computational complexity. The proposed system is validated in novel software defined networks (SDN) using Mininet emulator. The results show that D-CAD defense system outperformed its existing counterparts on various detection system evaluation metrics.
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