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Saarinen, Markku-Juhani Olavi.  2017.  Ring-LWE Ciphertext Compression and Error Correction: Tools for Lightweight Post-Quantum Cryptography. Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security. :15–22.

Some lattice-based public key cryptosystems allow one to transform ciphertext from one lattice or ring representation to another efficiently and without knowledge of public and private keys. In this work we explore this lattice transformation property from cryptographic engineering viewpoint. We apply ciphertext transformation to compress Ring-LWE ciphertexts and to enable efficient decryption on an ultra-lightweight implementation targets such as Internet of Things, Smart Cards, and RFID applications. Significantly, this can be done without modifying the original encryption procedure or its security parameters. Such flexibility is unique to lattice-based cryptography and may find additional, unique real-life applications. Ciphertext compression can significantly increase the probability of decryption errors. We show that the frequency of such errors can be analyzed, measured and used to derive precise failure bounds for n-bit error correction. We introduce XECC, a fast multi-error correcting code that allows constant time implementation in software. We use these tools to construct and explore TRUNC8, a concrete Ring-LWE encryption and authentication system. We analyze its implementation, security, and performance. We show that our lattice compression technique reduces ciphertext size by more than 40% at equivalent security level, while also enabling public key cryptography on previously unreachable ultra-lightweight platforms. The experimental public key encryption and authentication system has been implemented on an 8-bit AVR target, where it easily outperforms elliptic curve and RSA-based proposals at similar security level. Similar results have been obtained with a Cortex M0 implementation. The new decryption code requires only a fraction of the software footprint of previous Ring-LWE implementations with the same encryption parameters, and is well suited for hardware implementation.

Saavedra Benitez, Y.I., Ben-Othman, J., Claude, J.-P..  2014.  Performance evaluation of security mechanisms in RAOLSR protocol for Wireless Mesh Networks. Communications (ICC), 2014 IEEE International Conference on. :1808-1812.

In this paper, we have proposed the IBE-RAOLSR and ECDSA-RAOLSR protocols for WMNs (Wireless Mesh Networks), which contributes to security routing protocols. We have implemented the IBE (Identity Based Encryption) and ECDSA (Elliptic Curve Digital Signature Algorithm) methods to secure messages in RAOLSR (Radio Aware Optimized Link State Routing), namely TC (Topology Control) and Hello messages. We then compare the ECDSA-based RAOLSR with IBE-based RAOLSR protocols. This study shows the great benefits of the IBE technique in securing RAOLSR protocol for WMNs. Through extensive ns-3 (Network Simulator-3) simulations, results have shown that the IBE-RAOLSR outperforms the ECDSA-RAOLSR in terms of overhead and delay. Simulation results show that the utilize of the IBE-based RAOLSR provides a greater level of security with light overhead.

Sabaliauskaite, G., Mathur, A.P..  2014.  Countermeasures to Enhance Cyber-physical System Security and Safety. Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International. :13-18.

An application of two Cyber-Physical System (CPS) security countermeasures - Intelligent Checker (IC) and Cross-correlator - for enhancing CPS safety and achieving required CPS safety integrity level is presented. ICs are smart sensors aimed at detecting attacks in CPS and alerting the human operators. Cross-correlator is an anomaly detection technique for detecting deception attacks. We show how ICs could be implemented at three different CPS safety protection layers to maintain CPS in a safe state. In addition, we combine ICs with the cross-correlator technique to assure high probability of failure detection. Performance simulations show that a combination of these two security countermeasures is effective in detecting and mitigating CPS failures, including catastrophic failures.

Sabbagh, Majid, Gongye, Cheng, Fei, Yunsi, Wang, Yanzhi.  2019.  Evaluating Fault Resiliency of Compressed Deep Neural Networks. 2019 IEEE International Conference on Embedded Software and Systems (ICESS). :1–7.

Model compression is considered to be an effective way to reduce the implementation cost of deep neural networks (DNNs) while maintaining the inference accuracy. Many recent studies have developed efficient model compression algorithms and implementations in accelerators on various devices. Protecting integrity of DNN inference against fault attacks is important for diverse deep learning enabled applications. However, there has been little research investigating the fault resilience of DNNs and the impact of model compression on fault tolerance. In this work, we consider faults on different data types and develop a simulation framework for understanding the fault resiliency of compressed DNN models as compared to uncompressed models. We perform our experiments on two common DNNs, LeNet-5 and VGG16, and evaluate their fault resiliency with different types of compression. The results show that binary quantization can effectively increase the fault resilience of DNN models by 10000x for both LeNet5 and VGG16. Finally, we propose software and hardware mitigation techniques to increase the fault resiliency of DNN models.

Sabek, I., Chandramouli, B., Minhas, U. F..  2019.  CRA: Enabling Data-Intensive Applications in Containerized Environments. 2019 IEEE 35th International Conference on Data Engineering (ICDE). :1762—1765.
Today, a modern data center hosts a wide variety of applications comprising batch, interactive, machine learning, and streaming applications. In this paper, we factor out the commonalities in a large majority of these applications, into a generic dataflow layer called Common Runtime for Applications (CRA). In parallel, another trend, with containerization technologies (e.g., Docker), has taken a serious hold on cloud-scale data centers, with direct implications on building next generation of data center applications. Container orchestrators (e.g., Kubernetes) have made deployment a lot easy, and they solve many infrastructure level problems, e.g., service discovery, auto-restart, and replication. For best in class performance, there is a need to marry the next generation applications with containerization technologies. To that end, CRA leverages and builds upon the containerization and resource orchestration capabilities of Kubernetes/Docker, and makes it easy to build a wide range of cloud-edge applications on top. To the best of our knowledge, we are the first to present a cloud native runtime for building data center applications. We show the efficiency of CRA through various micro-benchmarking experiments.
Sabillon, Regner, Serra-Ruiz, Jordi, Cavaller, Victor, Cano, Jeimy.  2017.  A Comprehensive Cybersecurity Audit Model to Improve Cybersecurity Assurance: The CyberSecurity Audit Model (CSAM). 2017 International Conference on Information Systems and Computer Science (INCISCOS). :253—259.

Nowadays, private corporations and public institutions are dealing with constant and sophisticated cyberthreats and cyberattacks. As a general warning, organizations must build and develop a cybersecurity culture and awareness in order to defend against cybercriminals. Information Technology (IT) and Information Security (InfoSec) audits that were efficient in the past, are trying to converge into cybersecurity audits to address cyber threats, cyber risks and cyberattacks that evolve in an aggressive cyber landscape. However, the increase in number and complexity of cyberattacks and the convoluted cyberthreat landscape is challenging the running cybersecurity audit models and putting in evidence the critical need for a new cybersecurity audit model. This article reviews the best practices and methodologies of global leaders in the cybersecurity assurance and audit arena. By means of the analysis of the current approaches and theoretical background, their real scope, strengths and weaknesses are highlighted looking forward a most efficient and cohesive synthesis. As a resut, this article presents an original and comprehensive cybersecurity audit model as a proposal to be utilized for conducting cybersecurity audits in organizations and Nation States. The CyberSecurity Audit Model (CSAM) evaluates and validates audit, preventive, forensic and detective controls for all organizational functional areas. CSAM has been tested, implemented and validated along with the Cybersecurity Awareness TRAining Model (CATRAM) in a Canadian higher education institution. A research case study is being conducted to validate both models and the findings will be published accordingly.

Sabita Maharjan, Quanyan Zhu, University of Illinois at Urbana-Champaign, Yan Zhang, Stein Gjessing, Tamer Başar, University of Illinois at Urbana-Champaign.  2013.  Dependable Demand Response Management in Smart Grid: A Stackelberg Game Approach. IEEE Transactions on Smart Grid. 4(1)

Demand ResponseManagement (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists.We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies.We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability.

Saboor khan, Abdul, Shafi, Imran, Anas, Muhammad, Yousuf, Bilal M, Abbas, Muhammad Jamshed, Noor, Aqib.  2019.  Facial Expression Recognition using Discrete Cosine Transform Artificial Neural Network. 2019 22nd International Multitopic Conference (INMIC). :1—5.

Every so often Humans utilize non-verbal gestures (e.g. facial expressions) to express certain information or emotions. Moreover, countless face gestures are expressed throughout the day because of the capabilities possessed by humans. However, the channels of these expression/emotions can be through activities, postures, behaviors & facial expressions. Extensive research unveiled that there exists a strong relationship between the channels and emotions which has to be further investigated. An Automatic Facial Expression Recognition (AFER) framework has been proposed in this work that can predict or anticipate seven universal expressions. In order to evaluate the proposed approach, Frontal face Image Database also named as Japanese Female Facial Expression (JAFFE) is opted as input. This database is further processed with a frequency domain technique known as Discrete Cosine transform (DCT) and then classified using Artificial Neural Networks (ANN). So as to check the robustness of this novel strategy, the random trial of K-fold cross validation, leave one out and person independent methods is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application.

Sabu, R., Yasuda, K., Kato, R., Kawaguchi, S., Iwata, H..  2020.  Does visual search by neck motion improve hemispatial neglect?: An experimental study using an immersive virtual reality system 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :262—267.

Unilateral spatial neglect (USN) is a higher cognitive dysfunction that can occur after a stroke. It is defined as an impairment in finding, reporting, reacting to, and directing stimuli opposite the damaged side of the brain. We have proposed a system to identify neglected regions in USN patients in three dimensions using three-dimensional virtual reality. The objectives of this study are twofold: first, to propose a system for numerically identifying the neglected regions using an object detection task in a virtual space, and second, to compare the neglected regions during object detection when the patient's neck is immobilized (‘fixed-neck’ condition) versus when the neck can be freely moved to search (‘free-neck’ condition). We performed the test using an immersive virtual reality system, once with the patient's neck fixed and once with the patient's neck free to move. Comparing the results of the study in two patients, we found that the neglected areas were similar in the fixed-neck condition. However, in the free-neck condition, one patient's neglect improved while the other patient’s neglect worsened. These results suggest that exploratory ability affects the symptoms of USN and is crucial for clinical evaluation of USN patients.

Saccente, Nicholas, Dehlinger, Josh, Deng, Lin, Chakraborty, Suranjan, Xiong, Yin.  2019.  Project Achilles: A Prototype Tool for Static Method-Level Vulnerability Detection of Java Source Code Using a Recurrent Neural Network. 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW). :114—121.

Software has become an essential component of modern life, but when software vulnerabilities threaten the security of users, new ways of analyzing for software security must be explored. Using the National Institute of Standards and Technology's Juliet Java Suite, containing thousands of examples of defective Java methods for a variety of vulnerabilities, a prototype tool was developed implementing an array of Long-Short Term Memory Recurrent Neural Networks to detect vulnerabilities within source code. The tool employs various data preparation methods to be independent of coding style and to automate the process of extracting methods, labeling data, and partitioning the dataset. The result is a prototype command-line utility that generates an n-dimensional vulnerability prediction vector. The experimental evaluation using 44,495 test cases indicates that the tool can achieve an accuracy higher than 90% for 24 out of 29 different types of CWE vulnerabilities.

Sachdeva, A., Kapoor, R., Sharma, A., Mishra, A..  2017.  Categorical Classification and Deletion of Spam Images on Smartphones Using Image Processing and Machine Learning. 2017 International Conference on Machine Learning and Data Science (MLDS). :23–30.

We regularly use communication apps like Facebook and WhatsApp on our smartphones, and the exchange of media, particularly images, has grown at an exponential rate. There are over 3 billion images shared every day on Whatsapp alone. In such a scenario, the management of images on a mobile device has become highly inefficient, and this leads to problems like low storage, manual deletion of images, disorganization etc. In this paper, we present a solution to tackle these issues by automatically classifying every image on a smartphone into a set of predefined categories, thereby segregating spam images from them, allowing the user to delete them seamlessly.

Sachidananda, Vinay, Siboni, Shachar, Shabtai, Asaf, Toh, Jinghui, Bhairav, Suhas, Elovici, Yuval.  2017.  Let the Cat Out of the Bag: A Holistic Approach Towards Security Analysis of the Internet of Things. Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security. :3–10.

The exponential increase of Internet of Things (IoT) devices have resulted in a range of new and unanticipated vulnerabilities associated with their use. IoT devices from smart homes to smart enterprises can easily be compromised. One of the major problems associated with the IoT is maintaining security; the vulnerable nature of IoT devices poses a challenge to many aspects of security, including security testing and analysis. It is trivial to perform the security analysis for IoT devices to understand the loop holes and very nature of the devices itself. Given these issues, there has been less emphasis on security testing and analysis of the IoT. In this paper, we show our preliminary efforts in the area of security analysis for IoT devices and introduce a security IoT testbed for performing security analysis. We also discuss the necessary design, requirements and the architecture to support our security analysis conducted via the proposed testbed.

Sadasivam, G. K., Hota, C..  2015.  Scalable Honeypot Architecture for Identifying Malicious Network Activities. 2015 International Conference on Emerging Information Technology and Engineering Solutions. :27–31.

Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.

Sadasivarao, Abhinava, Bardhan, Sanjoy, Syed, Sharfuddin, Lu, Biao, Paraschis, Loukas.  2019.  Optonomic: Architecture for Secure Autonomic Optical Transport Networks. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :321–328.
We present a system architecture for autonomic operation, administration and maintenance of both the optical and digital layers within the integrated optical transport network infrastructure. This framework encompasses the end-to-end instrumentation: From equipment commissioning to automatic discovery and bring-up, to self-managed, self-(re)configuring optical transport layer. We leverage prevalent networking protocols to build an autonomic control plane for the optical network elements. Various aspects of security, a critical element for self-managed operations, are addressed. We conclude with a discussion on the interaction with SDN, and how autonomic functions can benefit from these capabilities, a brief survey of standardization activities and scope for future work.
Sadeghi, Ahmad-Reza.  2017.  Moving Targets vs. Moving Adversaries: On the Effectiveness of System Randomization. Proceedings of the 2017 Workshop on Moving Target Defense. :51–52.
Memory-corruption vulnerabilities pose a severe threat on modern systems security. Although this problem is known for almost three decades it is unlikely to be solved in the near future because a large amount of modern software is still programmed in unsafe, legacy languages such as C/C++. With new vulnerabilities in popular software discovered almost every day, and with high third party demand for (purchasing) the corresponding exploits, runtime attacks are more prevalent than ever. Even perfect cryptography can easily be undermined by exploiting software vulnerabilities. Typically, one vulnerability in wide-spread software (e.g., Tor Browser) is sufficient for the adversary to compromise all users. Moving target approaches such as software diversity [2] and system randomization techniques [7] are considered to be effective and practical means to strongly reduce the scale of such attacks because ideally, the adversary would require to craft a unique exploit per user. However, recently it was shown that existing software-randomization schemes can be circumvented by practical exploitation techniques such as Just-In-Time Return Oriented Programming (JIT-ROP) that takes advantage of information leakage [1]. The attack demonstrated that even a single disclosed code pointer can be exploited to defeat any (fine-grained) code randomization scheme. Later, it was shown that there are various sources of information leakage that can be exploited such as virtual function pointers [4]. JIT-ROP motivated a number of subsequent works to prevent the adversary from reading code such as Readactor [3,5], or ASLR Guard [8]. For instance, Readactor and its successor Readactor++ [3,5] use various techniques to prevent direct and indirect code disclosure, which seems to be non-trivial in general [6]. The arms race will continue.
Sadeghi, Alireza, Esfahani, Naeem, Malek, Sam.  2017.  Mining Mobile App Markets for Prioritization of Security Assessment Effort. Proceedings of the 2Nd ACM SIGSOFT International Workshop on App Market Analytics. :1–7.

Like any other software engineering activity, assessing the security of a software system entails prioritizing the resources and minimizing the risks. Techniques ranging from the manual inspection to automated static and dynamic analyses are commonly employed to identify security vulnerabilities prior to the release of the software. However, none of these techniques is perfect, as static analysis is prone to producing lots of false positives and negatives, while dynamic analysis and manual inspection are unwieldy, both in terms of required time and cost. This research aims to improve these techniques by mining relevant information from vulnerabilities found in the app markets. The approach relies on the fact that many modern software systems, in particular mobile software, are developed using rich application development frameworks (ADF), allowing us to raise the level of abstraction for detecting vulnerabilities and thereby making it possible to classify the types of vulnerabilities that are encountered in a given category of application. By coupling this type of information with severity of the vulnerabilities, we are able to improve the efficiency of static and dynamic analyses, and target the manual effort on the riskiest vulnerabilities.

Sadeghi, Koosha, Banerjee, Ayan, Gupta, Sandeep K. S..  2019.  An Analytical Framework for Security-Tuning of Artificial Intelligence Applications Under Attack. 2019 IEEE International Conference On Artificial Intelligence Testing (AITest). :111—118.
Machine Learning (ML) algorithms, as the core technology in Artificial Intelligence (AI) applications, such as self-driving vehicles, make important decisions by performing a variety of data classification or prediction tasks. Attacks on data or algorithms in AI applications can lead to misclassification or misprediction, which can fail the applications. For each dataset separately, the parameters of ML algorithms should be tuned to reach a desirable classification or prediction accuracy. Typically, ML experts tune the parameters empirically, which can be time consuming and does not guarantee the optimal result. To this end, some research suggests an analytical approach to tune the ML parameters for maximum accuracy. However, none of the works consider the ML performance under attack in their tuning process. This paper proposes an analytical framework for tuning the ML parameters to be secure against attacks, while keeping its accuracy high. The framework finds the optimal set of parameters by defining a novel objective function, which takes into account the test results of both ML accuracy and its security against attacks. For validating the framework, an AI application is implemented to recognize whether a subject's eyes are open or closed, by applying k-Nearest Neighbors (kNN) algorithm on her Electroencephalogram (EEG) signals. In this application, the number of neighbors (k) and the distance metric type, as the two main parameters of kNN, are chosen for tuning. The input data perturbation attack, as one of the most common attacks on ML algorithms, is used for testing the security of the application. Exhaustive search approach is used to solve the optimization problem. The experiment results show k = 43 and cosine distance metric is the optimal configuration of kNN for the EEG dataset, which leads to 83.75% classification accuracy and reduces the attack success rate to 5.21%.
Sadeghi, Parham, Movahhedinia, Naser.  2018.  An Analytical Approach to ETX Estimation in Linear-Topology in IoT. Proceedings of the International Conference on Smart Cities and Internet of Things. :2:1–2:4.
With ubiquitous connectivity no longer a dream, the need for efficient communications between smart objects in Internet of Things has become more demanding than ever before. In this context, efficient yet low cost modeling tools, capable of considering the impactful parameters are essential in provisioning such systems. ETX (Expected Transmission count), used in RPL (Routing Protocol for Low power lossy networks), turns to be one of the most widely used routing metrics, so it is considered by IETF for IPv6 routing in low-power wireless sensor networks and to infer many network layer characteristics like packet loss rate. As more and more wireless sensor networks being deployed with 6LoWPAN, having a model to easily obtain network parameters gains importance. This work aims to provide such a tool in the form of a modular analytic model for linear topology of 6LoWPANs which are mostly seen in monitoring systems of linear structures like pipeline, bridges and highways. The proposed model is evaluated in obtaining the ETX metric and packet loss rate in a typical IoT network.
Sadique, Farhan, Bakhshaliyev, Khalid, Springer, Jeff, Sengupta, Shamik.  2019.  A System Architecture of Cybersecurity Information Exchange with Privacy (CYBEX-P). 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0493—0498.
Rapid evolution of cyber threats and recent trends in the increasing number of cyber-attacks call for adopting robust and agile cybersecurity techniques. Cybersecurity information sharing is expected to play an effective role in detecting and defending against new attacks. However, reservations and or-ganizational policies centering the privacy of shared data have become major setbacks in large-scale collaboration in cyber defense. The situation is worsened by the fact that the benefits of cyber-information exchange are not realized unless many actors participate. In this paper, we argue that privacy preservation of shared threat data will motivate entities to share threat data. Accordingly, we propose a framework called CYBersecurity information EXchange with Privacy (CYBEX-P) to achieve this. CYBEX-P is a structured information sharing platform with integrating privacy-preserving mechanisms. We propose a complete system architecture for CYBEX-P that guarantees maximum security and privacy of data. CYBEX-P outlines the details of a cybersecurity information sharing platform. The adoption of blind processing, privacy preservation, and trusted computing paradigms make CYBEX-P a versatile and secure information exchange platform.
Sadkhan, S. B., Reda, D. M..  2018.  Cryptosystem Security Evaluation Based on Diagonal Game and Information Theory. 2018 International Conference on Engineering Technology and their Applications (IICETA). :118–123.

security evaluation of cryptosystem is a critical topic in cryptology. It is used to differentiate among cryptosystems' security. The aim of this paper is to produce a new model for security evaluation of cryptosystems, which is a combination of two theories (Game Theory and Information Theory). The result of evaluation method can help researchers to choose the appropriate cryptosystems in Wireless Communications Networks such as Cognitive Radio Networks.

Sadkhan, S. B., Reda, D. M..  2018.  A Proposed Security Evaluator for Cryptosystem Based on Information Theory and Triangular Game. 2018 International Conference on Advanced Science and Engineering (ICOASE). :306-311.

The purpose of this research is to propose a new mathematical model, designed to evaluate the security of cryptosystems. This model is a mixture of ideas from two basic mathematical theories, information theory and game theory. The role of information theory is assigning the model with security criteria of the cryptosystems. The role of game theory was to produce the value of the game which is representing the outcome of these criteria, which finally refers to cryptosystem's security. The proposed model support an accurate and mathematical way to evaluate the security of cryptosystems by unifying the criteria resulted from information theory and produce a unique reasonable value.

Sadkhan, Sattar B., Yaseen, Basim S..  2018.  A DNA-Sticker Algorithm for Cryptanalysis LFSRs and NLFSRs Based Stream Cipher. 2018 International Conference on Advanced Science and Engineering (ICOASE). :301-305.
In this paper, We propose DNA sticker model based algorithm, a computability model, which is a simulation of the parallel computations using the Molecular computing as in Adelman's DNA computing experiment, it demonstrates how to use a sticker-based model to design a simple DNA-based algorithm for attacking a linear and a non-linear feedback shift register (FSR) based stream cipher. The algorithm first construct the TEST TUBE contains all overall solution space of memory complexes for the cipher and initials of registers via the sticker-based model. Then, with biological operations, separate and combine, we remove those which encode illegal plain and key stream from the TEST TUBE of memory complexes, the decision based on verifying a key stream bit this bit represented by output of LFSRs equation. The model anticipates two basic groups of single stranded DNA molecules in its representation one of a genetic bases and second of a bit string, It invests parallel search into the space of solutions through the possibilities of DNA computing and makes use of the method of cryptanalysis of algebraic code as a decision technique to accept the solution or not, and their operations are repeated until one solution or limited group of solutions is reached. The main advantages of the suggested algorithm are limited number of cipher characters, and finding one exact solution The present work concentrates on showing the applicability of DNA computing concepts as a powerful tool in breaking cryptographic systems.
Sadkhan, Sattar B., Yaseen, Basim S..  2019.  Hybrid Method to Implement a Parallel Search of the Cryptosystem Keys. 2019 International Conference on Advanced Science and Engineering (ICOASE). :204–207.

The current paper proposes a method to combine the theoretical concepts of the parallel processing created by the DNA computing and GA environments, with the effectiveness novel mechanism of the distinction and discover of the cryptosystem keys. Three-level contributions to the current work, the first is the adoption of a final key sequence mechanism by the principle of interconnected sequence parts, the second to exploit the principle of the parallel that provides GA in the search for the counter value of the sequences of the challenge to the mechanism of the discrimination, the third, the most important and broadening the breaking of the cipher, is the harmony of the principle of the parallelism that has found via the DNA computing to discover the basic encryption key. The proposed method constructs a combined set of files includes binary sequences produced from substitution of the guess attributes of the binary equations system of the cryptosystem, as well as generating files that include all the prospects of the DNA strands for all successive cipher characters, the way to process these files to be obtained from the first character file, where extract a key sequence of each sequence from mentioned file and processed with the binary sequences that mentioned the counter produced from GA. The aim of the paper is exploitation and implementation the theoretical principles of the parallelism that providing via biological environment with the new sequences recognition mechanism in the cryptanalysis.

Sadkhan, Sattar B., Reda, Dhilal M..  2018.  Best Strategies of Choosing Crypto-System’s Key for Cryptographer and Attacker Based on Game Theory. 2018 Al-Mansour International Conference on New Trends in Computing, Communication, and Information Technology (NTCCIT). :1—6.
One of the most important strength features of crypto-system's is the key space. As a result, whenever the system has more key space, it will be more resistant to attack. The weakest type of attack on the key space is Brute Force attack, which tests all the keys on the ciphertext in order to get the plaintext. But there are several strategies that can be considered by the attacker and cryptographer related to the selection of the right key with the lowest cost (time). Game theory is a mathematical theory that draws the best strategies for most problems. This research propose a new evaluation method which is employing game theory to draw best strategies for both players (cryptographer & attacker).
Sadri, Mehdi, Mehrotra, Sharad, Yu, Yaming.  2016.  Online Adaptive Topic Focused Tweet Acquisition. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. :2353–2358.

Twitter provides a public streaming API that is strictly limited, making it difficult to simultaneously achieve good coverage and relevance when monitoring tweets for a specific topic of interest. In this paper, we address the tweet acquisition challenge to enhance monitoring of tweets based on the client/application needs in an online adaptive manner such that the quality and quantity of the results improves over time. We propose a Tweet Acquisition System (TAS), that iteratively selects phrases to track based on an explore-exploit strategy. Our experimental studies show that TAS significantly improves recall of relevant tweets and the performance improves when the topics are more specific.