Visible to the public Biblio

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2021-09-21
Wang, Duanyi, Shu, Hui, Kang, Fei, Bu, Wenjuan.  2020.  A Malware Similarity Analysis Method Based on Network Control Structure Graph. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :295–300.
Recently, graph-based malware similarity analysis has been widely used in the field of malware detection. However, the wide application of code obfuscation, polymorphism, and deformation changes the structure of malicious code, which brings great challenges to the malware similarity analysis. To solve these problems, in this paper, we present a new approach to malware similarity analysis based on the network control structure graph (NCSG). This method analyzed the behavior of malware by application program interface (API) association and constructed NCSG. The graph could reflect the command-and-control(C&C) logic of malware. Therefore, it can resist the interference of code obfuscation technology. The structural features extracted from NCSG will be used as the basis of similarity analysis for training the detection model. Finally, we tested the dataset constructed from five known malware family samples, and the experimental results showed that the accuracy of this method for malware variation analysis reached 92.75%. In conclusion, the malware similarity analysis based on NCSG has a strong application value for identifying the same family of malware.
2021-09-01
Hardin, David S..  2020.  Verified Hardware/Software Co-Assurance: Enhancing Safety and Security for Critical Systems. 2020 IEEE International Systems Conference (SysCon). :1—6.
Experienced developers of safety-critical and security-critical systems have long emphasized the importance of applying the highest degree of scrutiny to a system's I/O boundaries. From a safety perspective, input validation is a traditional “best practice.” For security-critical architecture and design, identification of the attack surface has emerged as a primary analysis technique. One of our current research focus areas concerns the identification of and mitigation against attacks along that surface, using mathematically-based tools. We are motivated in these efforts by emerging application areas, such as assured autonomy, that feature a high degree of network connectivity, require sophisticated algorithms and data structures, are subject to stringent accreditation/certification, and encourage hardware/software co-design approaches. We have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O'Leary, and originally designed for use in floating-point hardware verification, to determine its suitability for the creation of safety-critical/security-critical input filters. We focus first on software implementation, but extending to hardware as well as hardware/software co-designs. We have implemented a high-assurance filter for JSON-formatted data used in an Unmanned Aerial Vehicle (UAV) application. Our JSON filter is built using a table-driven lexer/parser, supported by mathematically-proven lexer and parser table generation technology, as well as verified data structures. Filter behavior is expressed in a subset of Algorithmic C, which defines a set of C++ header files providing support for hardware design, including the peculiar bit widths utilized in that discipline, and enables compilation to both hardware and software platforms. The Russinoff-O'Leary Restricted Algorithmic C (RAC) toolchain translates Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover; once in ACL2, filter behavior can be mathematically verified. We describe how we utilize RAC to translate our JSON filter to ACL2, present proofs of correctness for its associated data types, and describe validation and performance results obtained through the use of concrete test vectors.
2021-08-31
Salimboyevich, Olimov Iskandar, Absamat ugli, Boriyev Yusuf, Akmuratovich, Sadikov Mahmudjon.  2020.  Making algorithm of improved key generation model and software. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—3.
In this paper is devoted methods for generating keys for cryptographic algorithms. Hash algorithms were analysed and learned linear and nonlinear. It was made up improved key generation algorithm and software.
2021-08-18
Jha, Pallavi, Zorkta, Haythem Yosef, Allawi, Dahham, Al-Nakkar, Maher Riad.  2020.  Improved Lightweight Encryption Algorithm (ILEA). 2020 International Conference for Emerging Technology (INCET). :1—4.
Lightweight cryptography concept has been a very hot topic for the last few years and considered as a new domain of encryption suitable for big data networks, small devices, phones, cards and embedded systems. These systems require low latency security and low power consuming [1]. An improved lightweight encryption algorithm ILEA is proposed in this paper. ILEA is based on PRINCE lightweight algorithm as his main core with two defacing balanced mixing layers added. ILEA presented in two programming languages: PYTHON, C++ with a comparative study with original PRINCE results and some of another lightweight algorithms.
2021-08-11
Gaikwad, Nikhil B., Ugale, Hrishikesh, Keskar, Avinash, Shivaprakash, N. C..  2020.  The Internet-of-Battlefield-Things (IoBT)-Based Enemy Localization Using Soldiers Location and Gunshot Direction. IEEE Internet of Things Journal. 7:11725–11734.
The real-time information of enemy locations is capable to transform the outcome of combat operations. Such information gathered using connected soldiers on the Internet of Battlefield Things (IoBT) is highly beneficial to create situational awareness (SA) and to plan an effective war strategy. This article presents the novel enemy localization method that uses the soldier's own locations and their gunshot direction. The hardware prototype has been developed that uses a triangulation for an enemy localization in two soldiers and a single enemy scenario. 4.24±1.77 m of average localization error and ±4° of gunshot direction error has been observed during this prototype testing. This basic model is further extended using three-stage software simulation for multiple soldiers and multiple enemy scenarios with the necessary assumptions. The effective algorithm has been proposed, which differentiates between the ghost and true predictions by analyzing the groups of subsequent shooting intents (i.e., frames). Four different complex scenarios are tested in the first stage of the simulation, around three to six frames are required for the accurate enemy localization in the relatively simple cases, and nine frames are required for the complex cases. The random error within ±4° in gunshot direction is included in the second stage of the simulation which required almost double the number of frames for similar four cases. As the number of frames increases, the accuracy of the proposed algorithm improves and better ghost point elimination is observed. In the third stage, two conventional clustering algorithms are implemented to validate the presented work. The comparative analysis shows that the proposed algorithm is faster, computationally simple, consistent, and reliable compared with others. Detailed analysis of hardware and software results for various scenarios has been discussed in this article.
2021-05-25
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.
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.
2021-05-13
Everson, Douglas, Cheng, Long.  2020.  Network Attack Surface Simplification for Red and Blue Teams. 2020 IEEE Secure Development (SecDev). :74–80.
Network port scans are a key first step to developing a true understanding of a network-facing attack surface. However in large-scale networks, the data resulting from such scans can be too numerous for Red Teams to process for manual and semiautomatic testing. Indiscriminate port scans can also compromise a Red Team seeking to quickly gain a foothold on a network. A large attack surface can even complicate Blue Team activities like threat hunting. In this paper we provide a cluster analysis methodology designed to group similar hosts to reduce security team workload and Red Team observability. We also measure the Internet-facing network attack surface of 13 organizations by clustering their hosts based on similarity. Through a case study we demonstrate how the output of our clustering technique provides new insight to both Red and Blue Teams, allowing them to quickly identify potential high-interest points on the attack surface.
2021-04-27
Matthews, I., Mace, J., Soudjani, S., Moorsel, A. van.  2020.  Cyclic Bayesian Attack Graphs: A Systematic Computational Approach. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :129–136.
Attack graphs are commonly used to analyse the security of medium-sized to large networks. Based on a scan of the network and likelihood information of vulnerabilities, attack graphs can be transformed into Bayesian Attack Graphs (BAGs). These BAGs are used to evaluate how security controls affect a network and how changes in topology affect security. A challenge with these automatically generated BAGs is that cycles arise naturally, which make it impossible to use Bayesian network theory to calculate state probabilities. In this paper we provide a systematic approach to analyse and perform computations over cyclic Bayesian attack graphs. We present an interpretation of Bayesian attack graphs based on combinational logic circuits, which facilitates an intuitively attractive systematic treatment of cycles. We prove properties of the associated logic circuit and present an algorithm that computes state probabilities without altering the attack graphs (e.g., remove an arc to remove a cycle). Moreover, our algorithm deals seamlessly with any cycle without the need to identify their type. A set of experiments demonstrates the scalability of the algorithm on computer networks with hundreds of machines, each with multiple vulnerabilities.
2021-03-15
Khuchit, U., Wu, L., Zhang, X., Yin, Y., Batsukh, A., Mongolyn, B., Chinbat, M..  2020.  Hardware Design of Polynomial Multiplication for Byte-Level Ring-LWE Based Cryptosystem. 2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :86–89.
An ideal lattice is defined over a ring learning with errors (Ring-LWE) problem. Polynomial multiplication over the ring is the most computational and time-consuming block in lattice-based cryptography. This paper presents the first hardware design of the polynomial multiplication for LAC, one of the Round-2 candidates of the NIST PQC Standardization Process, which has byte-level modulus p=251. The proposed architecture supports polynomial multiplications for different degree n (n=512/1024/2048). For designing the scheme, we used the Vivado HLS compiler, a high-level synthesis based hardware design methodology, which is able to optimize software algorithms into actual hardware products. The design of the scheme takes 274/280/291 FFs and 204/217/208 LUTs on the Xilinx Artix-7 family FPGA, requested by NIST PQC competition for hardware implementation. Multiplication core uses only 1/1/2 pieces of 18Kb BRAMs, 1/1/1 DSPs, and 90/94/95 slices on the board. Our timing result achieved in an alternative degree n with 5.052/4.3985/5.133ns.
2021-01-28
Salib, E. H., Aboutabl, M. S..  2020.  Hands-on Undergraduate Labs on Anonymity Cryptographic Algorithms. 2020 IEEE Frontiers in Education Conference (FIE). :1—9.

This is an innovative practice full paper. In past projects, we have successfully used a private TOR (anonymity network) platform that enabled our students to explore the end-to-end inner workings of the TOR anonymity network through a number of controlled hands-on lab assignments. These have saisfied the needs of curriculum focusing on networking functions and algorithms. To be able to extend the use and application of the private TOR platform into cryptography courses, there is a desperate need to enhance the platform to allow the development of hands-on lab assignments on the cryptographic algorithms and methods utilized in the creation of TOR secure connections and end-to-end circuits for anonymity.In tackling this challenge, and since TOR is open source software, we identify the cryptographic functions called by the TOR algorithms in the process of establishing TLS connections and creating end-to-end TOR circuits as well tearing them down. We instrumented these functions with the appropriate code to log the cryptographic keys dynamically created at all nodes involved in the creation of the end to end circuit between the Client and the exit relay (connected to the target server).We implemented a set of pedagogical lab assignments on a private TOR platform and present them in this paper. Using these assignments, students are able to investigate and validate the cryptographic procedures applied in the establishment of the initial TLS connection, the creation of the first leg of a TOR circuit, as well as extending the circuit through additional relays (at least two relays). More advanced assignments are created to challenge the students to unwrap the traffic sent from the Client to the exit relay at all onion skin layers and compare it with the actual traffic delivered to the target server.

Krasnov, A. N., Prakhova, M. Y., Novikova, U. V..  2020.  Ensuring Cybersecurity of Data Transmission in Limited Energy Consumption Networks. 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). :1—5.

In the northern gas fields, most data are transmitted via wireless networks, which requires special transmission security measures. Herewith, the gas field infrastructure dictates cybersecurity modules to not only meet standard requirements but also ensure reduced energy consumption. The paper discusses the issue of building such a module for a process control system based on the RTP-04M recorder operating in conjunction with an Android-based mobile device. The software options used for the RSA and Diffie-Hellman data encryption and decryption algorithms on both the RTP-04M and the Android-based mobile device sides in the Keil μVision4 and Android Studio software environments, respectively, have shown that the Diffie-Hellman algorithm is preferable. It provides significant savings in RAM and CPU resources and power consumption of the recorder. In terms of energy efficiency, the implemented programs have been analyzed in the Android Studio (Android Profiler) and Simplicity Studio (Advanced Energy Monitor) environments. The integration of this module into the existing software will improve the field's PCS cybersecurity level due to protecting data transmitted from third-party attacks.

2021-01-25
Abbas, M. S., Mahdi, S. S., Hussien, S. A..  2020.  Security Improvement of Cloud Data Using Hybrid Cryptography and Steganography. 2020 International Conference on Computer Science and Software Engineering (CSASE). :123–127.
One of the significant advancements in information technology is Cloud computing, but the security issue of data storage is a big problem in the cloud environment. That is why a system is proposed in this paper for improving the security of cloud data using encryption, information concealment, and hashing functions. In the data encryption phase, we implemented hybrid encryption using the algorithm of AES symmetric encryption and the algorithm of RSA asymmetric encryption. Next, the encrypted data will be hidden in an image using LSB algorithm. In the data validation phase, we use the SHA hashing algorithm. Also, in our suggestion, we compress the data using the LZW algorithm before hiding it in the image. Thus, it allows hiding as much data as possible. By using information concealment technology and mixed encryption, we can achieve strong data security. In this paper, PSNR and SSIM values were calculated in addition to the graph to evaluate the image masking performance before and after applying the compression process. The results showed that PSNR values of stego-image are better for compressed data compared to data before compression.
Abusukhon, A., AlZu’bi, S..  2020.  New Direction of Cryptography: A Review on Text-to-Image Encryption Algorithms Based on RGB Color Value. 2020 Seventh International Conference on Software Defined Systems (SDS). :235–239.
Data encryption techniques are important for answering the question: How secure is the Internet for sending sensitive data. Keeping data secure while they are sent through the global network is a difficult task. This is because many hackers are fishing these data in order to get some benefits. The researchers have developed various types of encryption algorithms to protect data from attackers. These algorithms are mainly classified into two categories namely symmetric and asymmetric encryption algorithms. This survey sheds light on the recent work carried out on encrypting a text into an image based on the RGB color value and held a comparison between them based on various factors evolved from the literature.
2020-11-02
Huang, S., Chen, Q., Chen, Z., Chen, L., Liu, J., Yang, S..  2019.  A Test Cases Generation Technique Based on an Adversarial Samples Generation Algorithm for Image Classification Deep Neural Networks. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :520–521.

With widely applied in various fields, deep learning (DL) is becoming the key driving force in industry. Although it has achieved great success in artificial intelligence tasks, similar to traditional software, it has defects that, once it failed, unpredictable accidents and losses would be caused. In this paper, we propose a test cases generation technique based on an adversarial samples generation algorithm for image classification deep neural networks (DNNs), which can generate a large number of good test cases for the testing of DNNs, especially in case that test cases are insufficient. We briefly introduce our method, and implement the framework. We conduct experiments on some classic DNN models and datasets. We further evaluate the test set by using a coverage metric based on states of the DNN.

Zhao, Xinghan, Gao, Xiangfei.  2018.  An AI Software Test Method Based on Scene Deductive Approach. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :14—20.
Artificial intelligence (AI) software has high algorithm complexity, and the scale and dimension of the input and output parameters are high, and the test oracle isn't explicit. These features make a lot of difficulties for the design of test cases. This paper proposes an AI software testing method based on scene deductive approach. It models the input, output parameters and the environment, uses the random algorithm to generate the inputs of the test cases, then use the algorithm of deductive approach to make the software testing automatically, and use the test assertions to verify the results of the test. After description of the theory, this paper uses intelligent tracking car as an example to illustrate the application of this method and the problems needing attention. In the end, the paper describes the shortcoming of this method and the future research directions.
2020-10-26
Walker, Aaron, Sengupta, Shamik.  2019.  Insights into Malware Detection via Behavioral Frequency Analysis Using Machine Learning. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1–6.
The most common defenses against malware threats involves the use of signatures derived from instances of known malware. However, the constant evolution of the malware threat landscape necessitates defense against unknown malware, making a signature catalog of known threats insufficient to prevent zero-day vulnerabilities from being exploited. Recent research has applied machine learning approaches to identify malware through artifacts of malicious activity as observed through dynamic behavioral analysis. We have seen that these approaches mimic common malware defenses by simply offering a method of detecting known malware. We contribute a new method of identifying software as malicious or benign through analysis of the frequency of Windows API system function calls. We show that this is a powerful technique for malware detection because it generates learning models which understand the difference between malicious and benign software, rather than producing a malware signature classifier. We contribute a method of systematically comparing machine learning models against different datasets to determine their efficacy in accurately distinguishing the difference between malicious and benign software.
Sun, Pengfei, Garcia, Luis, Zonouz, Saman.  2019.  Tell Me More Than Just Assembly! Reversing Cyber-Physical Execution Semantics of Embedded IoT Controller Software Binaries. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :349–361.
The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.
2020-09-28
Shen, Jingyi, Baysal, Olga, Shafiq, M. Omair.  2019.  Evaluating the Performance of Machine Learning Sentiment Analysis Algorithms in Software Engineering. 2019 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). :1023–1030.
In recent years, sentiment analysis has been aware within software engineering domain. While automated sentiment analysis has long been suffering from doubt of accuracy, the tool performance is unstable when being applied on datasets other than the original dataset for evaluation. Researchers also have the disagreements upon if machine learning algorithms perform better than conventional lexicon and rule based approaches. In this paper, we looked into the factors in datasets that may affect the evaluation performance, also evaluated the popular machine learning algorithms in sentiment analysis, then proposed a novel structure for automated sentiment tool combines advantages from both approaches.
2020-09-04
Hayashi, Masayoshi, Higaki, Hiroaki.  2018.  Security Improvement of Common-Key Cryptographic Communication by Mixture of Fake Plain- Texts. 2018 Second World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :151—157.
One of the fundamental methods for eavesdroppers to achieve a plaintext from a cryptogram is the brute force attack where possible candidates of decryption keys are exhaustively applied to the decryption algorithm. Here the only reason why the eavesdroppers believe to find the common-key and to achieve the plaintext is that the output of the decryption algorithm is contextually acceptable. According to this fact, this paper proposes a novel common-key cryptosystem where fake plaintexts which are also contextually acceptable are mixed into a cryptogram with the legal plaintext. If an eavesdropper applies a fake common-key to the decryption algorithm, it outputs the fake plaintexts which the eavesdroppers might believe legal. This paper also proposes concrete encryption/decryption algorithm which can be combined with any conventional common-key cryptosystem. Results of simulation experiments show the proposed method reduces probability for eavesdroppers to get legal plaintexts.
2020-07-30
Srisopha, Kamonphop, Phonsom, Chukiat, Lin, Keng, Boehm, Barry.  2019.  Same App, Different Countries: A Preliminary User Reviews Study on Most Downloaded iOS Apps. 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). :76—80.
Prior work on mobile app reviews has demonstrated that user reviews contain a wealth of information and are seen as a potential source of requirements. However, most of the studies done in this area mainly focused on mining and analyzing user reviews from the US App Store, leaving reviews of users from other countries unexplored. In this paper, we seek to understand if the perception of the same apps between users from other countries and that from the US differs through analyzing user reviews. We retrieve 300,643 user reviews of the 15 most downloaded iOS apps of 2018, published directly by Apple, from nine English-speaking countries over the course of 5 months. We manually classify 3,358 reviews into several software quality and improvement factors. We leverage a random forest based algorithm to identify factors that can be used to differentiate reviews between the US and other countries. Our preliminary results show that all countries have some factors that are proportionally inconsistent with the US.
2020-05-18
Sharma, Sarika, Kumar, Deepak.  2019.  Agile Release Planning Using Natural Language Processing Algorithm. 2019 Amity International Conference on Artificial Intelligence (AICAI). :934–938.
Once the requirement is gathered in agile, it is broken down into smaller pre-defined format called user stories. These user stories are then scoped in various sprint releases and delivered accordingly. Release planning in Agile becomes challenging when the number of user stories goes up in hundreds. In such scenarios it is very difficult to manually identify similar user stories and package them together into a release. Hence, this paper suggests application of natural language processing algorithms for identifying similar user stories and then scoping them into a release This paper takes the approach to build a word corpus for every project release identified in the project and then to convert the provided user stories into a vector of string using Java utility for calculating top 3 most occurring words from the given project corpus in a user story. Once all the user stories are represented as vector array then by using RV coefficient NLP algorithm the user stories are clustered into various releases of the software project. Using the proposed approach, the release planning for large and complex software engineering projects can be simplified resulting into efficient planning in less time. The automated commercial tools like JIRA and Rally can be enhanced to include suggested algorithms for managing release planning in Agile.
2020-04-17
Bicakci, Kemal, Ak, Ihsan Kagan, Ozdemir, Betul Askin, Gozutok, Mesut.  2019.  Open-TEE is No Longer Virtual: Towards Software-Only Trusted Execution Environments Using White-Box Cryptography. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :177—183.

Trusted Execution Environments (TEEs) provide hardware support to isolate the execution of sensitive operations on mobile phones for improved security. However, they are not always available to use for application developers. To provide a consistent user experience to those who have and do not have a TEE-enabled device, we could get help from Open-TEE, an open-source GlobalPlatform (GP)-compliant software TEE emulator. However, Open-TEE does not offer any of the security properties hardware TEEs have. In this paper, we propose WhiteBox-TEE which integrates white-box cryptography with Open-TEE to provide better security while still remaining complaint with GP TEE specifications. We discuss the architecture, provisioning mechanism, implementation highlights, security properties and performance issues of WhiteBox-TEE and propose possible revisions to TEE specifications to have better use of white-box cryptography in software-only TEEs.

2020-01-20
Myzdrikov, Nikita Ye., Semeonov, Ivan Ye., Yukhnov, Vasiliy I., Safaryan, Olga A., Reshetnikova, Irina V., Lobodenko, Andrey G., Cherckesova, Larissa V., Porksheyan, Vitaliy M..  2019.  Modification and Optimization of Solovey-Strassen's Fast Exponentiation Probablistic Test Binary Algorithm. 2019 IEEE East-West Design Test Symposium (EWDTS). :1–3.

This article will consider the probability test of Solovey-Strassen, to determine the simplicity of the number and its possible modifications. This test allows for the shortest possible time to determine whether the number is prime or not. C\# programming language was used to implement the algorithm in practice.

2019-12-09
Khokhlov, Igor, Jain, Chinmay, Miller-Jacobson, Ben, Heyman, Andrew, Reznik, Leonid, Jacques, Robert St..  2018.  MeetCI: A Computational Intelligence Software Design Automation Framework. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-8.

Computational Intelligence (CI) algorithms/techniques are packaged in a variety of disparate frameworks/applications that all vary with respect to specific supported functionality and implementation decisions that drastically change performance. Developers looking to employ different CI techniques are faced with a series of trade-offs in selecting the appropriate library/framework. These include resource consumption, features, portability, interface complexity, ease of parallelization, etc. Considerations such as language compatibility and familiarity with a particular library make the choice of libraries even more difficult. The paper introduces MeetCI, an open source software framework for computational intelligence software design automation that facilitates the application design decisions and their software implementation process. MeetCI abstracts away specific framework details of CI techniques designed within a variety of libraries. This allows CI users to benefit from a variety of current frameworks without investigating the nuances of each library/framework. Using an XML file, developed in accordance with the specifications, the user can design a CI application generically, and utilize various CI software without having to redesign their entire technology stack. Switching between libraries in MeetCI is trivial and accessing the right library to satisfy a user's goals can be done easily and effectively. The paper discusses the framework's use in design of various applications. The design process is illustrated with four different examples from expert systems and machine learning domains, including the development of an expert system for security evaluation, two classification problems and a prediction problem with recurrent neural networks.