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2021-05-26
Yang, Wenti, Wang, Ruimiao, Guan, Zhitao, Wu, Longfei, Du, Xiaojiang, Guizani, Mohsen.  2020.  A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

Wah Myint, Phyo Wah, Hlaing, Swe Zin, Htoon, Ei Chaw.  2020.  EAC: Encryption Access Control Scheme for Policy Revocation in Cloud Data. 2020 International Conference on Advanced Information Technologies (ICAIT). :182—187.

Since a lot of information is outsourcing into cloud servers, data confidentiality becomes a higher risk to service providers. To assure data security, Ciphertext Policy Attributes-Based Encryption (CP-ABE) is observed for the cloud environment. Because ciphertexts and secret keys are relying on attributes, the revocation issue becomes a challenge for CP-ABE. This paper proposes an encryption access control (EAC) scheme to fulfill policy revocation which covers both attribute and user revocation. When one of the attributes in an access policy is changed by the data owner, the authorized users should be updated immediately because the revoked users who have gained previous access policy can observe the ciphertext. Especially for data owners, four types of updating policy levels are predefined. By classifying those levels, each secret token key is distinctly generated for each level. Consequently, a new secret key is produced by hashing the secret token key. This paper analyzes the execution times of key generation, encryption, and decryption times between non-revocation and policy revocation cases. Performance analysis for policy revocation is also presented in this paper.

Ghosh, Bedatrayee, Parimi, Priyanka, Rout, Rashmi Ranjan.  2020.  Improved Attribute-Based Encryption Scheme in Fog Computing Environment for Healthcare Systems. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—6.

In today's smart healthcare system, medical records of patients are exposed to a large number of users for various purposes, from monitoring the patients' health to data analysis. Preserving the privacy of a patient has become an important and challenging issue. outsourced Ciphertext-Policy Attribute-Based Encryption (CP-ABE) provides a solution for the data sharing and privacy preservation problem in the healthcare system in fog environment. However, the high computational cost in case of frequent attribute updates renders it infeasible for providing access control in healthcare systems. In this paper, we propose an efficient method to overcome the frequent attribute update problem of outsourced CP-ABE. In our proposed approach, we generate two keys for each user (a static key and a dynamic key) based on the constant and changing attributes of the users. Therefore, in case of an attribute change for a user, only the dynamic key is updated. Also, the key update is done at the fog nodes without compromising the security of the system. Thus, both the communication and the computational overhead associated with the key update in the outsourced CP-ABE scheme are reduced, making it an ideal solution for data access control in healthcare systems. The efficacy of our proposed approach is shown through theoretical analysis and experimentation.

Zhengbo, Chen, Xiu, Liu, Yafei, Xing, Miao, Hu, Xiaoming, Ju.  2020.  Markov Encrypted Data Prefetching Model Based On Attribute Classification. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :54—59.

In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.

2021-05-25
Bakhtiyor, Abdurakhimov, Zarif, Khudoykulov, Orif, Allanov, Ilkhom, Boykuziev.  2020.  Algebraic Cryptanalysis of O'zDSt 1105:2009 Encryption Algorithm. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—7.
In this paper, we examine algebraic attacks on the O'zDSt 1105:2009. We begin with a brief review of the meaning of algebraic cryptanalysis, followed by an algebraic cryptanalysis of O'zDSt 1105:2009. Primarily O'zDSt 1105:2009 encryption algorithm is decomposed and each transformation in it is algebraic described separately. Then input and output of each transformation are expressed with other transformation, encryption key, plaintext and cipher text. Created equations, unknowns on it and degree of unknowns are analyzed, and then overall result is given. Based on experimental results, it is impossible to save all system of equations that describes all transformations in O'zDSt 1105:2009 standard. Because, this task requires 273 bytes for the second round. For this reason, it is advisable to evaluate the parameters of the system of algebraic equations, representing the O'zDSt 1105:2009 standard, theoretically.
Tashev, Komil, Rustamova, Sanobar.  2020.  Analysis of Subject Recognition Algorithms based on Neural Networks. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—4.
This article describes the principles of construction, training and use of neural networks. The features of the neural network approach are indicated, as well as the range of tasks for which it is most preferable. Algorithms of functioning, software implementation and results of work of an artificial neural network are presented.
Karimov, Madjit, Tashev, Komil, Rustamova, Sanobar.  2020.  Application of the Aho-Corasick algorithm to create a network intrusion detection system. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—5.
One of the main goals of studying pattern matching techniques is their significant role in real-world applications, such as the intrusion detection systems branch. The purpose of the network attack detection systems NIDS is to protect the infocommunication network from unauthorized access. This article provides an analysis of the exact match and fuzzy matching methods, and discusses a new implementation of the classic Aho-Korasik pattern matching algorithm at the hardware level. The proposed approach to the implementation of the Aho-Korasik algorithm can make it possible to ensure the efficient use of resources, such as memory and energy.
Ajorlou, Amir, Abbasfar, Aliazam.  2020.  An Optimized Structure of State Channel Network to Improve Scalability of Blockchain Algorithms. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :73—76.
Nowadays, blockchain is very common and widely used in various fields. The properties of blockchain-based algorithms such as being decentralized and uncontrolled by institutions and governments, are the main reasons that has attracted many applications. The security and the scalability limitations are the main challenges for the development of these systems. Using second layer network is one of the various methods proposed to improve the scalability of these systems. This network can increase the total number of transactions per second by creating extra channels between the nodes that operate in a different layer not obligated to be on consensus ledger. In this paper, the optimal structure for the second layer network has been presented. In the proposed structure we try to distribute the parameters of the second layer network as symmetrically as possible. To prove the optimality of this structure we first introduce the maximum scalability bound, and then calculate it for the proposed structure. This paper will show how the second layer method can improve the scalability without any information about the rate of transactions between nodes.
Satılmış, Hami, Akleylek, Sedat.  2020.  Efficient Implementation of HashSieve Algorithm for Lattice-Based Cryptography. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :75—79.
The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven's standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.
ÇELİK, Mahmut, ALKAN, Mustafa, ALKAN, Abdulkerim Oğuzhan.  2020.  Protection of Personal Data Transmitted via Web Service Against Software Developers. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :88—92.
Through the widespread use of information technologies, institutions have started to offer most of their services electronically. The best example of this is e-government. Since institutions provide their services to the electronic environment, the quality of the services they provide increases and their access to services becomes easier. Since personal information can be verified with inter-agency information sharing systems, wrong or unfair transactions can be prevented. Since information sharing between institutions is generally done through web services, protection of personal data transmitted via web services is of great importance. There are comprehensive national and international regulations on the protection of personal data. According to these regulations, protection of personal data shared between institutions is a legal obligation; protection of personal data is an issue that needs to be handled comprehensively. This study, protection of personal data shared between institutions through web services against software developers is discussed. With a proposed application, it is aimed to take a new security measure for the protection of personal data. The proposed application consists of a web interface prepared using React and Java programming languages and rest services that provide anonymization of personal data.
[Anonymous].  2020.  B-DCT based Watermarking Algorithm for Patient Data Protection in IoMT. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :1—4.
Internet of Medical Things (IoMT) is the connection between medical devices and information systems to share, collect, process, store, and integrate patient and health data using network technologies. X-Rays, MR, MRI, and CT scans are the most frequently used patient medical image data. These images usually include patient information in one of the corners of the image. In this research work, to protect patient information, a new robust and secure watermarking algorithm developed for a selected region of interest (ROI) of medical images. First ROI selected from the medical image, then selected part divided equal blocks and applied Discrete Cosine Transformation (DCT) algorithm to embed a watermark into the selected coefficients. Several geometric and removal attacks are applied to the watermarked multimedia element such as lossy image compression, the addition of Gaussian noise, denoising, filtering, median filtering, sharpening, contrast enhancement, JPEG compression, and rotation. Experimental results show very promising results in PSNR and similarity ratio (SR) values after blocked DCT (B-DCT) based embedding algorithm against the Discrete Wavelet Transformation (DWT), Least Significant Bits (LSB) and DCT algorithms.
Susilo, Willy, Duong, Dung Hoang, Le, Huy Quoc.  2020.  Efficient Post-quantum Identity-based Encryption with Equality Test. 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). :633—640.
Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.'s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.
Ahmedova, Oydin, Mardiyev, Ulugbek, Tursunov, Otabek.  2020.  Generation and Distribution Secret Encryption Keys with Parameter. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—4.
This article describes a new way to generate and distribute secret encryption keys, in which the processes of generating a public key and formicating a secret encryption key are performed in algebra with a parameter, the secrecy of which provides increased durability of the key.
AKCENGİZ, Ziya, Aslan, Melis, Karabayır, Özgür, Doğanaksoy, Ali, Uğuz, Muhiddin, Sulak, Fatih.  2020.  Statistical Randomness Tests of Long Sequences by Dynamic Partitioning. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :68—74.
Random numbers have a wide usage in the area of cryptography. In practice, pseudo random number generators are used in place of true random number generators, as regeneration of them may be required. Therefore because of generation methods of pseudo random number sequences, statistical randomness tests have a vital importance. In this paper, a randomness test suite is specified for long binary sequences. In literature, there are many randomness tests and test suites. However, in most of them, to apply randomness test, long sequences are partitioned into a certain fixed length and the collection of short sequences obtained is evaluated instead. In this paper, instead of partitioning a long sequence into fixed length subsequences, a concept of dynamic partitioning is introduced in accordance with the random variable in consideration. Then statistical methods are applied. The suggested suite, containing four statistical tests: Collision Tests, Weight Test, Linear Complexity Test and Index Coincidence Test, all of them work with the idea of dynamic partitioning. Besides the adaptation of this approach to randomness tests, the index coincidence test is another contribution of this work. The distribution function and the application of all tests are given in the paper.
Diao, Yiqing, Ye, Ayong, Cheng, Baorong, Zhang, Jiaomei, Zhang, Qiang.  2020.  A Dummy-Based Privacy Protection Scheme for Location-Based Services under Spatiotemporal Correlation. 2020 International Conference on Networking and Network Applications (NaNA). :443—447.
The dummy-based method has been commonly used to protect the users location privacy in location-based services, since it can provide precise results and generally do not rely on a third party or key sharing. However, the close spatiotemporal correlation between the consecutively reported locations enables the adversary to identify some dummies, which lead to the existing dummy-based schemes fail to protect the users location privacy completely. To address this limit, this paper proposes a new algorithm to produce dummy location by generating dummy trajectory, which naturally takes into account of the spatiotemporal correlation all round. Firstly, the historical trajectories similar to the user's travel route are chosen as the dummy trajectories which depend on the distance between two trajectories with the help of home gateway. Then, the dummy is generated from the dummy trajectory by taking into account of time reachability, historical query similarity and the computation of in-degree/out-degree. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the users real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the users location privacy effectively and efficiently.
2021-05-20
Neema, Himanshu, Sztipanovits, Janos, Hess, David J., Lee, Dasom.  2020.  TE-SAT: Transactive Energy Simulation and Analysis Toolsuite. 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION). :19—20.

Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backend server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments.

2021-05-05
Chalkiadakis, Nikolaos, Deyannis, Dimitris, Karnikis, Dimitris, Vasiliadis, Giorgos, Ioannidis, Sotiris.  2020.  The Million Dollar Handshake: Secure and Attested Communications in the Cloud. 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). :63—70.

The number of applications and services that are hosted on cloud platforms is constantly increasing. Nowadays, more and more applications are hosted as services on cloud platforms, co-existing with other services in a mutually untrusted environment. Facilities such as virtual machines, containers and encrypted communication channels aim to offer isolation between the various applications and protect sensitive user data. However, such techniques are not always able to provide a secure execution environment for sensitive applications nor they offer guarantees that data are not monitored by an honest but curious provider once they reach the cloud infrastructure. The recent advancements of trusted execution environments within commodity processors, such as Intel SGX, provide a secure reverse sandbox, where code and data are isolated even from the underlying operating system. Moreover, Intel SGX provides a remote attestation mechanism, allowing the communicating parties to verify their identity as well as prove that code is executed on hardware-assisted software enclaves. Many approaches try to ensure code and data integrity, as well as enforce channel encryption schemes such as TLS, however, these techniques are not enough to achieve complete isolation and secure communications without hardware assistance or are not efficient in terms of performance. In this work, we design and implement a practical attestation system that allows the service provider to offer a seamless attestation service between the hosted applications and the end clients. Furthermore, we implement a novel caching system that is capable to eliminate the latencies introduced by the remote attestation process. Our approach allows the parties to attest one another before each communication attempt, with improved performance when compared to a standard TLS handshake.

Coulter, Rory, Zhang, Jun, Pan, Lei, Xiang, Yang.  2020.  Unmasking Windows Advanced Persistent Threat Execution. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :268—276.

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.

Poudyal, Subash, Dasgupta, Dipankar.  2020.  AI-Powered Ransomware Detection Framework. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :1154—1161.

Ransomware attacks are taking advantage of the ongoing pandemics and attacking the vulnerable systems in business, health sector, education, insurance, bank, and government sectors. Various approaches have been proposed to combat ransomware, but the dynamic nature of malware writers often bypasses the security checkpoints. There are commercial tools available in the market for ransomware analysis and detection, but their performance is questionable. This paper aims at proposing an AI-based ransomware detection framework and designing a detection tool (AIRaD) using a combination of both static and dynamic malware analysis techniques. Dynamic binary instrumentation is done using PIN tool, function call trace is analyzed leveraging Cuckoo sandbox and Ghidra. Features extracted at DLL, function call, and assembly level are processed with NLP, association rule mining techniques and fed to different machine learning classifiers. Support vector machine and Adaboost with J48 algorithms achieved the highest accuracy of 99.54% with 0.005 false-positive rates for a multi-level combined term frequency approach.

Chi, Po-Wen, Wang, Ming-Hung, Zheng, Yu.  2020.  SandboxNet: An Online Malicious SDN Application Detection Framework for SDN Networking. 2020 International Computer Symposium (ICS). :397—402.

Software Defined Networking (SDN) is a concept that decouples the control plane and the user plane. So the network administrator can easily control the network behavior through its own programs. However, the administrator may unconsciously apply some malicious programs on SDN controllers so that the whole network may be under the attacker’s control. In this paper, we discuss the malicious software issue on SDN networks. We use the idea of sandbox to propose a sandbox network called SanboxNet. We emulate a virtual isolated network environment to verify the SDN application functions. With continuous monitoring, we can locate the suspicious SDN applications. We also consider the sandbox-evading issue in our framework. The emulated networks and the real world networks will be indistinguishable to the SDN controller.

Singh, Sukhpreet, Jagdev, Gagandeep.  2020.  Execution of Big Data Analytics in Automotive Industry using Hortonworks Sandbox. 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). :158—163.

The market landscape has undergone dramatic change because of globalization, shifting marketing conditions, cost pressure, increased competition, and volatility. Transforming the operation of businesses has been possible because of the astonishing speed at which technology has witnessed the change. The automotive industry is on the edge of a revolution. The increased customer expectations, changing ownership, self-driving vehicles and much more have led to the transformation of automobiles, applications, and services from artificial intelligence, sensors, RFID to big data analysis. Large automobiles industries have been emphasizing the collection of data to gain insight into customer's expectations, preferences, and budgets alongside competitor's policies. Statistical methods can be applied to historical data, which has been gathered from various authentic sources and can be used to identify the impact of fixed and variable marketing investments and support automakers to come up with a more effective, precise, and efficient approach to target customers. Proper analysis of supply chain data can disclose the weak links in the chain enabling to adopt timely countermeasures to minimize the adverse effects. In order to fully gain benefit from analytics, the collaboration of a detailed set of capabilities responsible for intersecting and integrating with multiple functions and teams across the business is required. The effective role played by big data analysis in the automobile industry has also been expanded in the research paper. The research paper discusses the scope and challenges of big data. The paper also elaborates on the working technology behind the concept of big data. The paper illustrates the working of MapReduce technology that executes in the back end and is responsible for performing data mining.

Cano M, Jeimy J..  2020.  Sandbox: Revindicate failure as the foundation of learning. 2020 IEEE World Conference on Engineering Education (EDUNINE). :1—6.

In an increasingly asymmetric context of both instability and permanent innovation, organizations demand new capacities and learning patterns. In this sense, supervisors have adopted the metaphor of the "sandbox" as a strategy that allows their regulated parties to experiment and test new proposals in order to study them and adjust to the established compliance frameworks. Therefore, the concept of the "sandbox" is of educational interest as a way to revindicate failure as a right in the learning process, allowing students to think, experiment, ask questions and propose ideas outside the known theories, and thus overcome the mechanistic formation rooted in many of the higher education institutions. Consequently, this article proposes the application of this concept for educational institutions as a way of resignifying what students have learned.

Kumar, Rahul, Sethi, Kamalakanta, Prajapati, Nishant, Rout, Rashmi Ranjan, Bera, Padmalochan.  2020.  Machine Learning based Malware Detection in Cloud Environment using Clustering Approach. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—7.

Enforcing security and resilience in a cloud platform is an essential but challenging problem due to the presence of a large number of heterogeneous applications running on shared resources. A security analysis system that can detect threats or malware must exist inside the cloud infrastructure. Much research has been done on machine learning-driven malware analysis, but it is limited in computational complexity and detection accuracy. To overcome these drawbacks, we proposed a new malware detection system based on the concept of clustering and trend micro locality sensitive hashing (TLSH). We used Cuckoo sandbox, which provides dynamic analysis reports of files by executing them in an isolated environment. We used a novel feature extraction algorithm to extract essential features from the malware reports obtained from the Cuckoo sandbox. Further, the most important features are selected using principal component analysis (PCA), random forest, and Chi-square feature selection methods. Subsequently, the experimental results are obtained for clustering and non-clustering approaches on three classifiers, including Decision Tree, Random Forest, and Logistic Regression. The model performance shows better classification accuracy and false positive rate (FPR) as compared to the state-of-the-art works and non-clustering approach at significantly lesser computation cost.

Kishore, Pushkar, Barisal, Swadhin Kumar, Prasad Mohapatra, Durga.  2020.  JavaScript malware behaviour analysis and detection using sandbox assisted ensemble model. 2020 IEEE REGION 10 CONFERENCE (TENCON). :864—869.

Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyberworld. Attackers take advantage of the dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that are needed for creating the dataset. We compute Pearson's r between every feature for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

Đuranec, A., Gruičić, S., Žagar, M..  2020.  Forensic analysis of Windows 10 Sandbox. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1224—1229.

With each Windows operating system Microsoft introduces new features to its users. Newly added features present a challenge to digital forensics examiners as they are not analyzed or tested enough. One of the latest features, introduced in Windows 10 version 1909 is Windows Sandbox; a lightweight, temporary, environment for running untrusted applications. Because of the temporary nature of the Sandbox and insufficient documentation, digital forensic examiners are facing new challenges when examining this newly added feature which can be used to hide different illegal activities. Throughout this paper, the focus will be on analyzing different Windows artifacts and event logs, with various tools, left behind as a result of the user interaction with the Sandbox feature on a clear virtual environment. Additionally, the setup of testing environment will be explained, the results of testing and interpretation of the findings will be presented, as well as open-source tools used for the analysis.