Intelligent Automation & Soft Computing DOI:10.32604/iasc.2023.024469 | [picture]
Article |

Optimal Unification of Static and Dynamic Features for Smartphone Security Analysis

Sumit Kumar¹,*, S. Indu² and Gurjit Singh Walia¹

1 Scientific Analysis Group, DRDO, Delhi, 110054, India
2 Delhi Technological University, Delhi, 110042, India
Corresponding Author: Sumit Kumar. Email: sumitkr@hotmail.com
Received: 18 October 2021; Accepted: 14 December 2021

Abstract: Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification model to distinguish benign and malicious applications. The suggested framework is extensively experimentally validated through both qualitative and quantitative analysis and results are compared with the existing solutions. Performance evaluation over benchmarked datasets from Google Play Store, Drebin, Androzoo, AMD, and CICMalDroid2020 revealed that the suggested solution outperforms state-of-the-art methods. We achieve average detection accuracy of 98.62% and F1 Score of 0.9916.

Keywords: Fusion; smartphone; android; security analysis; malware detection

1  Introduction

Smartphones are deeply rooted in the digital market due to their potential applications in 4G and 5G-based wireless networks. Android upheld its status as a leading smartphone OS universally in September 2020, with 85% of the total market share [1]. The profound growth of mobile technology brings significant measures to be incorporated in the mobile security landscape. Also, sum of existing apps in the Google Play repository has been increased to 3.047 million [2]. However, this deluge of mobile apps attracted the malice writers to infuse malwares in these apps for nefarious deeds and the number of new android malwares are also growing as 482579 [3] malware samples per month. Android malicious applications (malapps) proliferate due to the easiness of installing fresh apps from third-party [4] sources. Amongst different mobile OS, Android is the most widespread platform because of its open architecture. Unluckily, android based smartphones have progressively turned into the key target of the attackers, thereby enforcing urgency for mobile app security. On the other hand, Linux based OS like SailfishOS, PostmarketOS, Ubuntu Touch, Mobian, LuneOS etc. are also vulnerable to malwares. But due to their limited presence, the attacks are also limited.

Abundant literature is available on static and dynamic analysis to detect malapps and other unintended functionalities in Android apps. Generally, malign (M) apps are camouflaged as benign (B) apps causing system impairment, financial damage, and information seepage. Malign apps can also form mobile botnets. Numerous investigation mechanism has been suggested to identify malapps. The detection mechanism can be broadly characterized into static and dynamic analysis. Static analysis analyzes code and the manifest.xml file of the app without executing them [5]. However, in dynamic analysis, apps are executed and the run-time activities of the apps are analyzed for building solutions. Mostly, static-analysis is thwarted by code-obfuscation and code-polymorphism resulting in variations of malware to escape detections. Whereas, dynamic analysis is favorable for analyzing these types of obfuscated apps.

To build a solution to address these issues, static and dynamic features are exploited by the various machine learning(ML) algo to detect the android malwares [6]. The static features mostly used are permissions, app components, intents, API, network address, opcode, hardware component, call flow graph, static taint analysis, dataflow, file property, system command, and native code [7]. On the other hand, the dynamic features frequently used are system calls, API calls, network traffic characteristics, and battery features [8]. In hybrid analysis, both static and dynamic features are exploited for malicious app detection [9]. We propose a hybrid solution that combines both static and dynamic analysis to overcome the limitations of static and dynamic analysis.

In brief, the key contributions of our paper are described as below:

1. First, we put forward a unique approach for optimal unification of static and dynamic features resulting in Unified feature (UF) for smartphone security analysis by cross diffusion technique.

2. Second, UF is fed to two ML classifiers to detect the android malapps. Results of these classifier’s scores were combined by fuzzy based fusion approach for improving the performance.

3. Lastly, we provided a comprehensive study founded on benchmarked databases and compare the results with contemporary techniques to validate the efficacy of the suggested framework.

The rest of this manuscript is organized as follows. Section 2 covers the android malapp detection-related work. Section 3 explicates core design of our suggested unified framework for malapp detection. Section 4 covers details of the datasets and experimental scheme. Section 5 covers the experimental results of suggested model by qualitative and quantitative methods. In Section 6, we discuss about the suggested framework and its comparison with other state-of-the-arts methods. Section 7 covers some limitations of the proposed methodology. Finally, under Section 8, concluding comments together with future directions for the suggested work are emphasized.

2  Related Work

Smartphone security analysis has been extensively studied in the literature wherein ML based approaches have been surveyed in reported work [10–12]. Also, hybrid techniques exploiting both static and dynamic features for malicious app detection can address the major limitations of static and dynamic analysis. Surveys [13–15] available on static and dynamic analysis techniques also conclude that hybrid technique comprising both static and dynamic features is the better alternative to static and dynamic analysis methods.

In this section, to measure the research gap in the latest work, we have reviewed the work in the research papers based on the hybrid security analysis techniques with ML algorithms and without ML algorithms.

2.1 Hybrid Security Analysis Techniques without ML Algorithms

The authors in [16] presented a hybrid model DAMBA based on client/server architecture. It can detect the android malapps by constructing the directed graphs depicting the object reference information with 96.9% detection accuracy and a detection time of approximately 5 seconds. Hybrid analysis approach “mad4a” offered by the authors [17] for android malapp detection leveraging advantages of both the dynamic and static analysis methods. In this, authors pointed out some undervalued characteristics of android malapp that can further help the investigators to augment their knowledge for detecting android malapps. In this, API calls and network logs were used as static and dynamic features respectively.

Manzanares et al. offered a novel hybrid analysis method “KronoDroid” [18] that addresses the time and data platform source. Here, 489 static and dynamic features were used for generating hybrid dataset with labelled timestamps on each sample to detect the malapps with high accuracy. A hybrid method [19] “DirectDroid”, that merges fuzzing and a novel app analysis procedure “on-demand forced execution” to activate concealed malicious behaviour. It can effectively detect malapps by the “augmenting fuzzing” technique. This method could not cater to obfuscated codes in the malapps. In sum, hybrid security analysis techniques without ML are limitedly applied for malapp detection.

2.2 Hybrid Security Analysis Techniques with ML Algorithms

In [20], a multi-level hybrid model SAMADroid for effective android based malapp detection was proposed. Static analysis results were determined on the remote host. Smartphone was used for the dynamic analysis. Machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes(NB), and Decision Trees (DT) were used to train the model to detect malapps with 98.5% of TPR. In [21], a hybrid model using API calls and permissions was suggested. SVM and RF classifiers were further leveraged to achieve a true positive rate of up to 89%. Authors in [22] presented a model for android malapp detection with intents, permission, and API calls as features and compare the results with four ML classifiers viz. RF, NB, Gradient Boosting(GB), and DT. Accuracy of 96% and TP rate (TPR) of 0.85 were achieved with GB classifier. In [23], authors exploited static and dynamic feature vectors to detect malapps with 89.7% accuracy with a voting classifier-based fusion approach.

A hybrid model [24] implements dynamic analysis on the outcomes of static investigations. API calls and permissions were used as static features, while system calls were used as dynamic features to identify the malapps with an accuracy of 94.6%. Improved Bayesian classifier was used in static analysis and ensemble of three classifiers viz. RF, GC Forest, and XG boost were used for dynamic analysis. A Hybrid method “MADAM” [25] was proposed to detect the malapps on a rooted device by extracting static and dynamic features. A feature vector was formed and input was given to K-NN classifier to obtain an accuracy of 96.9%. Detection accuracy of 94.7% was achieved.

Dhalaria et al. [26] proposed the hybrid framework by combining the static and dynamic features for the classification of malapp and family it belong and 90.10% to by creating the two feature oriented datasets. This method attains accuracies of 98.53% for malapp detection and 90.10% for its family classification. Kabakus et al. [27]presented a hybrid analysis method for detecting malapps with the maximum detection accuracy of 99.5% when using J48 ML algorithm.

Karim et al. [28] proposed a hybrid android smartphone botnet detection platform by exploiting the API calls, permissions as static features and network traffic-based dynamic features for detecting the botnets with high detection accuracy of 98% with RF algo. Ding et al. [29] presented a ResLSTM based hybrid model using static features and traffic based dynamic features to achieve the detection accuracy of 99%. The subsequent section elaborates on the proposed android malapp detection framework. In sum, ML based smartphone security analysis has shown promising results and were in use. But optimal combining of static and dynamic features were limitedly addressed.

3  Core Design of Proposed Framework

In this paper, a unified feature resulting from optimal combination of static and dynamic features followed by fuzzy-based score fusion model for smartphone security analysis is proposed. The outline of the suggested hybrid analysis framework is described in Fig. 1. Framework basically comprised of four building blocks namely, feature extraction (static and dynamic vector formation), feature fusion, classifier fusion, and eventually a decision block for effective malapp detection. Extracted dynamic features and static features are converted into dynamic and static feature vectors. Each dynamic and static feature vector is used for producing similarity graphs using the cosine similarity. Similarity graphs are further subjected to normalization so as to produce the normalized graphs. Using the reference curves for dynamic and static feature vectors, we obtain refined graph for dynamic and static feature vectors. The obtained dynamic and static normalized and refined graphs are further cross diffused to produce a diffused graph corresponding to the dynamic and static feature vectors. The diffused graphs of dynamic and static feature vectors are fused to generate a unified feature which is extremely discriminatory. This discriminative unified feature is given to two ML classifiers so as to classify a test app into Benign (B) or Malicious (M).

Figure 1: Proposed hybrid analysis framework for smartphone

Our methodology exploits two classifiers in parallel whose scores are fused using fuzzy-based fusion technique to enhance the overall performance. Lastly, the final score wfused in the decision model is matched with the threshold, wthr and test app is categorized into B if wfused ≥ wthr or M otherwise.

The description of the suggested framework is as follows:

3.1 Feature Fusion

Feature fusion block comprises static and dynamic feature vector generation after extracting the five static features and two dynamic features. In the proposed model, feature fusion is basically the concatenation of diffused graphs corresponding to static and dynamic feature vectors obtained by cross-diffusion process of normalized and refined graphs.

3.1.1 Feature Extraction

Five static and two dynamic features were extracted for a given android test app t together with N android apps from ref. repository, r∈{R+,R−},R+ and R− relates to B and M app respectively. The feature extraction process has been illustrated in Fig. 2. Apps in the ref. repository are updated so as to include the latest apps to improve the suggested framework detection ability. Here, extraction of static-based features is done using the APK and Baksmali tool. APK tool converts the app into classes.dex and manifest.xml files. Classes.dex files are further subjected to baksmali tool to convert it into smali file. Static-API calls are extracted from smali file. The rest of the static features permissions, hardware features, app components, and intents are extracted from the manifest.xml file. For dynamic features, the system runs the app on a sandbox environment using an Android emulator. The dynamic API calls and the system calls were extracted from the system log files.

Figure 2: Static and dynamic feature extraction process

Static Features

API: We have selected a1 number of API’s whose sum of frequency is the feature value. API-linked static-feature vector of test app (t) is computed as follows:

FASt=∑i=1a1f(Ai)

where, function f(Ai) computes the frequency of API, Ai . Likewise, API linked static feature vector FASr for repository apps are extracted for r∈{R+,R−} .

Similarly, pairs { FPert=∑i=1p1f(Pi) , FPerr }, { FIntt=∑i=1i1f(Ii) , FIntr }, { FCompt=f(App_Component) , FCompr } and { FHardr=f(Hardware_Feature) , FHardr } corresponding to Requested Permissions, Intents Filters, APP Component and Hardware Feature were computed.

Dynamic Features

System calls: System call-linked dynamic feature vector of test app t is computed as follows:

FSyst=f(System_Calls)

where f(System_Calls) is a function that calculates total count of system calls resulted by executing the app. Likewise, system calls-linked dynamic feature FSysr for the repository apps are extracted for r∈{R+,R−} .

Similarly, pair { FAdt=f(API_Calls) , FAdr } for API calls was computed.

From the above seven feature-descriptors, we form two static and dynamic feature vectors as follows in Eqs. (1) and (2) respectively:

FStatic={FAS,FPer,FInt,Fcomp,FHard) (1)

FDynamic={FSys,FAd) (2)

In short, we have built seven feature-descriptor as stated for every app. In feature fusion, features vectors corresponding to test and repository apps are used for creating non-linear graph. In the generated graph, feature-vectors corresponding to test apps and repository apps acts as nodes. Subsequently, graphs are created for each test app t corresponding to static feature vector and dynamic feature vector.

For feature vectors, FStatict and FDynamict of test app t corresponding to static and dynamic features, we construct graphs Gϕ=(Vϕ,Edϕ,wϕ) , where ϕ∈{Static,Dynamic} , wϕ are edge weights that act as the similarity between feature-vectors of apps t and r where r∈{R+,R−} , Vϕ corresponds to the nodes of the generated similarity graphs, Edϕ corresponds to the edges of the similarity graphs that portray the association between the test app and the repository apps. In the proposed framework, similarity matrices Gϕ∈RN×N are constructed by calculating the cosine similarity between the static and dynamic feature vectors of the test app and repository apps, wherein N=r+1 . For feature pair values (Fϕt,Fϕr) corresponding to t and r apps, where ϕ corresponds to static and dynamic feature vector, the edge weights are denoted by similarity vector wϕ(t,r) , and is calculated by the cosine similarity between the pair (Fϕt,Fϕr) using Eq. (3)

wϕ(t,r)=Fϕt∗Fϕr‖Fϕt‖‖Fϕr‖ (3)

Constructed graphs are further fused to obtain unified feature. Feature unification follows in the coming subsection.

3.1.2 Feature Unification

Constructed static and dynamic feature vectors are fused in a way to extract complementary information embedded in them. This is achieved by the means of the suggested optimal non-linear cross-diffusion of generated refined and normalized graphs to create a distinct borderline between the B and M apps. To unify the multiple features graph-oriented cross diffusion method was presented by [30]. The results validate that the feature fusion via non-linear graph-based technique is better than linear graph-based approaches. Graph-based unification maintains a robust depiction of the apps and discards all the feeble features that contribute to undesirable classification errors.

Similarity graph created using Eq. (3) for the static and dynamic feature vectors are again normalized by means of “min-max normalization” to obtain the normalized graphs G¯ϕ whose edge weights are calculated as w¯ϕ(r) using Eq. (4)

w¯ϕ(t,r)=wϕ(t,r)∗min(wϕ(t,r))max(wϕ(t,r))−min(wϕ(t,r)) (4)

Normalized graphs G¯ϕ are employed to obtain the refined graphs Rϕ , for static features and dynamic features vectors. A refined graph is generated to attain highly distinctive attributes. Normalized attributes are initially deducted out of a generated reference curve to make an estimated graph. There are N normalized weights corresponding to apps. The ideal plot of normalized feature characterized as weight vector w¯ϕ(t,r) can be represented as:

w¯ϕ(t,r)={1,ift=r0,otherwise (5)

Static feature vectors when plotted appear as a curve, where a self-match appeared as a peak and the rest tends to the horizontal line. The more the match score tends to zero, the more dissimilar is the feature value with other apps. Capitalizing this, a curve Reϕ representing reference score values is calculated using the training apps, and deviance from Reϕ is used to attain adaptability. Reϕ∈RN×1 is produced by taking mean of the normalized attributes when evaluated over training apps:

Reϕ=∑i=1Narrange[w¯ϕ(t∼,i)]N (6)

where t∼ one of training apps of set N and “arrange” is a function used to arrange values in increasing order. This reference curve gives the estimation of training app attributes.

G¯ϕ is used to form an estimated graph for test apps, where estimated test app feature components are calculated by deducting w¯ϕ(l) from Reϕ as follows:

wϕe(l)={w¯ϕ(l),l<m|w¯ϕ(l)−Reϕ(l)|,m≤l≤N (7)

where, wϕe∈RN×1 denotes estimated test app attribute and the variable m segregate the dimension of a feature vector. This method helps in generating the highly discriminative test app features leading to the detection of the malapps with high efficiency. Estimated features wϕe(l) are plotted to determine the test app’s estimated feature weights. Significant area under curve (SA) of the estimated feature is determined and its weight eϕ is calculated using Eq.(8).

eϕ=N×[1(SAϕ)t∑i=1N1(SAϕ)i] (8)

where, (SAϕ)i and (SAϕ)t are the SA of the ith app used for training and of the test app t in the estimated graph respectively. This area signifies feature efficacy.

To generate a Refined Graph Rϕt , its edge weights wϕT(l) are calculated by reorganizing wϕe(l) by means of Eq. (9). Rϕt resulted in the robust connections among vertices of graph and all the feeble connections are significantly reduced.

wϕr(l)=(wϕe(l))αeϕ (9)

where α is pre-estimated constant used to achieve adaptiveness.

Normalized and refined graphs for static features and dynamic features vectors are further exploited in the cross diffusion process.

Static features and dynamic features vectors possess distinctive and complementary information for the segregation of apps into B or M. Therefore, the cross-diffusion of static feature vector normalized graph and the dynamic feature vector refined graph and vice-versa results in boosting up of the robust connections along with filtering out the weaker connections leading to better accuracy.

In the proposed framework, G¯ϕ and Rϕ are fused via non-linear cross diffusion scheme resulting in the fused graphs Dϕ with edge-weights calculated using Eq. (10)

wϕdiffused=w¯ϕ∗(2F∗∑j=1,j≠ϕF(wjr))∗(w¯ϕ)T (10)

Where F is the total number of feature vectors and T above represents transposition. In our framework, F = 2 as we have taken only two feature vectors i.e. static and dynamic feature vector

The diffused edge weights wϕdiffused are further used to form a unified feature vector U by means of Eq. (11).

U(t)=1βF∗(∑j=12(wjdiffused)) (11)

Where j=1 corresponds to static feature vector, j=2 corresponds to dynamic feature vector and β is the pre-estimated constant used to achieve adaptiveness. Details of the classifier fusion follows next.

3.2 Classifier Fusion

Vector U is given to the two classifiers in parallel. Classification scores obtained are again fused. To achieve this, the U is inputted to two ML classifiers viz. SVM and RF. Their respective classification scores Ss (SVM) and Sr (RF) are determined. In the proposed framework, the obtained classifier(s) scores are optimally fused using the fuzzy-based score fusion method to improve the segregation of apps. Fuzzy fusion is basically combining scores of two ML algorithms in a natural way to determine valuable info and also to boost the performances of the individual algorithm. In the suggested method, the fuzzy logic conditions are formulated by a group of twenty-five fuzzy rules as stated in Tab. 1 under Section 3.2. The classifiers scores are combined in a way so as to boost the concurrent classifier scores and to suppress the discordant classifier scores. Proposed fusion model attains a precise decision boundary between B and M apps. Here, we have defined the fuzzy set as signifying very large, large, medium, small, very small values of the classifier’s score. Membership value for the classification score is calculated as elements of a fuzzy set by means of Eq. (12–16)

ΨVL(x)=11+e−mVL(x−fVL) (12)

ΨL(x)=e−(x−nL)2fL2 (13)

ΨM(x)=e−(x−nM)2fM2 (14)

ΨS(x)=e−(x−nS)2fS2 (15)

ΨVS(x)=11+e−mVS(x−fVS) (16)

where mVL=36 , fVL=0.84 , fL=0.13 , nL=0.7 , fM=0.13 , nM=0.5 , fS=0.12 , nS=0.3 , fVS=0.14 , mVS=−36 are linguistic variable values attained from the training phase and x∈{Sr,Ss} .These values are chosen so that concordant classifiers scores are boosted and discordant classifier scores are suppressed concurrently. The functional mapping Tu,v between the RF and SVM classifiers scores are tabulated in Tab. 1, where u and v are fuzzy set values allocated to each evaluated score. This mapping guarantees an accurate decision boundary-line for segregating the malapps.

Fuzzy fused output is further converted to the optimal crisp value using the center of gravity (COG) technique for defuzzification. Crisp variable value using COG for a pair of elements u and v in the fuzzy set is calculated using Eq. (17)

COGTu,v=∑u∑vΨTu,v(x)x∑u∑vΨTu,v(x) (17)

where value of Tu,v is taken from Tab. 1 and x∈{Sr,Ss} . The weighted mean of the COG values over the pair of elements u , v is used for the estimation of the final fused weight using Eq. (18).

wfused=∑u∑vΩu,v∗COGTu,v∑u∑vΩu,v(Sr,Ss) (18)

where Ωu,v is fuzzy control rule calculated using Eq.(19)

Ωu,v=min(Ψp(Sr),Ψq(Ss)) (19)

The wfused is compared with the wthr to determine whether a given app is M or B depending on whether wfused<wthr or vice-versa ( wfused>wthr ). Datasets and experimental design follows in the subsequent subsection.

4  Datasets and Experimental Design

The balanced and unbalanced [31] datasets can be used for experimentation purpose. Here, we have used balanced datasets for experimentation. B apps are taken from CICMalDroid2020 Dataset and Google Play Store and M apps are collected from CICMalDroid2020 [32], AMD [33], Androzoo [34], and Drebin [35] covering the multitude of malwares from different families. 2000 B and 2000 M apps are selected from these datasets and rearranged in Tab. 2 as Group1, Group2, Group3, Group4, & Group5.

Experimental validation of the framework was accomplished by means of MATLAB 2018a installed on i7, 2.7 GHz CPU with 16 GB RAM. Ten-fold cross-validation method was employed by randomly subdividing the dataset into ten equal parts and using one for testing and the rest for training. The final result is the average of the results obtained from five Groups as in Tab. 2.

5  Experimental Results

Experimental results comprise of the assessment of the suggested framework on the benchmarked datasets containing B and M apps as mentioned in Tab. 2. Also, comparisons of the results with other state-of-art methods applying static and dynamic features were also reported.

5.1 Qualitative Assessment

Cumulative Frequency Analysis: Qualitative assessment for the suggested framework is performed by plotting the cumulative frequencies (CF) as shown in Fig. 3 of the various static and dynamic features selected for M and B apps. The CF of the features in the M apps is directly proportional to the threat level of that particular feature for platform security.

Figure 3: Cumulative frequencies for five static and two dynamic features for group 5 dataset

In addition, score distributions of the two best state-of-the-art techniques and the suggested framework is obtained and results are shown in Fig. 4. It is apparent from Fig. 4 that the suggested framework performs better than the other two methods because of the minimum overlap of the scores. Quantitative assessment of the suggested framework follows in the next sub-section.

Figure 4: Score distribution for group2 dataset of (a) Proposed method (b) Arshad et al. [20] (c) Hussain et al. [22]

5.2 Quantitative Assessment

Quantitative assessment of the suggested method was realized using the standard evaluation benchmark viz. sensitivity, specificity, F1 Score, detection accuracy via ten-fold cross validation over datasets as mentioned in Tab. 1. The suggested method was also compared with respect to running time against different state-of-the-art methods. Evaluation metrics results are also compared with the two state-of-the-arts techniques [20,22], and two self-proposed techniques. Specificity, Sensitivity, F1 Score, and Accuracy are calculated using Eqs. (20–23).

SPECIFICITY=TNTN+FP (20)

SENSITIVITY=TPTP+FN (21)

F1SCORE=2TP2TP+FP+FN (22)

ACCURACY=TP+TNTP+FP+TN+FN (23)

where TP is true positive, FP is false positive, TN is true negative, FN is false negative. ROC curves depicted in Fig. 5 are drawn to assess the binary classifier. ROC is an overall index portraying sensitivity and specificity.

Figure 5: ROC curves for the proposed method (red) and two best state-of-the-art methods (Arshad et al. [20] (blue) and Hussain et al. [22] (black)) for (a) Group1, (b) Group 2, (c) Group 3, (d) Group 4

To test the robustness and to evade overfitting issues, 10-fold cross-validation is employed to estimate the performance of the suggested model. The investigational outcomes are displayed in Tab. 3.

6  Discussions

It has been observed that for the suggested framework the mean value of the result of the Accuracy, Specificity, Sensitivity, and F1 measure for the proposed framework are 98.62%, 98.634%, 99.30%, and 0.9916 respectively. Maximum value of the accuracy, specificity, sensitivity, and F1 score is 98.80%, 98.80%, 98.81%, and 0.9940 respectively.

The suggested technique outperforms the other hybrid-based state-of-the-art techniques when assessed on datasets as tabularized in Tab. 2. Enhancement for a mean value of detection accuracy of the suggested technique by 1.402% and 2.914%, over [20] and [22] respectively has been realized. Self-proposed techniques are also included to show the proper justification for the choice of ML algorithms in the optimal classifier. Enhancement of average detection accuracy of 2.674% and 3.274% have been achieved by the proposed method over two self-proposed methods RF+UF and SVM+UF.

The run time of different state-of-the-art approaches is also compared with the proposed approach. To calculate the running time of different methods, we first built and learned their corresponding detection model. These detection models were then fed with the 200 random apps for analysis. Our proposed method attains an average analysis performance of 5.6 seconds per app. Similarly, the average analysis performance of [20] and [22] comes out to be 6.1 seconds and 6.7 seconds respectively. Hence, our proposed method outclassed other methods in respect of detection time, detection accuracy, and efficacy in real-life apps scenarios.

7  Limitations

Hybrid technique encompasses both the static and dynamic features to detect the malapps, thereby increasing the all-around complexity of the framework regarding time, effort, and cost. Also, our method is unable to distinguish the zero-day malware as malware authors are constantly updating the current malware families and frequently produce new malwares exhibiting the unfamiliar behavior close to the B apps thereby tricking even the latest detection models.

8  Conclusion and Future Direction

To tackle the challenges of effective detection of ever-evolving android malapps, we suggested a novel hybrid malapp detection scheme. Here, the optimal combination of static and dynamic features by cross-diffusion followed by fuzzy-based score level fusion was proposed. In the suggested framework, we have used five static and two dynamic features to form static and dynamic feature vectors. These feature vectors are further fused after the formation of normalized and refined graphs through the non-linear graph diffusion method. Fused feature vector is then given to an optimal classifier comprising of RF and SVM classifiers. A remarkable benefit of our method is that it can extract the static and dynamic features in each app almost in real-time.

In sum, the unification of static and dynamic features resulted in highly distinct feature. Adoption of fuzzy-based fusion of classifier scores not only create clear boundary but also achieve optimal performance. Our technique has accomplished mean value of accuracy, specificity, sensitivity, and F1 score as 98.62%, 98.634%, 98.604%, and 99.16% respectively. Experimental results reveal that our technique outstrips other state-of-the-art methods.

In the future, we will look forward to incorporate traffic-based dynamic features for smartphone security analysis as many malapps are detected solely on their traffic characteristics. Furthermore, training on more datasets comprising diverse malware families will further add robustness to the framework.

Funding Statement: The authors received no specific funding for this study.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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