Biblio

Found 1645 results

Filters: Keyword is privacy  [Clear All Filters]
2021-05-13
Lit, Yanyan, Kim, Sara, Sy, Eric.  2021.  A Survey on Amazon Alexa Attack Surfaces. 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). :1–7.
Since being launched in 2014, Alexa, Amazon's versatile cloud-based voice service, is now active in over 100 million households worldwide [1]. Alexa's user-friendly, personalized vocal experience offers customers a more natural way of interacting with cutting-edge technology by allowing the ability to directly dictate commands to the assistant. Now in the present year, the Alexa service is more accessible than ever, available on hundreds of millions of devices from not only Amazon but third-party device manufacturers. Unfortunately, that success has also been the source of concern and controversy. The success of Alexa is based on its effortless usability, but in turn, that has led to a lack of sufficient security. This paper surveys various attacks against Amazon Alexa ecosystem including attacks against the frontend voice capturing and the cloud backend voice command recognition and processing. Overall, we have identified six attack surfaces covering the lifecycle of Alexa voice interaction that spans several stages including voice data collection, transmission, processing and storage. We also discuss the potential mitigation solutions for each attack surface to better improve Alexa or other voice assistants in terms of security and privacy.
2021-06-24
Pashchenko, Ivan, Scandariato, Riccardo, Sabetta, Antonino, Massacci, Fabio.  2021.  Secure Software Development in the Era of Fluid Multi-party Open Software and Services. 2021 IEEE/ACM 43rd International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). :91—95.
Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates over months or years, must now cope with small, continuous changes taking place within a week, and happening in sub-components that are controlled by third-party developers one might not even know they existed. In this paper, we aim to provide an overview of the current software security approaches and evaluate their appropriateness in the face of the changed nature in software development. Software security assurance could benefit by switching from a process-based to an artefact-based approach. Further, security evaluation might need to be more incremental, automated and decentralized. We believe this can be achieved by supporting mechanisms for lightweight and scalable screenings that are applicable to the entire population of software components albeit there might be a price to pay.
2021-09-21
Ghanem, Sahar M., Aldeen, Donia Naief Saad.  2020.  AltCC: Alternating Clustering and Classification for Batch Analysis of Malware Behavior. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–6.
The most common goal of malware analysis is to determine if a given binary is malware or benign. Another objective is similarity analysis of malware binaries to understand how new samples differ from known ones. Similarity analysis helps to analyze the malware with respect to those already analyzed and guides the discovery of novel aspects that should be analyzed more in depth. In this work, we are concerned with similarities and differences detection of malware binaries. Thousands of malware are created every day and machine learning is an indispensable tool for its analysis. Previous work has studied clustering and classification as competing paradigms. However, in this work, a malware similarity analysis technique (AltCC) is proposed that alternates the use of clustering and classification. In addition it assumes the malware are not available all at once but processed in batches. Initially, clustering is applied to the first batch to group similar binaries into novel malware classes. Then, the discovered classes are used to train a classifier. For the following batches, the classifier is used to decide if a new binary classifies to a known class or otherwise unclassified. The unclassified binaries are clustered and the process repeats. Malware clustering (i.e. labeling) may entail further human expert analysis but dramatically reduces the effort. The effectiveness of AltCC is studied using a dataset of 29,661 malware binaries that represent malware received in six consecutive days/batches. When KMeans is used to label the dataset all at once and its labeling is compared to AltCC's, the adjusted-rand-index scores 0.71.
bin Asad, Ashub, Mansur, Raiyan, Zawad, Safir, Evan, Nahian, Hossain, Muhammad Iqbal.  2020.  Analysis of Malware Prediction Based on Infection Rate Using Machine Learning Techniques. 2020 IEEE Region 10 Symposium (TENSYMP). :706–709.
In this modern, technological age, the internet has been adopted by the masses. And with it, the danger of malicious attacks by cybercriminals have increased. These attacks are done via Malware, and have resulted in billions of dollars of financial damage. This makes the prevention of malicious attacks an essential part of the battle against cybercrime. In this paper, we are applying machine learning algorithms to predict the malware infection rates of computers based on its features. We are using supervised machine learning algorithms and gradient boosting algorithms. We have collected a publicly available dataset, which was divided into two parts, one being the training set, and the other will be the testing set. After conducting four different experiments using the aforementioned algorithms, it has been discovered that LightGBM is the best model with an AUC Score of 0.73926.
2021-01-11
Žulj, S., Delija, D., Sirovatka, G..  2020.  Analysis of secure data deletion and recovery with common digital forensic tools and procedures. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1607–1610.
This paper presents how students practical’s is developed and used for the important task forensic specialist have to do when using common digital forensic tools for data deletion and data recovery from various types of digital media and live systems. Digital forensic tools like EnCase, FTK imager, BlackLight, and open source tools are discussed in developed practical’s scenarios. This paper shows how these tools can be used to train and enhance student understanding of the capabilities and limitations of digital forensic tools in uncommon digital forensic scenarios. Students’ practicals encourage students to efficiently use digital forensic tools in the various professional scenarios that they will encounter.
2021-09-21
Kartel, Anastasia, Novikova, Evgenia, Volosiuk, Aleksandr.  2020.  Analysis of Visualization Techniques for Malware Detection. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :337–340.
Due to the steady growth of various sophisticated types of malware, different malware analysis systems are becoming more and more demanded. While there are various automatic approaches available to identify and detect malware, the malware analysis is still time-consuming process. The visualization-driven techniques may significantly increase the efficiency of the malware analysis process by involving human visual system which is a powerful pattern seeker. In this paper the authors reviewed different visualization methods, examined their features and tasks solved with their help. The paper presents the most commonly used approaches and discusses open challenges in malware visual analytics.
2021-05-25
Zanin, M., Menasalvas, E., González, A. Rodriguez, Smrz, P..  2020.  An Analytics Toolbox for Cyber-Physical Systems Data Analysis: Requirements and Challenges. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :271–276.
The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.
2021-02-10
Tizio, G. Di, Ngo, C. Nam.  2020.  Are You a Favorite Target For Cryptojacking? A Case-Control Study On The Cryptojacking Ecosystem 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :515—520.
Illicitly hijacking visitors' computational resources for mining cryptocurrency via compromised websites is a consolidated activity.Previous works mainly focused on large-scale analysis of the cryptojacking ecosystem, technical means to detect browser-based mining as well as economic incentives of cryptojacking. So far, no one has studied if certain technical characteristics of a website can increase (decrease) the likelihood of being compromised for cryptojacking campaigns.In this paper, we propose to address this unanswered question by conducting a case-control study with cryptojacking websites obtained crawling the web using Minesweeper. Our preliminary analysis shows some association for certain website characteristics, however, the results obtained are not statistically significant. Thus, more data must be collected and further analysis must be conducted to obtain a better insight into the impact of these relations.
2021-05-13
Feng, Xiaohua, Feng, Yunzhong, Dawam, Edward Swarlat.  2020.  Artificial Intelligence Cyber Security Strategy. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :328—333.
Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments' strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.
2021-09-21
Yang, Ping, Shu, Hui, Kang, Fei, Bu, Wenjuan.  2020.  Automatically Generating Malware Summary Using Semantic Behavior Graphs (SBGs). 2020 Information Communication Technologies Conference (ICTC). :282–291.
In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary.
2021-04-27
Tsai, W., Chou, T., Chen, J., Ma, Y., Huang, C..  2020.  Blockchain as a Platform for Secure Cloud Computing Services. 2020 22nd International Conference on Advanced Communication Technology (ICACT). :155—158.
Problems related to privacy and cyber-attacks have increased in recent years as a result of the rapid development of cloud computing. This work concerns secure cloud computing services on a blockchain platform, called cloud@blockchain, which benefit from the anonymity and immutability of blockchain. Two functions- anonymous file sharing and inspections to find illegally uploaded files- on cloud@blockchain are designed. On cloud@blockchain, cloud users can access data through smart contracts, and recognize all users within the application layer. The performance of three architectures- a pure blockchain, a hybrid blockchain with cache and a traditional database in accessing data is analyzed. The results reveal the superiority of the hybrid blockchain with the cache over the pure blockchain and the traditional database, which it outperforms by 500% and 53.19%, respectively.
2021-06-24
Połap, Dawid, Srivastava, Gautam, Jolfaei, Alireza, Parizi, Reza M..  2020.  Blockchain Technology and Neural Networks for the Internet of Medical Things. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :508–513.
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
2021-04-27
Kuhn, C., Beck, M., Strufe, T..  2020.  Breaking and (Partially) Fixing Provably Secure Onion Routing. 2020 IEEE Symposium on Security and Privacy (SP). :168–185.
After several years of research on onion routing, Camenisch and Lysyanskaya, in an attempt at rigorous analysis, defined an ideal functionality in the universal composability model, together with properties that protocols have to meet to achieve provable security. A whole family of systems based their security proofs on this work. However, analyzing HORNET and Sphinx, two instances from this family, we show that this proof strategy is broken. We discover a previously unknown vulnerability that breaks anonymity completely, and explain a known one. Both should not exist if privacy is proven correctly.In this work, we analyze and fix the proof strategy used for this family of systems. After proving the efficacy of the ideal functionality, we show how the original properties are flawed and suggest improved, effective properties in their place. Finally, we discover another common mistake in the proofs. We demonstrate how to avoid it by showing our improved properties for one protocol, thus partially fixing the family of provably secure onion routing protocols.
2021-01-11
Wang, W.-C., Ho, C.-C., Chang, Y.-M., Chang, Y.-H..  2020.  Challenges and Designs for Secure Deletion in Storage Systems. 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). :181–189.
Data security has risen to be one of the most critical concerns of computer professionals. Tighter legal requirements now exist for the purpose of protecting user data from unauthorized uses and for both preserving and erasing/sanitizing data records to meet legal compliance requirements. To meet the data security requirement, many secure (data) deletion techniques have been proposed to deal with the data security concerns from different system layers. This paper surveys the state-of-the-art secure deletion techniques that have been designed to pursue higher efficiency, verifiability, and portability for emerging types of hard disk drives and flash-based solid-state drives. Meanwhile, the pros and cons of implementing secure deletion in different system layers are also discussed, so as to assist in pursuing better secure deletion designs for future storage systems.
2021-09-21
Barr, Joseph R., Shaw, Peter, Abu-Khzam, Faisal N., Yu, Sheng, Yin, Heng, Thatcher, Tyler.  2020.  Combinatorial Code Classification Amp; Vulnerability Rating. 2020 Second International Conference on Transdisciplinary AI (TransAI). :80–83.
Empirical analysis of source code of Android Fluoride Bluetooth stack demonstrates a novel approach of classification of source code and rating for vulnerability. A workflow that combines deep learning and combinatorial techniques with a straightforward random forest regression is presented. Two kinds of embedding are used: code2vec and LSTM, resulting in a distance matrix that is interpreted as a (combinatorial) graph whose vertices represent code components, functions and methods. Cluster Editing is then applied to partition the vertex set of the graph into subsets representing nearly complete subgraphs. Finally, the vectors representing the components are used as features to model the components for vulnerability risk.
Brezinski, Kenneth, Ferens, Ken.  2020.  Complexity-Based Convolutional Neural Network for Malware Classification. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). :1–9.
Malware classification remains at the forefront of ongoing research as the prevalence of metamorphic malware introduces new challenges to anti-virus vendors and firms alike. One approach to malware classification is Static Analysis - a form of analysis which does not require malware to be executed before classification can be performed. For this reason, a lightweight classifier based on the features of a malware binary is preferred, with relatively low computational overhead. In this work a modified convolutional neural network (CNN) architecture was deployed which integrated a complexity-based evaluation based on box-counting. This was implemented by setting up max-pooling layers in parallel, and then extracting the fractal dimension using a polyscalar relationship based on the resolution of the measurement scale and the number of elements of a malware image covered in the measurement under consideration. To test the robustness and efficacy of our approach we trained and tested on over 9300 malware binaries from 25 unique malware families. This work was compared to other award-winning image recognition models, and results showed categorical accuracy in excess of 96.54%.
2021-04-27
Sekar, K., Devi, K. Suganya, Srinivasan, P., SenthilKumar, V. M..  2020.  Deep Wavelet Architecture for Compressive sensing Recovery. 2020 Seventh International Conference on Information Technology Trends (ITT). :185–189.
The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.
2021-02-01
Sendhil, R., Amuthan, A..  2020.  A Descriptive Study on Homomorphic Encryption Schemes for Enhancing Security in Fog Computing. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :738–743.
Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.
2021-06-24
Gamagedara Arachchilage, Nalin Asanka, Hameed, Mumtaz Abdul.  2020.  Designing a Serious Game: Teaching Developers to Embed Privacy into Software Systems. 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). :7—12.
Software applications continue to challenge user privacy when users interact with them. Privacy practices (e.g. Data Minimisation (DM), Privacy by Design (PbD) or General Data Protection Regulation (GDPR)) and related “privacy engineering” methodologies exist and provide clear instructions for developers to implement privacy into software systems they develop that preserve user privacy. However, those practices and methodologies are not yet a common practice in the software development community. There has been no previous research focused on developing “educational” interventions such as serious games to enhance software developers' coding behaviour. Therefore, this research proposes a game design framework as an educational tool for software developers to improve (secure) coding behaviour, so they can develop privacy-preserving software applications that people can use. The elements of the proposed framework were incorporated into a gaming application scenario that enhances the software developers' coding behaviour through their motivation. The proposed work not only enables the development of privacy-preserving software systems but also helping the software development community to put privacy guidelines and engineering methodologies into practice.
2021-02-16
Liu, F., Eugenio, E., Jin, I. H., Bowen, C..  2020.  Differentially Private Generation of Social Networks via Exponential Random Graph Models. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1695—1700.
Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.
2021-09-21
Petrenko, Sergei A., Petrenko, Alexey S., Makoveichuk, Krystina A., Olifirov, Alexander V..  2020.  "Digital Bombs" Neutralization Method. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :446–451.
The article discusses new models and methods for timely identification and blocking of malicious code of critically important information infrastructure based on static and dynamic analysis of executable program codes. A two-stage method for detecting malicious code in the executable program codes (the so-called "digital bombs") is described. The first step of the method is to build the initial program model in the form of a control graph, the construction is carried out at the stage of static analysis of the program. The article discusses the purpose, features and construction criteria of an ordered control graph. The second step of the method is to embed control points in the program's executable code for organizing control of the possible behavior of the program using a specially designed recognition automaton - an automaton of dynamic control. Structural criteria for the completeness of the functional control of the subprogram are given. The practical implementation of the proposed models and methods was completed and presented in a special instrumental complex IRIDA.
2021-05-20
Almogbil, Atheer, Alghofaili, Abdullah, Deane, Chelsea, Leschke, Timothy, Almogbil, Atheer, Alghofaili, Abdullah.  2020.  Digital Forensic Analysis of Fitbit Wearable Technology: An Investigator’s Guide. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :44—49.
Wearable technology, such as Fitbit devices, log a user's daily activities, heart rate, calories burned, step count, and sleep activity. This information is valuable to digital forensic investigators as it may serve as evidence to a crime, to either support a suspect's innocence or guilt. It is important for an investigator to find and analyze every piece of data for accuracy and integrity; however, there is no standard for conducting a forensic investigation for wearable technology. In this paper, we conduct a forensic analysis of two different Fitbit devices using open-source tools. It is the responsibility of the investigator to show how the data was obtained and to ensure that the data was not modified during the analysis. This paper will guide investigators in understanding what data is collected by a Fitbit device (specifically the Ionic smartwatch and Alta tracker), how to handle Fitbit devices, and how to extract and forensically analyze said devices using open-source tools, Autopsy Sleuth Kit and Bulk Extractor Viewer.
2021-01-11
Awad, M. A., Ashkiani, S., Porumbescu, S. D., Owens, J. D..  2020.  Dynamic Graphs on the GPU. 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). :739–748.
We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4-14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures.
2021-09-21
Mohanasruthi, V., Chakraborty, Abhishek, Thanudas, B., Sreelal, S., Manoj, B. S..  2020.  An Efficient Malware Detection Technique Using Complex Network-Based Approach. 2020 National Conference on Communications (NCC). :1–6.
System security is becoming an indispensable part of our daily life due to the rapid proliferation of unknown malware attacks. Recent malware found to have a very complicated structure that is hard to detect by the traditional malware detection techniques such as antivirus, intrusion detection systems, and network scanners. In this paper, we propose a complex network-based malware detection technique, Malware Detection using Complex Network (MDCN), that considers Application Program Interface Call Transition Matrix (API-CTM) to generate complex network topology and then extracts various feature set by analyzing different metrics of the complex network to distinguish malware and benign applications. The generated feature set is then sent to several machine learning classifiers, which include naive-Bayes, support vector machine, random forest, and multilayer perceptron, to comparatively analyze the performance of MDCN-based technique. The analysis reveals that MDCN shows higher accuracy, with lower false-positive cases, when the multilayer perceptron-based classifier is used for the detection of malware. MDCN technique can efficiently be deployed in the design of an integrated enterprise network security system.
2021-06-01
Shang, X., Shi, L.N., Niu, J.B., Xie, C.Q..  2020.  Efficient Mie Resonance of Metal-masked Titanium Dioxide Nanopillars. 2020 Fourteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). :171—173.
Here, we propose a simple design approach based on metal-masked titanium dioxide nanopillars, which can realize strong Mie resonance in metasurfaces and enables light confinement within itself over the range of visible wavelengths. By selecting the appropriate period and diameter of individual titanium dioxide nanopillars, the coincidence of resonance peak positions derived from excited electric and magnetic dipoles can be achived. And the optical properties in this design have been investigated with the Finite-Difference Time-Domain(FDTD) solutions.