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2019-08-05
Kita, Kentaro, Kurihara, Yoshiki, Koizumi, Yuki, Hasegawa, Toru.  2018.  Location Privacy Protection with a Semi-honest Anonymizer in Information Centric Networking. Proceedings of the 5th ACM Conference on Information-Centric Networking. :95–105.
Location-based services, which provide services based on locations of consumers' interests, are becoming essential for our daily lives. Since the location of a consumer's interest contains private information, several studies propose location privacy protection mechanisms using an anonymizer, which sends queries specifying anonymous location sets, each of which contains k - 1 locations in addition to a location of a consumer's interest, to an LBS provider based on the k-anonymity principle. The anonymizer is, however, assumed to be trusted/honest, and hence it is a single point of failure in terms of privacy leakage. To address this privacy issue, this paper designs a semi-honest anonymizer to protect location privacy in NDN networks. This study first reveals that session anonymity and location anonymity must be achieved to protect location privacy with a semi-honest anonymizer. Session anonymity is to hide who specifies which anonymous location set and location anonymity is to hide a location of a consumer's interest in a crowd of locations. We next design an architecture to achieve session anonymity and an algorithm to generate anonymous location sets achieving location anonymity. Our evaluations show that the architecture incurs marginal overhead to achieve session anonymity and anonymous location sets generated by the algorithm sufficiently achieve location anonymity.
Suksomboon, Kalika, Ueda, Kazuaki, Tagami, Atsushi.  2018.  Content-centric Privacy Model for Monitoring Services in Surveillance Systems. Proceedings of the 5th ACM Conference on Information-Centric Networking. :190–191.
This paper proposes a content-centric privacy (CCP) model that enables a privacy-preserving monitoring services in surveillance systems without cloud dependency. We design a simple yet powerful method that could not be obtained from a cloud-like system. The CCP model includes two key ideas: (1) the separation of the private data (i.e., target object images) from the public data (i.e., background images), and (2) the service authentication with the classification model. Deploying the CCP model over ICN enables the privacy central around the content itself rather than relying on a cloud system. Our preliminary analysis shows that the ICN-based CCP model can preserve privacy with respect to the W3 -privacy in which the private information of target object are decoupled from the queries and cameras.
Papernot, Nicolas.  2018.  A Marauder's Map of Security and Privacy in Machine Learning: An Overview of Current and Future Research Directions for Making Machine Learning Secure and Private. Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security. :1–1.
There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The second encompasses a spectrum of approaches to input verification and mediation, which is a prerequisite to enable fail-safe defaults in machine learning systems. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desirata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our growing community.
2019-07-01
Ferreyra, N. E. Díaz, Meisy, R., Heiselz, M..  2018.  At Your Own Risk: Shaping Privacy Heuristics for Online Self-Disclosure. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1-10.

Revealing private and sensitive information on Social Network Sites (SNSs) like Facebook is a common practice which sometimes results in unwanted incidents for the users. One approach for helping users to avoid regrettable scenarios is through awareness mechanisms which inform a priori about the potential privacy risks of a self-disclosure act. Privacy heuristics are instruments which describe recurrent regrettable scenarios and can support the generation of privacy awareness. One important component of a heuristic is the group of people who should not access specific private information under a certain privacy risk. However, specifying an exhaustive list of unwanted recipients for a given regrettable scenario can be a tedious task which necessarily demands the user's intervention. In this paper, we introduce an approach based on decision trees to instantiate the audience component of privacy heuristics with minor intervention from the users. We introduce Disclosure- Acceptance Trees, a data structure representative of the audience component of a heuristic and describe a method for their generation out of user-centred privacy preferences.

Saleem, Jibran, Hammoudeh, Mohammad, Raza, Umar, Adebisi, Bamidele, Ande, Ruth.  2018.  IoT Standardisation: Challenges, Perspectives and Solution. Proceedings of the 2Nd International Conference on Future Networks and Distributed Systems. :1:1-1:9.

The success and widespread adoption of the Internet of Things (IoT) has increased many folds over the last few years. Industries, technologists and home users recognise the importance of IoT in their lives. Essentially, IoT has brought vast industrial revolution and has helped automate many processes within organisations and homes. However, the rapid growth of IoT is also a cause for significant concern. IoT is not only plagued with security, authentication and access control issues, it also doesn't work as well as it should with fourth industrial revolution, commonly known as Industry 4.0. The absence of effective regulation, standards and weak governance has led to a continual downward trend in the security of IoT networks and devices, as well as given rise to a broad range of privacy issues. This paper examines the IoT industry and discusses the urgent need for standardisation, the benefits of governance as well as the issues affecting the IoT sector due to the absence of regulation. Additionally, through this paper, we are introducing an IoT security framework (IoTSFW) for organisations to bridge the current lack of guidelines in the IoT industry. Implementation of the guidelines, defined in the proposed framework, will assist organisations in achieving security, privacy, sustainability and scalability within their IoT networks.

2019-06-24
Diamond, Lisa, Schrammel, Johann, Fröhlich, Peter, Regal, Georg, Tscheligi, Manfred.  2018.  Privacy in the Smart Grid: End-user Concerns and Requirements. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. :189–196.

Mobile interfaces will be central in connecting end-users to the smart grid and enabling their active participation. Services and features supporting this participation do, however, rely on high-frequency collection and transmission of energy usage data by smart meters which is privacy-sensitive. The successful communication of privacy to end-users via consumer interfaces will therefore be crucial to ensure smart meter acceptance and consequently enable participation. Current understanding of user privacy concerns in this context is not very differentiated, and user privacy requirements have received little attention. A preliminary user questionnaire study was conducted to gain a more detailed understanding of the differing perceptions of various privacy risks and the relative importance of different privacy-ensuring measures. The results underline the significance of open communication, restraint in data collection and usage, user control, transparency, communication of security measures, and a good customer relationship.

You, Y., Li, Z., Oechtering, T. J..  2018.  Optimal Privacy-Enhancing And Cost-Efficient Energy Management Strategies For Smart Grid Consumers. 2018 IEEE Statistical Signal Processing Workshop (SSP). :826–830.

The design of optimal energy management strategies that trade-off consumers' privacy and expected energy cost by using an energy storage is studied. The Kullback-Leibler divergence rate is used to assess the privacy risk of the unauthorized testing on consumers' behavior. We further show how this design problem can be formulated as a belief state Markov decision process problem so that standard tools of the Markov decision process framework can be utilized, and the optimal solution can be obtained by using Bellman dynamic programming. Finally, we illustrate the privacy-enhancement and cost-saving by numerical examples.

Okay, F. Y., Ozdemir, S..  2018.  A secure data aggregation protocol for fog computing based smart grids. 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018). :1–6.

In Smart Grids (SGs), data aggregation process is essential in terms of limiting packet size, data transmission amount and data storage requirements. This paper presents a novel Domingo-Ferrer additive privacy based Secure Data Aggregation (SDA) scheme for Fog Computing based SGs (FCSG). The proposed protocol achieves end-to-end confidentiality while ensuring low communication and storage overhead. Data aggregation is performed at fog layer to reduce the amount of data to be processed and stored at cloud servers. As a result, the proposed protocol achieves better response time and less computational overhead compared to existing solutions. Moreover, due to hierarchical architecture of FCSG and additive homomorphic encryption consumer privacy is protected from third parties. Theoretical analysis evaluates the effects of packet size and number of packets on transmission overhead and the amount of data stored in cloud server. In parallel with the theoretical analysis, our performance evaluation results show that there is a significant improvement in terms of data transmission and storage efficiency. Moreover, security analysis proves that the proposed scheme successfully ensures the privacy of collected data.

Oriero, E., Rahman, M. A..  2018.  Privacy Preserving Fine-Grained Data Distribution Aggregation for Smart Grid AMI Networks. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :1–9.

An advanced metering infrastructure (AMI)allows real-time fine-grained monitoring of the energy consumption data of individual consumers. Collected metering data can be used for a multitude of applications. For example, energy demand forecasting, based on the reported fine-grained consumption, can help manage the near future energy production. However, fine- grained metering data reporting can lead to privacy concerns. It is, therefore, imperative that the utility company receives the fine-grained data needed to perform the intended demand response service, without learning any sensitive information about individual consumers. In this paper, we propose an anonymous privacy preserving fine-grained data aggregation scheme for AMI networks. In this scheme, the utility company receives only the distribution of the energy consumption by the consumers at different time slots. We leverage a network tree topology structure in which each smart meter randomly reports its energy consumption data to its parent smart meter (according to the tree). The parent node updates the consumption distribution and forwards the data to the utility company. Our analysis results show that the proposed scheme can preserve the privacy and security of individual consumers while guaranteeing the demand response service.

Wang, J., Zhang, X., Zhang, H., Lin, H., Tode, H., Pan, M., Han, Z..  2018.  Data-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile. 2018 IEEE Global Communications Conference (GLOBECOM). :1–6.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Mohammad, Z., Qattam, T. A., Saleh, K..  2019.  Security Weaknesses and Attacks on the Internet of Things Applications. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). :431–436.

Internet of Things (IoT) is a contemporary concept for connecting the existing things in our environment with the Internet for a sake of making the objects information are accessible from anywhere and anytime to support a modern life style based on the Internet. With the rapid development of the IoT technologies and widely spreading in most of the fields such as buildings, health, education, transportation and agriculture. Thus, the IoT applications require increasing data collection from the IoT devices to send these data to the applications or servers which collect or analyze the data, so it is a very important to secure the data and ensure that do not reach a malicious adversary. This paper reviews some attacks in the IoT applications and the security weaknesses in the IoT environment. In addition, this study presents the challenges of IoT in terms of hardware, network and software. Moreover, this paper summarizes and points to some attacks on the smart car, smart home, smart campus, smart farm and healthcare.

2019-06-17
Noroozi, Hamid, Khodaei, Mohammad, Papadimitratos, Panos.  2018.  VPKIaaS: A Highly-Available and Dynamically-Scalable Vehicular Public-Key Infrastructure. Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :302–304.
The central building block of secure and privacy-preserving Vehicular Communication (VC) systems is a Vehicular Public-Key Infrastructure (VPKI), which provides vehicles with multiple anonymized credentials, termed pseudonyms. These pseudonyms are used to ensure message authenticity and integrity while preserving vehicle (and thus passenger) privacy. In the light of emerging large-scale multi-domain VC environments, the efficiency of the VPKI and, more broadly, its scalability are paramount. In this extended abstract, we leverage the state-of-the-art VPKI system and enhance its functionality towards a highly-available and dynamically-scalable design; this ensures that the system remains operational in the presence of benign failures or any resource depletion attack, and that it dynamically scales out, or possibly scales in, according to the requests' arrival rate. Our full-blown implementation on the Google Cloud Platform shows that deploying a VPKI for a large-scale scenario can be cost-effective, while efficiently issuing pseudonyms for the requesters.
2019-06-10
Tran, T. K., Sato, H., Kubo, M..  2018.  One-Shot Learning Approach for Unknown Malware Classification. 2018 5th Asian Conference on Defense Technology (ACDT). :8-13.

Early detection of new kinds of malware always plays an important role in defending the network systems. Especially, if intelligent protection systems could themselves detect an existence of new malware types in their system, even with a very small number of malware samples, it must be a huge benefit for the organization as well as the social since it help preventing the spreading of that kind of malware. To deal with learning from few samples, term ``one-shot learning'' or ``fewshot learning'' was introduced, and mostly used in computer vision to recognize images, handwriting, etc. An approach introduced in this paper takes advantage of One-shot learning algorithms in solving the malware classification problem by using Memory Augmented Neural Network in combination with malware's API calls sequence, which is a very valuable source of information for identifying malware behavior. In addition, it also use some advantages of the development in Natural Language Processing field such as word2vec, etc. to convert those API sequences to numeric vectors before feeding to the one-shot learning network. The results confirm very good accuracies compared to the other traditional methods.

Kargaard, J., Drange, T., Kor, A., Twafik, H., Butterfield, E..  2018.  Defending IT Systems against Intelligent Malware. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). :411-417.

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

Roseline, S. A., Geetha, S..  2018.  Intelligent Malware Detection Using Oblique Random Forest Paradigm. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :330-336.

With the increase in the popularity of computerized online applications, the analysis, and detection of a growing number of newly discovered stealthy malware poses a significant challenge to the security community. Signature-based and behavior-based detection techniques are becoming inefficient in detecting new unknown malware. Machine learning solutions are employed to counter such intelligent malware and allow performing more comprehensive malware detection. This capability leads to an automatic analysis of malware behavior. The proposed oblique random forest ensemble learning technique is efficient for malware classification. The effectiveness of the proposed method is demonstrated with three malware classification datasets from various sources. The results are compared with other variants of decision tree learning models. The proposed system performs better than the existing system in terms of classification accuracy and false positive rate.

Udayakumar, N., Saglani, V. J., Cupta, A. V., Subbulakshmi, T..  2018.  Malware Classification Using Machine Learning Algorithms. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). :1-9.

Lately, we are facing the Malware crisis due to various types of malware or malicious programs or scripts available in the huge virtual world - the Internet. But, what is malware? Malware can be a malicious software or a program or a script which can be harmful to the user's computer. These malicious programs can perform a variety of functions, including stealing, encrypting or deleting sensitive data, altering or hijacking core computing functions and monitoring users' computer activity without their permission. There are various entry points for these programs and scripts in the user environment, but only one way to remove them is to find them and kick them out of the system which isn't an easy job as these small piece of script or code can be anywhere in the user system. This paper involves the understanding of different types of malware and how we will use Machine Learning to detect these malwares.

Jiang, J., Yin, Q., Shi, Z., Li, M..  2018.  Comprehensive Behavior Profiling Model for Malware Classification. 2018 IEEE Symposium on Computers and Communications (ISCC). :00129-00135.

In view of the great threat posed by malware and the rapid growing trend about malware variants, it is necessary to determine the category of new samples accurately for further analysis and taking appropriate countermeasures. The network behavior based classification methods have become more popular now. However, the behavior profiling models they used usually only depict partial network behavior of samples or require specific traffic selection in advance, which may lead to adverse effects on categorizing advanced malware with complex activities. In this paper, to overcome the shortages of traditional models, we raise a comprehensive behavior model for profiling the behavior of malware network activities. And we also propose a corresponding malware classification method which can extract and compare the major behavior of samples. The experimental and comparison results not only demonstrate our method can categorize samples accurately in both criteria, but also prove the advantage of our profiling model to two other approaches in accuracy performance, especially under scenario based criteria.

Kim, C. H., Kabanga, E. K., Kang, S..  2018.  Classifying Malware Using Convolutional Gated Neural Network. 2018 20th International Conference on Advanced Communication Technology (ICACT). :40-44.

Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset.

Kalash, M., Rochan, M., Mohammed, N., Bruce, N. D. B., Wang, Y., Iqbal, F..  2018.  Malware Classification with Deep Convolutional Neural Networks. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1-5.

In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.

Kornish, D., Geary, J., Sansing, V., Ezekiel, S., Pearlstein, L., Njilla, L..  2018.  Malware Classification Using Deep Convolutional Neural Networks. 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). :1-6.

In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learning, object detection, and pattern recognition. Furthermore, deep learning techniques achieved exceptional performance in image classification, reaching accuracy levels beyond human capability. Malware variants from similar categories often contain similarities due to code reuse. Converting malware samples into images can cause these patterns to manifest as image features, which can be exploited for DCNN classification. Techniques for converting malware binaries into images for visualization and classification have been reported in the literature, and while these methods do reach a high level of classification accuracy on training datasets, they tend to be vulnerable to overfitting and perform poorly on previously unseen samples. In this paper, we explore and document a variety of techniques for representing malware binaries as images with the goal of discovering a format best suited for deep learning. We implement a database for malware binaries from several families, stored in hexadecimal format. These malware samples are converted into images using various approaches and are used to train a neural network to recognize visual patterns in the input and classify malware based on the feature vectors. Each image type is assessed using a variety of learning models, such as transfer learning with existing DCNN architectures and feature extraction for support vector machine classifier training. Each technique is evaluated in terms of classification accuracy, result consistency, and time per trial. Our preliminary results indicate that improved image representation has the potential to enable more effective classification of new malware.

Alsulami, B., Mancoridis, S..  2018.  Behavioral Malware Classification Using Convolutional Recurrent Neural Networks. 2018 13th International Conference on Malicious and Unwanted Software (MALWARE). :103-111.

Behavioral malware detection aims to improve on the performance of static signature-based techniques used by anti-virus systems, which are less effective against modern polymorphic and metamorphic malware. Behavioral malware classification aims to go beyond the detection of malware by also identifying a malware's family according to a naming scheme such as the ones used by anti-virus vendors. Behavioral malware classification techniques use run-time features, such as file system or network activities, to capture the behavioral characteristic of running processes. The increasing volume of malware samples, diversity of malware families, and the variety of naming schemes given to malware samples by anti-virus vendors present challenges to behavioral malware classifiers. We describe a behavioral classifier that uses a Convolutional Recurrent Neural Network and data from Microsoft Windows Prefetch files. We demonstrate the model's improvement on the state-of-the-art using a large dataset of malware families and four major anti-virus vendor naming schemes. The model is effective in classifying malware samples that belong to common and rare malware families and can incrementally accommodate the introduction of new malware samples and families.

Kim, H. M., Song, H. M., Seo, J. W., Kim, H. K..  2018.  Andro-Simnet: Android Malware Family Classification Using Social Network Analysis. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1-8.

While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.

Debatty, T., Mees, W., Gilon, T..  2018.  Graph-Based APT Detection. 2018 International Conference on Military Communications and Information Systems (ICMCIS). :1-8.

In this paper we propose a new algorithm to detect Advanced Persistent Threats (APT's) that relies on a graph model of HTTP traffic. We also implement a complete detection system with a web interface that allows to interactively analyze the data. We perform a complete parameter study and experimental evaluation using data collected on a real network. The results show that the performance of our system is comparable to currently available antiviruses, although antiviruses use signatures to detect known malwares while our algorithm solely uses behavior analysis to detect new undocumented attacks.

Xue, S., Zhang, L., Li, A., Li, X., Ruan, C., Huang, W..  2018.  AppDNA: App Behavior Profiling via Graph-Based Deep Learning. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1475-1483.

Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.

Jiang, H., Turki, T., Wang, J. T. L..  2018.  DLGraph: Malware Detection Using Deep Learning and Graph Embedding. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). :1029-1033.

In this paper we present a new approach, named DLGraph, for malware detection using deep learning and graph embedding. DLGraph employs two stacked denoising autoencoders (SDAs) for representation learning, taking into consideration computer programs' function-call graphs and Windows application programming interface (API) calls. Given a program, we first use a graph embedding technique that maps the program's function-call graph to a vector in a low-dimensional feature space. One SDA in our deep learning model is used to learn a latent representation of the embedded vector of the function-call graph. The other SDA in our model is used to learn a latent representation of the given program's Windows API calls. The two learned latent representations are then merged to form a combined feature vector. Finally, we use softmax regression to classify the combined feature vector for predicting whether the given program is malware or not. Experimental results based on different datasets demonstrate the effectiveness of the proposed approach and its superiority over a related method.