Visible to the public Biblio

Found 152 results

Filters: Keyword is Optimization  [Clear All Filters]
2020-05-18
Yang, Xiaoliu, Li, Zetao, Zhang, Fabin.  2018.  Simultaneous diagnosis of multiple parametric faults based on differential evolution algorithm. 2018 Chinese Control And Decision Conference (CCDC). :2781–2786.
This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.
2020-05-11
Kanimozhi, V., Jacob, T. Prem.  2019.  Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing. 2019 International Conference on Communication and Signal Processing (ICCSP). :0033–0036.
One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. The most important component used to detect cyber attacks or malicious activities is the Intrusion Detection System (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.
2020-05-08
Zhang, Xu, Ye, Zhiwei, Yan, Lingyu, Wang, Chunzhi, Wang, Ruoxi.  2018.  Security Situation Prediction based on Hybrid Rice Optimization Algorithm and Back Propagation Neural Network. 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). :73—77.
Research on network security situation awareness is currently a research hotspot in the field of network security. It is one of the easiest and most effective methods to use the BP neural network for security situation prediction. However, there are still some problems in BP neural network, such as slow convergence rate, easy to fall into local extremum, etc. On the other hand, some common used evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), easily fall into local optimum. Hybrid rice optimization algorithm is a newly proposed algorithm with strong search ability, so the method of this paper is proposed. This article describes in detail the use of BP network security posture prediction method. In the proposed method, HRO is used to train the connection weights of the BP network. Through the advantages of HRO global search and fast convergence, the future security situation of the network is predicted, and the accuracy of the situation prediction is effectively improved.
Zhang, Shaobo, Shen, Yongjun, Zhang, Guidong.  2018.  Network Security Situation Prediction Model Based on Multi-Swarm Chaotic Particle Optimization and Optimized Grey Neural Network. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). :426—429.
Network situation value is an important index to measure network security. Establishing an effective network situation prediction model can prevent the occurrence of network security incidents, and plays an important role in network security protection. Through the understanding and analysis of the network security situation, we can see that there are many factors affecting the network security situation, and the relationship between these factors is complex., it is difficult to establish more accurate mathematical expressions to describe the network situation. Therefore, this paper uses the grey neural network as the prediction model, but because the convergence speed of the grey neural network is very fast, the network is easy to fall into local optimum, and the parameters can not be further modified, so the Multi-Swarm Chaotic Particle Optimization (MSCPO)is used to optimize the key parameters of the grey neural network. By establishing the nonlinear mapping relationship between the influencing factors and the network security situation, the network situation can be predicted and protected.
2020-04-20
Xiao, Tianrui, Khisti, Ashish.  2019.  Maximal Information Leakage based Privacy Preserving Data Disclosure Mechanisms. 2019 16th Canadian Workshop on Information Theory (CWIT). :1–6.
It is often necessary to disclose training data to the public domain, while protecting privacy of certain sensitive labels. We use information theoretic measures to develop such privacy preserving data disclosure mechanisms. Our mechanism involves perturbing the data vectors to strike a balance in the privacy-utility trade-off. We use maximal information leakage between the output data vector and the confidential label as our privacy metric. We first study the theoretical Bernoulli-Gaussian model and study the privacy-utility trade-off when only the mean of the Gaussian distributions can be perturbed. We show that the optimal solution is the same as the case when the utility is measured using probability of error at the adversary. We then consider an application of this framework to a data driven setting and provide an empirical approximation to the Sibson mutual information. By performing experiments on the MNIST and FERG data sets, we show that our proposed framework achieves equivalent or better privacy than previous methods based on mutual information.
2020-04-17
Jang, Yunseok, Zhao, Tianchen, Hong, Seunghoon, Lee, Honglak.  2019.  Adversarial Defense via Learning to Generate Diverse Attacks. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). :2740—2749.
With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been applied as dominant tools to various machine learning domains. Despite this success, however, it has been found that DNNs are surprisingly vulnerable to malicious attacks; adding a small, perceptually indistinguishable perturbations to the data can easily degrade classification performance. Adversarial training is an effective defense strategy to train a robust classifier. In this work, we propose to utilize the generator to learn how to create adversarial examples. Unlike the existing approaches that create a one-shot perturbation by a deterministic generator, we propose a recursive and stochastic generator that produces much stronger and diverse perturbations that comprehensively reveal the vulnerability of the target classifier. Our experiment results on MNIST and CIFAR-10 datasets show that the classifier adversarially trained with our method yields more robust performance over various white-box and black-box attacks.
2020-04-06
Sun, Xuezi, Xu, Guangxian, Liu, Chao.  2019.  A Network Coding Optimization Scheme for Niche Algorithm based on Security Performance. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:1969—1972.

The network coding optimization based on niche genetic algorithm can observably reduce the network overhead of encoding technology, however, security issues haven't been considered in the coding operation. In order to solve this problem, we propose a network coding optimization scheme for niche algorithm based on security performance (SNGA). It is on the basis of multi-target niche genetic algorithm(NGA)to construct a fitness function which with k-secure network coding mechanism, and to ensure the realization of information security and achieve the maximum transmission of the network. The simulation results show that SNGA can effectively improve the security of network coding, and ensure the running time and convergence speed of the optimal solution.

Boussaha, Ryma, Challal, Yacine, Bouabdallah, Abdelmadjid.  2018.  Authenticated Network Coding for Software-Defined Named Data Networking. 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA). :1115–1122.
Named Data Networking (or NDN) represents a potential new approach to the current host based Internet architecture which prioritize content over the communication between end nodes. NDN relies on caching functionalities and local data storage, such as a content request could be satisfied by any node holding a copy of the content in its storage. Due to the fact that users in the same network domain can share their cached content with each other and in order to reduce the transmission cost for obtaining the desired content, a cooperative network coding mechanism is proposed in this paper. We first formulate our optimal coding and homomorphic signature scheme as a MIP problem and we show how to leverage Software Defined Networking to provide seamless implementation of the proposed solution. Evaluation results demonstrate the efficiency of the proposed coding scheme which achieves better performance than conventional NDN with random coding especially in terms of transmission cost and security.
2020-03-23
Hu, Rui, Guo, Yuanxiong, Pan, Miao, Gong, Yanmin.  2019.  Targeted Poisoning Attacks on Social Recommender Systems. 2019 IEEE Global Communications Conference (GLOBECOM). :1–6.
With the popularity of online social networks, social recommendations that rely on one’s social connections to make personalized recommendations have become possible. This introduces vulnerabilities for an adversarial party to compromise the recommendations for users by utilizing their social connections. In this paper, we propose the targeted poisoning attack on the factorization-based social recommender system in which the attacker aims to promote an item to a group of target users by injecting fake ratings and social connections. We formulate the optimal poisoning attack as a bi-level program and develop an efficient algorithm to find the optimal attacking strategy. We then evaluate the proposed attacking strategy on real-world dataset and demonstrate that the social recommender system is sensitive to the targeted poisoning attack. We find that users in the social recommender system can be attacked even if they do not have direct social connections with the attacker.
2020-03-09
Tun, Hein, Lupin, Sergey, Than, Ba Hla, Nay Zaw Linn, Kyaw, Khaing, Min Thu.  2019.  Estimation of Information System Security Using Hybrid Simulation in AnyLogic. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1829–1834.
Nowadays the role of Information systems in our life has greatly increased, which has become one of the biggest challenges for citizens, organizations and governments. Every single day we are becoming more and more dependent on information and communication technology (ICT). A major goal of information security is to find the best ways to mitigate the risks. The context-role and perimeter protection approaches can reduce and prevent an unauthorized penetration to protected zones and information systems inside the zones. The result of this work can be useful for the security system analysis and optimization of their organizations.
Perner, Cora, Kinkelin, Holger, Carle, Georg.  2019.  Adaptive Network Management for Safety-Critical Systems. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :25–30.
Present networks within safety-critical systems rely on complex and inflexible network configurations. New technologies such as software-defined networking are more dynamic and offer more flexibility, but due care needs to be exercised to ensure that safety and security are not compromised by incorrect configurations. To this end, this paper proposes the use of pre-generated and optimized configuration templates. These provide alternate routes for traffic considering availability, resilience and timing constraints where network components fail due to attacks or faults.To obtain these templates, two heuristics based on Dijkstra's algorithm and an optimization algorithm providing the maximum resilience were investigated. While the configurations obtained through optimization yield appropriate templates, the heuristics investigated are not suitable to obtain configuration templates, since they cannot fulfill all requirements.
2020-03-02
Tootaghaj, Diman Zad, La Porta, Thomas, He, Ting.  2019.  Modeling, Monitoring and Scheduling Techniques for Network Recovery from Massive Failures. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :695–700.

Large-scale failures in communication networks due to natural disasters or malicious attacks can severely affect critical communications and threaten lives of people in the affected area. In the absence of a proper communication infrastructure, rescue operation becomes extremely difficult. Progressive and timely network recovery is, therefore, a key to minimizing losses and facilitating rescue missions. To this end, we focus on network recovery assuming partial and uncertain knowledge of the failure locations. We proposed a progressive multi-stage recovery approach that uses the incomplete knowledge of failure to find a feasible recovery schedule. Next, we focused on failure recovery of multiple interconnected networks. In particular, we focused on the interaction between a power grid and a communication network. Then, we focused on network monitoring techniques that can be used for diagnosing the performance of individual links for localizing soft failures (e.g. highly congested links) in a communication network. We studied the optimal selection of the monitoring paths to balance identifiability and probing cost. Finally, we addressed, a minimum disruptive routing framework in software defined networks. Extensive experimental and simulation results show that our proposed recovery approaches have a lower disruption cost compared to the state-of-the-art while we can configure our choice of trade-off between the identifiability, execution time, the repair/probing cost, congestion and the demand loss.

Wang, Qing, Wang, Zengfu, Guo, Jun, Tahchi, Elias, Wang, Xinyu, Moran, Bill, Zukerman, Moshe.  2019.  Path Planning of Submarine Cables. 2019 21st International Conference on Transparent Optical Networks (ICTON). :1–4.
Submarine optical-fiber cables are key components in the conveying of Internet data, and their failures have costly consequences. Currently, there are over a million km of such cables empowering the Internet. To carry the ever-growing Internet traffic, additional 100,000s of km of cables will be needed in the next few years. At an average cost of \$28,000 per km, this entails investments of billions of dollars. In current industry practice, cable paths are planned manually by experts. This paper surveys our recent work on cable path planning algorithms, where we use several methods to plan cable paths taking account of a range of cable risk factors in addition to cable costs. Two methods, namely, the fast marching method (FMM) and the Dijkstra's algorithm are applied here to long-haul cable path design in a new geographical region. A specific example is given to demonstrate the benefit of the FMM-based method in terms of the better path planning solutions over the Dijkstra's algorithm.
2020-02-18
Fattahi, Saeideh, Yazdani, Reza, Vahidipour, Seyyed Mehdi.  2019.  Discovery of Society Structure in A Social Network Using Distributed Cache Memory. 2019 5th International Conference on Web Research (ICWR). :264–269.

Community structure detection in social networks has become a big challenge. Various methods in the literature have been presented to solve this challenge. Recently, several methods have also been proposed to solve this challenge based on a mapping-reduction model, in which data and algorithms are divided between different process nodes so that the complexity of time and memory of community detection in large social networks is reduced. In this paper, a mapping-reduction model is first proposed to detect the structure of communities. Then the proposed framework is rewritten according to a new mechanism called distributed cache memory; distributed cache memory can store different values associated with different keys and, if necessary, put them at different computational nodes. Finally, the proposed rewritten framework has been implemented using SPARK tools and its implementation results have been reported on several major social networks. The performed experiments show the effectiveness of the proposed framework by varying the values of various parameters.

Hasslinger, Gerhard, Ntougias, Konstantinos, Hasslinger, Frank, Hohlfeld, Oliver.  2019.  Fast and Efficient Web Caching Methods Regarding the Size and Performance Measures per Data Object. 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1–7.

Caching methods are developed since 50 years for paging in CPU and database systems, and since 25 years for web caching as main application areas among others. Pages of unique size are usual in CPU caches, whereas web caches are storing data chunks of different size in a widely varying range. We study the impact of different object sizes on the performance and the overhead of web caching. This entails different caching goals, starting from the byte and object hit ratio to a generalized value hit ratio for optimized costs and benefits of caching regarding traffic engineering (TE), reduced delays and other QoS measures. The selection of the cache contents turns out to be crucial for the web cache efficiency with awareness of the size and other properties in a score for each object. We introduce a new class of rank exchange caching methods and show how their performance compares to other strategies with extensions needed to include the size and scores for QoS and TE caching goals. Finally, we derive bounds on the object, byte and value hit ratio for the independent request model (IRM) based on optimum knapsack solutions of the cache content.

Yu, Jing, Fu, Yao, Zheng, Yanan, Wang, Zheng, Ye, Xiaojun.  2019.  Test4Deep: An Effective White-Box Testing for Deep Neural Networks. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :16–23.
Current testing for Deep Neural Networks (DNNs) focuses on quantity of test cases but ignores diversity. To the best of our knowledge, DeepXplore is the first white-box framework for Deep Learning testing by triggering differential behaviors between multiple DNNs and increasing neuron coverage to improve diversity. Since it is based on multiple DNNs facing problems that (1) the framework is not friendly to a single DNN, (2) if incorrect predictions made by all DNNs simultaneously, DeepXplore cannot generate test cases. This paper presents Test4Deep, a white-box testing framework based on a single DNN. Test4Deep avoids mistakes of multiple DNNs by inducing inconsistencies between predicted labels of original inputs and that of generated test inputs. Meanwhile, Test4Deep improves neuron coverage to capture more diversity by attempting to activate more inactivated neurons. The proposed method was evaluated on three popular datasets with nine DNNs. Compared to DeepXplore, Test4Deep produced average 4.59% (maximum 10.49%) more test cases that all found errors and faults of DNNs. These test cases got 19.57% more diversity increment and 25.88% increment of neuron coverage. Test4Deep can further be used to improve the accuracy of DNNs by average up to 5.72% (maximum 7.0%).
2020-02-17
Chalise, Batu K..  2019.  ADMM-based Beamforming Optimization for Physical Layer Security in a Full-duplex Relay System. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :4734–4738.
Although beamforming optimization problems in full-duplex communication systems can be optimally solved with the semidefinite relaxation (SDR) approach, its computational complexity increases rapidly when the problem size increases. In order to circumvent this issue, in this paper, we propose an alternating direction of multiplier method (ADMM) which minimizes the augmented Lagrangian of the dual of the SDR and handles the inequality constraints with the use of slack variables. The proposed ADMM is then applied for optimizing the relay beamformer to maximize the secrecy rate. Simulation results show that the proposed ADMM performs as good as the SDR approach.
2020-02-10
Ding, Steven H. H., Fung, Benjamin C. M., Charland, Philippe.  2019.  Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization. 2019 IEEE Symposium on Security and Privacy (SP). :472–489.

Reverse engineering is a manually intensive but necessary technique for understanding the inner workings of new malware, finding vulnerabilities in existing systems, and detecting patent infringements in released software. An assembly clone search engine facilitates the work of reverse engineers by identifying those duplicated or known parts. However, it is challenging to design a robust clone search engine, since there exist various compiler optimization options and code obfuscation techniques that make logically similar assembly functions appear to be very different. A practical clone search engine relies on a robust vector representation of assembly code. However, the existing clone search approaches, which rely on a manual feature engineering process to form a feature vector for an assembly function, fail to consider the relationships between features and identify those unique patterns that can statistically distinguish assembly functions. To address this problem, we propose to jointly learn the lexical semantic relationships and the vector representation of assembly functions based on assembly code. We have developed an assembly code representation learning model \textbackslashemphAsm2Vec. It only needs assembly code as input and does not require any prior knowledge such as the correct mapping between assembly functions. It can find and incorporate rich semantic relationships among tokens appearing in assembly code. We conduct extensive experiments and benchmark the learning model with state-of-the-art static and dynamic clone search approaches. We show that the learned representation is more robust and significantly outperforms existing methods against changes introduced by obfuscation and optimizations.

Elakkiya, E, Selvakumar, S.  2019.  Initial Weights Optimization Using Enhanced Step Size Firefly Algorithm for Feed Forward Neural Network Applied to Spam Detection. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). :942–946.

Spams are unsolicited and unnecessary messages which may contain harmful codes or links for activation of malicious viruses and spywares. Increasing popularity of social networks attracts the spammers to perform malicious activities in social networks. So an efficient spam detection method is necessary for social networks. In this paper, feed forward neural network with back propagation based spam detection model is proposed. The quality of the learning process is improved by tuning initial weights of feed forward neural network using proposed enhanced step size firefly algorithm which reduces the time for finding optimal weights during the learning process. The model is applied for twitter dataset and the experimental results show that, the proposed model performs well in terms of accuracy and detection rate and has lower false positive rate. 

2020-01-27
Tuba, Eva, Jovanovic, Raka, Zivkovic, Dejan, Beko, Marko, Tuba, Milan.  2019.  Clustering Algorithm Optimized by Brain Storm Optimization for Digital Image Segmentation. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1–6.
In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.
Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay.  2019.  A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
Kalaivani, S., Vikram, A., Gopinath, G..  2019.  An Effective Swarm Optimization Based Intrusion Detection Classifier System for Cloud Computing. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :185–188.
Most of the swarm optimization techniques are inspired by the characteristics as well as behaviour of flock of birds whereas Artificial Bee Colony is based on the foraging characteristics of the bees. However, certain problems which are solved by ABC do not yield desired results in-terms of performance. ABC is a new devised swarm intelligence algorithm and predominately employed for optimization of numerical problems. The main reason for the success of ABC algorithm is that it consists of feature such as fathomable and flexibility when compared to other swarm optimization algorithms and there are many possible applications of ABC. Cloud computing has their limitation in their application and functionality. The cloud computing environment experiences several security issues such as Dos attack, replay attack, flooding attack. In this paper, an effective classifier is proposed based on Artificial Bee Colony for cloud computing. It is evident in the evaluation results that the proposed classifier achieved a higher accuracy rate.
2020-01-20
Vu, Thang X., Vu, Trinh Anh, Lei, Lei, Chatzinotas, Symeon, Ottersten, Björn.  2019.  Linear Precoding Design for Cache-aided Full-duplex Networks. 2019 IEEE Wireless Communications and Networking Conference (WCNC). :1–6.
Edge caching has received much attention as a promising technique to overcome the stringent latency and data hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit simultaneously. In this paper, we study a cache-aided FD system via delivery time analysis and optimization. In the considered system, an edge node (EN) operates in FD mode and serves users via wireless channels. Two optimization problems are formulated to minimize the largest delivery time based on the two popular linear beamforming zero-forcing and minimum mean square error designs. Since the formulated problems are non-convex due to the self-interference at the EN, we propose two iterative optimization algorithms based on the inner approximation method. The convergence of the proposed iterative algorithms is analytically guaranteed. Finally, the impacts of caching and the advantages of the FD system over the half-duplex (HD) counterpart are demonstrated via numerical results.
Khairullin, Ilias, Bobrov, Vladimir.  2019.  On Cryptographic Properties of Some Lightweight Algorithms and its Application to the Construction of S-Boxes. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1807–1810.

We consider some approaches to the construction of lightweight block ciphers and introduce the definitions for "index of strong nonlinearity" and "index of perfection". For PRESENT, MIDORI, SKINNY, CLEFIA, LILLIPUT mixing and nonlinear properties were evaluated. We obtain the exact values of the exponents for mixing matrices of round functions and the upper bounds for indexes of perfection and strong nonlinearity. It was determined by the experiment that each coordinate function of output block is nonlinear during 500 rounds. We propose the algorithmic realization of 16×16 S-box based on the modified additive generator with lightweight cipher SPECK as a modification which does not demand memory for storage huge substitution tables. The best value of the differential characteristic of such S-box is 18/216, the minimal nonlinearity degree of coordinate functions is equal to 15 and the minimal linear characteristic is 788/215.

Faticanti, Francescomaria, De Pellegrini, Francesco, Siracusa, Domenico, Santoro, Daniele, Cretti, Silvio.  2019.  Cutting Throughput with the Edge: App-Aware Placement in Fog Computing. 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :196–203.

Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications.