6 research outputs found
Using Deep Neural Network for Android Malware Detection
The pervasiveness of the Android operating system, with the availability of
applications almost for everything, is readily accessible in the official
Google play store or a dozen alternative third-party markets. Additionally, the
vital role of smartphones in modern life leads to store significant information
on devices, not only personal information but also corporate information, which
attract malware developers to develop applications that can infiltrate user's
devices to steal information and perform harmful tasks. This accompanied with
the limitation of currently defenses techniques such as ineffective screening
in Google play store, weak or no screening in third-party markets. Antiviruses
software that still relies on a signature-based database that is effective only
in identifying known malware. To contrive with malicious applications that are
increased in volume and sophistication, we propose an Android malware detection
system that applies deep learning technique to face the threats of Android
malware. Extensive experiments on a real-world dataset contain benign and
malicious applications uncovered that the proposed system reaches an accuracy
of 95.31%.Comment: 9 pages, 5 figures, 6 Table
Android Malware Detection Using Autoencoder
Smartphones have become an intrinsic part of human's life. The smartphone
unifies diverse advanced characteristics. It enables users to store various
data such as photos, health data, credential bank data, and personal
information. The Android operating system is the prevalent mobile operating
system and, in the meantime, the most targeted operating system by malware
developers. Recently the unparalleled development of Android malware put
pressure on researchers to propose effective methods to suppress the spread of
the malware. In this paper, we propose a deep learning approach for Android
malware detection. The proposed approach investigates five different feature
sets and applies Autoencoder to identify malware. The experimental results show
that the proposed approach can identify malware with high accuracy.Comment: 9 Pages, 4 Figures, 3 Table
Identification of Significant Permissions for Efficient Android Malware Detection
Since Google unveiled Android OS for smartphones, malware are thriving with
3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out
of every five business/industry mobile application leaks sensitive personal
data. Traditional signature/heuristic-based malware detection systems are
unable to cope up with current malware challenges and thus threaten the Android
ecosystem. Therefore recently researchers have started exploring machine
learning and deep learning based malware detection systems. In this paper, we
performed a comprehensive feature analysis to identify the significant Android
permissions and propose an efficient Android malware detection system using
machine learning and deep neural network. We constructed a set of
permissions ( of the total set) derived from variance threshold,
auto-encoders, and principal component analysis to build a malware detection
engine that consumes less train and test time without significant compromise on
the model accuracy. Our experimental results show that the Android malware
detection model based on the random forest classifier is most balanced and
achieves the highest area under curve score of , which is better than
the current state-of-art systems. We also observed that deep neural networks
attain comparable accuracy to the baseline results but with a massive
computational penalty.Comment: BROADNETS, 202
A Review on The Use of Deep Learning in Android Malware Detection
Android is the predominant mobile operating system for the past few years.
The prevalence of devices that can be powered by Android magnetized not merely
application developers but also malware developers with criminal intention to
design and spread malicious applications that can affect the normal work of
Android phones and tablets, steal personal information and credential data, or
even worse lock the phone and ask for ransom. Researchers persistently devise
countermeasures strategies to fight back malware. One of these strategies
applied in the past five years is the use of deep learning methods in Android
malware detection. This necessitates a review to inspect the accomplished work
in order to know where the endeavors have been established, identify unresolved
problems, and motivate future research directions. In this work, an extensive
survey of static analysis, dynamic analysis, and hybrid analysis that utilized
deep learning methods are reviewed with an elaborated discussion on their key
concepts, contributions, and limitations.Comment: 15 pages, 4 table
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering
Today anti-malware community is facing challenges due to the ever-increasing
sophistication and volume of malware attacks developed by adversaries.
Traditional malware detection mechanisms are not able to cope-up with
next-generation malware attacks. Therefore in this paper, we propose effective
and efficient Android malware detection models based on machine learning and
deep learning integrated with clustering. We performed a comprehensive study of
different feature reduction, classification and clustering algorithms over
various performance metrics to construct the Android malware detection models.
Our experimental results show that malware detection models developed using
Random Forest eclipsed deep neural network and other classifiers on the
majority of performance metrics. The baseline Random Forest model without any
feature reduction achieved the highest AUC of 99.4%. Also, the segregating of
vector space using clustering integrated with Random Forest further boosted the
AUC to 99.6% in one cluster and direct detection of Android malware in another
cluster, thus reducing the curse of dimensionality. Additionally, we found that
feature reduction in detection models does improve the model efficiency
(training and testing time) many folds without much penalty on the
effectiveness of the detection model.Comment: BROADNETS, 202
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain
The widespread significance of Android IoT devices is due to its flexibility
and hardware support features which revolutionized the digital world by
introducing exciting applications almost in all walks of daily life, such as
healthcare, smart cities, smart environments, safety, remote sensing, and many
more. Such versatile applicability gives incentive for more malware attacks. In
this paper, we propose a framework which continuously aggregates multiple user
trained models on non-overlapping data into single model. Specifically for
malware detection task, (i) we propose a novel user (local) neural network
(LNN) which trains on local distribution and (ii) then to assure the model
authenticity and quality, we propose a novel smart contract which enable
aggregation process over blokchain platform. The LNN model analyzes various
static and dynamic features of both malware and benign whereas the smart
contract verifies the malicious applications both for uploading and downloading
processes in the network using stored aggregated features of local models. In
this way, the proposed model not only improves malware detection accuracy using
decentralized model network but also model efficacy with blockchain. We
evaluate our approach with three state-of-the-art models and performed deep
analyses of extracted features of the relative model