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Dou, Yanzhi, Zeng, Kexiong(Curtis), Li, He, Yang, Yaling, Gao, Bo, Guan, Chaowen, Ren, Kui, Li, Shaoqian.  2016.  P2-SAS: Preserving Users' Privacy in Centralized Dynamic Spectrum Access Systems. Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. :321–330.

Centralized spectrum management is one of the key dynamic spectrum access (DSA) mechanisms proposed to govern the spectrum sharing between government incumbent users (IUs) and commercial secondary users (SUs). In the current centralized DSA designs, the operation data of both government IUs and commercial SUs needs to be shared with a central server. However, the operation data of government IUs is often classified information and the SU operation data may also be commercial secret. The current system design dissatisfies the privacy requirement of both IUs and SUs since the central server is not necessarily trust-worthy for holding such sensitive operation data. To address the privacy issue, this paper presents a privacy-preserving centralized DSA system (P2-SAS), which realizes the complex spectrum allocation process of DSA through efficient secure multi-party computation. In P2-SAS, none of the IU or SU operation data would be exposed to any snooping party, including the central server itself. We formally prove the correctness and privacy-preserving property of P2-SAS and evaluate its scalability and practicality using experiments based on real-world data. Experiment results show that P2-SAS can respond an SU's spectrum request in 6.96 seconds with communication overhead of less than 4 MB.

Alnaami, K., Ayoade, G., Siddiqui, A., Ruozzi, N., Khan, L., Thuraisingham, B..  2015.  P2V: Effective Website Fingerprinting Using Vector Space Representations. 2015 IEEE Symposium Series on Computational Intelligence. :59–66.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

K. Alnaami, G. Ayoade, A. Siddiqui, N. Ruozzi, L. Khan, B. Thuraisingham.  2015.  "P2V: Effective Website Fingerprinting Using Vector Space Representations". 2015 IEEE Symposium Series on Computational Intelligence. :59-66.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

Liu, Jed, Hallahan, William, Schlesinger, Cole, Sharif, Milad, Lee, Jeongkeun, Soulé, Robert, Wang, Han, Ca\c scaval, C\u alin, McKeown, Nick, Foster, Nate.  2018.  P4V: Practical Verification for Programmable Data Planes. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication. :490-503.

We present the design and implementation of p4v, a practical tool for verifying data planes described using the P4 programming language. The design of p4v is based on classic verification techniques but adds several key innovations including a novel mechanism for incorporating assumptions about the control plane and domain-specific optimizations which are needed to scale to large programs. We present case studies showing that p4v verifies important properties and finds bugs in real-world programs. We conduct experiments to quantify the scalability of p4v on a wide range of additional examples. We show that with just a few hundred lines of control-plane annotations, p4v is able to verify critical safety properties for switch.p4, a program that implements the functionality of on a modern data center switch, in under three minutes.

Pan, Cheng, Hu, Xiameng, Zhou, Lan, Luo, Yingwei, Wang, Xiaolin, Wang, Zhenlin.  2018.  PACE: Penalty Aware Cache Modeling with Enhanced AET. Proceedings of the 9th Asia-Pacific Workshop on Systems. :19:1–19:8.
Past cache modeling techniques are typically limited to a cache system with a fixed cache line/block size. This limitation is not a problem for a hardware cache where the cache line size is uniform. However, modern in-memory software caches, such as Memcached and Redis, are able to cache varied-size data objects. A software cache supports update and delete operations in addition to only reads and writes for a hardware cache. Moreover, existing cache models often assume that the penalty for each cache miss is identical, which is not true especially for software cache targeting web services, and past cache management policies that aim to improve cache hit rate are no longer sufficient. We propose a more general cache model that can handle varied cache block sizes, nonuniform miss penalties, and diverse cache operations. In this paper, we first extend a state-of-the-art cache model to accurately predict cache miss ratios for variable cache sizes when object size, updates and deletions are considered. We then apply this model to drive cache management when miss penalty is brought into consideration. Our approach delivers better results than a recent penalty-aware cache management scheme, Hyperbolic Caching, especially when cache budget is tight. Another advantage of our approach is that it provides predictable and controllable cache management on cache space allocation, especially when multiple applications share the cache space.
Hussain, Mubashir, Guo, Hui.  2017.  Packet Leak Detection on Hardware-Trojan Infected NoCs for MPSoC Systems. Proceedings of the 2017 International Conference on Cryptography, Security and Privacy. :85–90.
Packet leak on network-on-chip (NoC) is one of the key security concerns in the MPSoC design, where the NoC of the system can come from a third-party vendor and can be illegitimately implanted with hardware trojans. Those trojans are usually small so that they can escape the scrutiny of circuit level testing and perform attacks when activated. This paper targets the trojan that leaks packets to malicious applications by altering the packet source and destination addresses. To detect such a packet leak, we present a cost effective authentication design where the packet source and destination addresses are tagged with a dynamic random value and the tag is scrambled with the packet data. Our design has two features: 1) If the adversary attempts to play with tag to escape detection, the data in the packet may likely be changed – hence invalidating the leaked packet; 2) If the attacker only alters the packet addresses without twiddling tag in the packet, the attack will be100% detected.
Elsadig, M. A., Fadlalla, Y. A..  2018.  Packet Length Covert Channel: A Detection Scheme. 2018 1st International Conference on Computer Applications Information Security (ICCAIS). :1-7.

A covert channel is a communication channel that is subjugated for illegal flow of information in a way that violates system security policies. It is a dangerous, invisible, undetectable, and developed security attack. Recently, Packet length covert channel has motivated many researchers as it is a one of the most undetectable network covert channels. Packet length covert channel generates a covert traffic that is very similar to normal terrific which complicates the detection of such type of covert channels. This motivates us to introduce a machine learning based detection scheme. Recently, a machine learning approach has proved its capability in many different fields especially in security field as it usually brings up a reliable and realistic results. Based in our developed content and frequency-based features, the developed detection scheme has been fully trained and tested. Our detection scheme has gained an excellent degree of detection accuracy which reaches 98% (zero false negative rate and 0.02 false positive rate).

Misra, G., Such, J. M..  2017.  PACMAN: Personal Agent for Access Control in Social Media. IEEE Internet Computing. 21:18–26.

Given social media users' plethora of interactions, appropriately controlling access to such information becomes a challenging task for users. Selecting the appropriate audience, even from within their own friend network, can be fraught with difficulties. PACMAN is a potential solution for this dilemma problem. It's a personal assistant agent that recommends personalized access control decisions based on the social context of any information disclosure by incorporating communities generated from the user's network structure and utilizing information in the user's profile. PACMAN provides accurate recommendations while minimizing intrusiveness.

Min, Chulhong, Lee, Seungchul, Lee, Changhun, Lee, Youngki, Kang, Seungwoo, Choi, Seungpyo, Kim, Wonjung, Song, Junehwa.  2016.  PADA: Power-aware Development Assistant for Mobile Sensing Applications. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. :946–957.

We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.

Tomandl, A., Herrmann, D., Federrath, H..  2014.  PADAVAN: Privacy-Aware Data Accumulation for Vehicular Ad-hoc Networks. Wireless and Mobile Computing, Networking and Communications (WiMob), 2014 IEEE 10th International Conference on. :487-493.

In this paper we introduce PADAVAN, a novel anonymous data collection scheme for Vehicular Ad Hoc Networks (VANETs). PADAVAN allows users to submit data anonymously to a data consumer while preventing adversaries from submitting large amounts of bogus data. PADAVAN is comprised of an n-times anonymous authentication scheme, mix cascades and various principles to protect the privacy of the submitted data itself. Furthermore, we evaluate the effectiveness of limiting an adversary to a fixed amount of messages.

Selim, Ahmed, Elgharib, Mohamed, Doyle, Linda.  2016.  Painting Style Transfer for Head Portraits Using Convolutional Neural Networks. ACM Trans. Graph.. 35:129:1–129:18.

Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This limits their domain of applicability. We present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style. We impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures. We generate a solution through Convolutional Neural Networks and we present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shower-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.

Agarwal, Pankaj K., Fox, Kyle, Munagala, Kamesh, Nath, Abhinandan.  2016.  Parallel Algorithms for Constructing Range and Nearest-Neighbor Searching Data Structures. Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. :429–440.

With the massive amounts of data available today, it is common to store and process data using multiple machines. Parallel programming platforms such as MapReduce and its variants are popular frameworks for handling such large data. We present the first provably efficient algorithms to compute, store, and query data structures for range queries and approximate nearest neighbor queries in a popular parallel computing abstraction that captures the salient features of MapReduce and other massively parallel communication (MPC) models. In particular, we describe algorithms for \$kd\$-trees, range trees, and BBD-trees that only require O(1) rounds of communication for both preprocessing and querying while staying competitive in terms of running time and workload to their classical counterparts. Our algorithms are randomized, but they can be made deterministic at some increase in their running time and workload while keeping the number of rounds of communication to be constant.

Yang, J.-S., Chang, J.-M., Pai, K.-J., Chan, H.-C..  2015.  Parallel Construction of Independent Spanning Trees on Enhanced Hypercubes. Parallel and Distributed Systems, IEEE Transactions on. PP:1-1.

The use of multiple independent spanning trees (ISTs) for data broadcasting in networks provides a number of advantages, including the increase of fault-tolerance, bandwidth and security. Thus, the designs of multiple ISTs on several classes of networks have been widely investigated. In this paper, we give an algorithm to construct ISTs on enhanced hypercubes Qn,k, which contain folded hypercubes as a subclass. Moreover, we show that these ISTs are near optimal for heights and path lengths. Let D(Qn,k) denote the diameter of Qn,k. If n - k is odd or n - k ∈ {2; n}, we show that all the heights of ISTs are equal to D(Qn,k) + 1, and thus are optimal. Otherwise, we show that each path from a node to the root in a spanning tree has length at most D(Qn,k) + 2. In particular, no more than 2.15 percent of nodes have the maximum path length. As a by-product, we improve the upper bound of wide diameter (respectively, fault diameter) of Qn,k from these path lengths.

Li, Yongnan, Xiao, Limin.  2019.  Parallel DNA Computing Model of Point-Doubling in Conic Curves Cryptosystem over Finite Field GF(2ˆn). 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :1564-1571.

DNA cryptography becomes a burgeoning new area of study along with the fast-developing of DNA computing and modern cryptography. Point-doubling, point-addition and point-multiplication are three fundamental point-operations to construct encryption protocols in some cryptosystem over mathematical curves such as elliptic curves and conic curves. This paper proposes a DNA computing model to calculate point-doubling in conic curves cryptosystem over finite held GF(2n). By decomposing and rearranging the computing steps of point-doubling, the assembly process could be fulfilled by using 8 different types of computation tiles performing different functions with 1097 encoding ways. This model could also figure out point-multiplication if its coefficient is 2k. The assembly time complexity is 2kn+n-k-1, and the space complexity is k2n2+kn2-k2n.

Despotovski, Filip, Gusev, Marjan, Zdraveski, Vladimir.  2018.  Parallel Implementation of K-Nearest-Neighbors for Face Recognition. 2018 26th Telecommunications Forum (℡FOR). :1—4.
Face recognition is a fast-expanding field of research. Countless classification algorithms have found use in face recognition, with more still being developed, searching for better performance and accuracy. For high-dimensional data such as images, the K-Nearest-Neighbours classifier is a tempting choice. However, it is very computationally-intensive, as it has to perform calculations on all items in the stored dataset for each classification it makes. Fortunately, there is a way to speed up the process by performing some of the calculations in parallel. We propose a parallel CUDA implementation of the KNN classifier and then compare it to a serial implementation to demonstrate its performance superiority.
Hazari, S. S., Mahmoud, Q. H..  2019.  A Parallel Proof of Work to Improve Transaction Speed and Scalability in Blockchain Systems. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0916-0921.

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. However, its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to increase transaction speed and scalability is to identify a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Preliminary results show improvement in the scalability of Proof of Work up to 34% compared to the current system.

Seal, S. K., Cianciosa, M. R., Hirshman, S. P., Wingen, A., Wilcox, R. S., Unterberg, E. A..  2017.  Parallel Reconstruction of Three Dimensional Magnetohydrodynamic Equilibria in Plasma Confinement Devices. 2017 46th International Conference on Parallel Processing (ICPP). :282–291.

Fast, accurate three dimensional reconstructions of plasma equilibria, crucial for physics interpretation of fusion data generated within confinement devices like stellarators/ tokamaks, are computationally very expensive and routinely require days, even weeks, to complete using serial approaches. Here, we present a parallel implementation of the three dimensional plasma reconstruction code, V3FIT. A formal analysis to identify the performance bottlenecks and scalability limits of this new parallel implementation, which combines both task and data parallelism, is presented. The theoretical findings are supported by empirical performance results on several thousands of processor cores of a Cray XC30 supercomputer. Parallel V3FIT is shown to deliver over 40X speedup, enabling fusion scientists to carry out three dimensional plasma equilibrium reconstructions at unprecedented scales in only a few hours (instead of in days/weeks) for the first time.

A. Motamedi, M. Najafi, N. Erami.  2015.  "Parallel secure turbo code for security enhancement in physical layer". 2015 Signal Processing and Intelligent Systems Conference (SPIS). :179-184.

Turbo code has been one of the important subjects in coding theory since 1993. This code has low Bit Error Rate (BER) but decoding complexity and delay are big challenges. On the other hand, considering the complexity and delay of separate blocks for coding and encryption, if these processes are combined, the security and reliability of communication system are guaranteed. In this paper a secure decoding algorithm in parallel on General-Purpose Graphics Processing Units (GPGPU) is proposed. This is the first prototype of a fast and parallel Joint Channel-Security Coding (JCSC) system. Despite of encryption process, this algorithm maintains desired BER and increases decoding speed. We considered several techniques for parallelism: (1) distribute decoding load of a code word between multiple cores, (2) simultaneous decoding of several code words, (3) using protection techniques to prevent performance degradation. We also propose two kinds of optimizations to increase the decoding speed: (1) memory access improvement, (2) the use of new GPU properties such as concurrent kernel execution and advanced atomics to compensate buffering latency.

Dong Jin, Illinois Institute of Technology, David Nicol, University of Illinois at Urbana-Champaign.  2015.  Parallel Simulation and Virtual-machine-based Emulation of Software-defined Network. ACM Transactions on Modeling and Computer Simulation. 26(1)

The emerging software-defined networking (SDN) technology decouples the control plane from the data plane in a computer network with open and standardized interfaces, and hence opens up the network designers’ options and ability to innovate. The wide adoption of SDN in industry has motivated the development of large-scale, high-fidelity testbeds for evaluation of systems that incorporate SDN. In this article, we develop a framework to support OpenFlow-based SDN simulation and distributed emulation, by leveraging our prior work on a hybrid network testbed with a parallel network simulator and a virtual-machine-based emulation system. We show how to exploit typical SDN controller behaviors to handle performance issues caused by the centralized controller in parallel discrete-event simulation. In particular, we develop an asynchronous synchronization algorithm for passive SDN controllers and design a two-level architecture for active SDN controllers. We evaluate the system performance, showing good scalability. Finally, we present a case study, using the testbed, to evaluate network verification applications in an SDN-based data center network. CCS Concepts: Networks→Network simulations; Computing methodologies→Simulation

Chondamrongkul, Nacha, Sun, Jing, Wei, Bingyang, Warren, Ian.  2019.  Parallel Verification of Software Architecture Design. 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE). :50–57.
In the component-based software system, certain behaviours of components and their composition may affect system reliability at runtime. This problem can be early detected through the automated verification of software architecture design, by which model checking is one of the techniques to achieve this. However, its practicality and performance issue remain challenges. This paper presents a scalable approach for the software architecture verification. The modelling is proposed to manifest the behaviours in the software component, in order to detect problematic behaviours, such as circular dependency and performance bottleneck. The outcome of the verification identifies the problem and the scenarios that cause it. In order to mitigate the verification performance issue, the parallelism is applied to the verification process so that multiple decomposed models can be simultaneously verified on a multi-threaded environment. As some software systems are designed as the monolithic architecture, we present a method that helps to automatically decompose a large monolithic model into a set of smaller sub-models. Our approach was evaluated and proved to enhance the performance of the verification process for the large-scale complex software systems.
Searles, R., Xu, L., Killian, W., Vanderbruggen, T., Forren, T., Howe, J., Pearson, Z., Shannon, C., Simmons, J., Cavazos, J..  2017.  Parallelization of Machine Learning Applied to Call Graphs of Binaries for Malware Detection. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). :69–77.

Malicious applications have become increasingly numerous. This demands adaptive, learning-based techniques for constructing malware detection engines, instead of the traditional manual-based strategies. Prior work in learning-based malware detection engines primarily focuses on dynamic trace analysis and byte-level n-grams. Our approach in this paper differs in that we use compiler intermediate representations, i.e., the callgraph representation of binaries. Using graph-based program representations for learning provides structure of the program, which can be used to learn more advanced patterns. We use the Shortest Path Graph Kernel (SPGK) to identify similarities between call graphs extracted from binaries. The output similarity matrix is fed into a Support Vector Machine (SVM) algorithm to construct highly-accurate models to predict whether a binary is malicious or not. However, SPGK is computationally expensive due to the size of the input graphs. Therefore, we evaluate different parallelization methods for CPUs and GPUs to speed up this kernel, allowing us to continuously construct up-to-date models in a timely manner. Our hybrid implementation, which leverages both CPU and GPU, yields the best performance, achieving up to a 14.2x improvement over our already optimized OpenMP version. We compared our generated graph-based models to previously state-of-the-art feature vector 2-gram and 3-gram models on a dataset consisting of over 22,000 binaries. We show that our classification accuracy using graphs is over 19% higher than either n-gram model and gives a false positive rate (FPR) of less than 0.1%. We are also able to consider large call graphs and dataset sizes because of the reduced execution time of our parallelized SPGK implementation.

Wei, P., Xia, B., Luo, X..  2017.  Parameter estimation and convergence analysis for a class of canonical dynamic systems by extended kalman filter. 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE). :336–340.

There were many researches about the parameter estimation of canonical dynamic systems recently. Extended Kalman filter (EKF) is a popular parameter estimation method in virtue of its easy applications. This paper focuses on parameter estimation for a class of canonical dynamic systems by EKF. By constructing associated differential equation, the convergence of EKF parameter estimation for the canonical dynamic systems is analyzed. And the simulation demonstrates the good performance.

Jian Wu, Yongmei Jiang, Gangyao Kuang, Jun Lu, Zhiyong Li.  2014.  Parameter estimation for SAR moving target detection using Fractional Fourier Transform. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International. :596-599.

This paper proposes an algorithm for multi-channel SAR ground moving target detection and estimation using the Fractional Fourier Transform(FrFT). To detect the moving target with low speed, the clutter is first suppressed by Displace Phase Center Antenna(DPCA), then the signal-to-clutter can be enhanced. Have suppressed the clutter, the echo of moving target remains and can be regarded as a chirp signal whose parameters can be estimated by FrFT. FrFT, one of the most widely used tools to time-frequency analysis, is utilized to estimate the Doppler parameters, from which the moving parameters, including the velocity and the acceleration can be obtained. The effectiveness of the proposed method is validated by the simulation.

Menezes, B. A. M., Wrede, F., Kuchen, H., Neto, F. B. de Lima.  2017.  Parameter selection for swarm intelligence algorithms \#x2014; Case study on parallel implementation of FSS. 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). :1–6.

Swarm Intelligence (SI) algorithms, such as Fish School Search (FSS), are well known as useful tools that can be used to achieve a good solution in a reasonable amount of time for complex optimization problems. And when problems increase in size and complexity, some increase in population size or number of iterations might be needed in order to achieve a good solution. In extreme cases, the execution time can be huge and other approaches, such as parallel implementations, might help to reduce it. This paper investigates the relation and trade off involving these three aspects in SI algorithms, namely population size, number of iterations, and problem complexity. The results with a parallel implementations of FSS show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior of the results, i.e. increasing the population over a certain threshold only leads to slight improvements.

Tan, R. K., Bora, Ş.  2017.  Parameter tuning in modeling and simulations by using swarm intelligence optimization algorithms. 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). :148–152.

Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.