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2021-10-12
Franchina, L., Socal, A..  2020.  Innovative Predictive Model for Smart City Security Risk Assessment. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1831–1836.
In a Smart City, new technologies such as big data analytics, data fusion and artificial intelligence will increase awareness by measuring many phenomena and storing a huge amount of data. 5G will allow communication of these data among different infrastructures instantaneously. In a Smart City, security aspects are going to be a major concern. Some drawbacks, such as vulnerabilities of a highly integrated system and information overload, must be considered. To overcome these downsides, an innovative predictive model for Smart City security risk assessment has been developed. Risk metrics and indicators are defined by considering data coming from a wide range of sensors. An innovative ``what if'' algorithm is introduced to identify critical infrastructures functional relationship. Therefore, it is possible to evaluate the effects of an incident that involves one infrastructure over the others.
2021-10-04
Lovetsky, I.V., Bukvina, E.A., Ponomarchuk, Y.V..  2020.  On Providing Information Security for Decentralized Databases. 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). :1–5.
The paper discusses a prototype of a database, which can be used for operation in a decentralized mode for an information system. In this project, the focus is on creation of a data structure model that provides flexibility of business processes. The research is based on the development of a model for decentralized access rights distribution by including users in groups where they are assigned similar roles using consensus of other group members. This paper summarizes the main technologies that were used to ensure information security of the decentralized storage, the mechanisms for fixing access rights to an object access (the minimum entity of the system), describes a process of the data access control at the role level and an algorithm for managing the consensus for applying changes.
Bi, Ting, Chen, Xuehong, Li, Jun, Yang, Shuaifeng.  2020.  Research on Industrial Data Desensitization Algorithm Based on Fuzzy Set. 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA). :1–5.
With the rapid development of internet technology, informatization and digitalization have penetrated into every link of human social life. A large amount of sensitive data has been accumulated and is still being generated within the enterprise. These sensitive data runs through the daily operation of enterprises and is widely used in business analysis, development and testing, and even some outsourcing business scenarios, which are increasing the possibility of sensitive data leakage and tampering. In fact, due to the improper use of data and the lack of protective measures and other reasons, data leakage events have happened again and again. Therefore, by introducing the concept of fuzzy set and using the membership function method, this paper proposes a desensitization technology framework for industrial data and a data desensitization algorithm based on fuzzy set, and verifies the desensitization effect and protective action on sensitive data of this algorithm through the test data desensitization experiment.
2021-09-30
Peng, Cheng, Yongli, Wang, Boyi, Yao, Yuanyuan, Huang, Jiazhong, Lu, Qiao, Peng.  2020.  Cyber Security Situational Awareness Jointly Utilizing Ball K-Means and RBF Neural Networks. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :261–265.
Low accuracy and slow speed of predictions for cyber security situational awareness. This paper proposes a network security situational awareness model based on accelerated accurate k-means radial basis function (RBF) neural network, the model uses the ball k-means clustering algorithm to cluster the input samples, to get the nodes of the hidden layer of the RBF neural network, speeding up the selection of the initial center point of the RBF neural network, and optimize the parameters of the RBF neural network structure. Finally, use the training data set to train the neural network, using the test data set to test the accuracy of this neural network structure, the results show that this method has a greater improvement in training speed and accuracy than other neural networks.
Manning, Derek, Li, Peilong, Wu, Xiaoban, Luo, Yan, Zhang, Tong, Li, Weigang.  2020.  ACETA: Accelerating Encrypted Traffic Analytics on Network Edge. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
Applying machine learning techniques to detect malicious encrypted network traffic has become a challenging research topic. Traditional approaches based on studying network patterns fail to operate on encrypted data, especially without compromising the integrity of encryption. In addition, the requirement of rendering network-wide intelligent protection in a timely manner further exacerbates the problem. In this paper, we propose to leverage ×86 multicore platforms provisioned at enterprises' network edge with the software accelerators to design an encrypted traffic analytics (ETA) system with accelerated speed. Specifically, we explore a suite of data features and machine learning models with an open dataset. Then we show that by using Intel DAAL and OpenVINO libraries in model training and inference, we are able to reduce the training and inference time by a maximum order of 31× and 46× respectively while retaining the model accuracy.
2021-09-21
Khan, Mamoona, Baig, Duaa, Khan, Usman Shahid, Karim, Ahmad.  2020.  Malware Classification Framework Using Convolutional Neural Network. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1–7.
Cyber-security is facing a huge threat from malware and malware mass production due to its mutation factors. Classification of malware by their features is necessary for the security of information technology (IT) society. To provide security from malware, deep neural networks (DNN) can offer a superior solution for the detection and categorization of malware samples by using image classification techniques. To strengthen our ideology of malware classification through image recognition, we have experimented by comparing two perspectives of malware classification. The first perspective implements dense neural networks on binary files and the other applies deep layered convolutional neural network on malware images. The proposed model is trained to a set of malware samples, which are further distributed into 9 different families. The dataset of malware samples which is used in this paper is provided by Microsoft for Microsoft Malware Classification Challenge in 2015. The proposed model shows an accuracy of 97.80% on the provided dataset. By using the proposed model optimum classifications results can be attained.
Chen, Chin-Wei, Su, Ching-Hung, Lee, Kun-Wei, Bair, Ping-Hao.  2020.  Malware Family Classification Using Active Learning by Learning. 2020 22nd International Conference on Advanced Communication Technology (ICACT). :590–595.
In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples.
Lin, Kuang-Yao, Huang, Wei-Ren.  2020.  Using Federated Learning on Malware Classification. 2020 22nd International Conference on Advanced Communication Technology (ICACT). :585–589.
In recent years, everything has been more and more systematic, and it would generate many cyber security issues. One of the most important of these is the malware. Modern malware has switched to a high-growth phase. According to the AV-TEST Institute showed that there are over 350,000 new malicious programs (malware) and potentially unwanted applications (PUA) be registered every day. This threat was presented and discussed in the present paper. In addition, we also considered data privacy by using federated learning. Feature extraction can be performed based on malware. The proposed method achieves very high accuracy ($\approx$0.9167) on the dataset provided by VirusTotal.
2021-09-16
Du, Xin, Tang, Songtao, Lu, Zhihui, Wet, Jie, Gai, Keke, Hung, Patrick C.K..  2020.  A Novel Data Placement Strategy for Data-Sharing Scientific Workflows in Heterogeneous Edge-Cloud Computing Environments. 2020 IEEE International Conference on Web Services (ICWS). :498–507.
The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.
Almohri, Hussain M. J., Watson, Layne T., Evans, David.  2020.  An Attack-Resilient Architecture for the Internet of Things. IEEE Transactions on Information Forensics and Security. 15:3940–3954.
With current IoT architectures, once a single device in a network is compromised, it can be used to disrupt the behavior of other devices on the same network. Even though system administrators can secure critical devices in the network using best practices and state-of-the-art technology, a single vulnerable device can undermine the security of the entire network. The goal of this work is to limit the ability of an attacker to exploit a vulnerable device on an IoT network and fabricate deceitful messages to co-opt other devices. The approach is to limit attackers by using device proxies that are used to retransmit and control network communications. We present an architecture that prevents deceitful messages generated by compromised devices from affecting the rest of the network. The design assumes a centralized and trustworthy machine that can observe the behavior of all devices on the network. The central machine collects application layer data, as opposed to low-level network traffic, from each IoT device. The collected data is used to train models that capture the normal behavior of each individual IoT device. The normal behavioral data is then used to monitor the IoT devices and detect anomalous behavior. This paper reports on our experiments using both a binary classifier and a density-based clustering algorithm to model benign IoT device behavior with a realistic test-bed, designed to capture normal behavior in an IoT-monitored environment. Results from the IoT testbed show that both the classifier and the clustering algorithms are promising and encourage the use of application-level data for detecting compromised IoT devices.
Conference Name: IEEE Transactions on Information Forensics and Security
Sarker, Partha S., Singh Saini, Amandeep, Sajan, K S, Srivastava, Anurag K..  2020.  CP-SAM: Cyber-Power Security Assessment and Resiliency Analysis Tool for Distribution System. 2020 Resilience Week (RWS). :188–193.
Cyber-power resiliency analysis of the distribution system is becoming critical with increase in adverse cyberevents. Distribution network operators need to assess and analyze the resiliency of the system utilizing the analytical tool with a carefully designed visualization and be driven by data and model-based analytics. This work introduces the Cyber-Physical Security Assessment Metric (CP-SAM) visualization tool to assist operators in ensuring the energy supply to critical loads during or after a cyber-attack. CP-SAM also provides decision support to operators utilizing measurement data and distribution power grid model and through well-designed visualization. The paper discusses the concepts of cyber-physical resiliency, software design considerations, open-source software components, and use cases for the tool to demonstrate the implementation and importance of the developed tool.
2021-09-07
Zhang, Yaofang, Wang, Bailing, Wu, Chenrui, Wei, Xiaojie, Wang, Zibo, Yin, Guohua.  2020.  Attack Graph-Based Quantitative Assessment for Industrial Control System Security. 2020 Chinese Automation Congress (CAC). :1748–1753.
Industrial control systems (ICSs) are facing serious security challenges due to their inherent flaws, and emergence of vulnerabilities from the integration with commercial components and networks. To that end, assessing the security plays a vital role for current industrial enterprises which are responsible for critical infrastructure. This paper accomplishes a complex task of quantitative assessment based on attack graphs in order to look forward critical paths. For the purpose of application to a large-scale heterogeneous ICSs, we propose a flexible attack graph generation algorithm is proposed with the help of the graph data model. Hereafter, our quantitative assessment takes a consideration of graph indicators on specific nodes and edges to get the security metrics. In order to improve results of obtaining the critical attack path, we introduced a formulating selection rule, considering the asset value of industrial control devices. The experimental results show validation and verification of the proposed method.
Lakshmi V., Santhana.  2020.  A Study on Machine Learning based Conversational Agents and Designing Techniques. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :965–968.
Chatbots are a computer program that was created to imitate the human during a conversation. In this technological era, humans were replaced by machines for performing most of the work. So chatbots were developed to mimic the conversation a human does with another person. The work a chatbot does ranges from answering simple queries to acting as personal assistant to the boss. There are different kinds of chatbots developed to cater to the needs of the people in different domain. The methodology of creating them also varies depending on their type. In this paper, the various types of chatbots and techniques such as Machine Learning, deep learning and natural language processing used for designing them were discussed in detail.
2021-08-31
Salimboyevich, Olimov Iskandar, Absamat ugli, Boriyev Yusuf, Akmuratovich, Sadikov Mahmudjon.  2020.  Making algorithm of improved key generation model and software. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—3.
In this paper is devoted methods for generating keys for cryptographic algorithms. Hash algorithms were analysed and learned linear and nonlinear. It was made up improved key generation algorithm and software.
Ebrahimian, Mahsa, Kashef, Rasha.  2020.  Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models. 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). :460–464.
Recommendation systems, especially collaborative filtering recommenders, are vulnerable to shilling attacks as some profit-driven users may inject fake profiles into the system to alter recommendation outputs. Current shilling attack detection methods are mostly based on feature extraction techniques. The hand-designed features can confine the model to specific domains or datasets while deep learning techniques enable us to derive deeper level features, enhance detection performance, and generalize the solution on various datasets and domains. This paper illustrates the application of two deep learning methods to detect shilling attacks. We conducted experiments on the MovieLens 100K and Netflix Dataset with different levels of attacks and types. Experimental results show that deep learning models can achieve an accuracy of up to 99%.
Hu, Dongfang, Xu, Bin, Wang, Jun, Han, Linfeng, Liu, Jiayi.  2020.  A Shilling Attack Model Based on TextCNN. 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). :282–289.
With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 - value as the evaluation index.
2021-08-17
Bicakci, Kemal, Salman, Oguzhan, Uzunay, Yusuf, Tan, Mehmet.  2020.  Analysis and Evaluation of Keystroke Dynamics as a Feature of Contextual Authentication. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :11—17.
The following topics are dealt with: authorisation; data privacy; mobile computing; security of data; cryptography; Internet of Things; message authentication; invasive software; Android (operating system); vectors.
Chen, Congwei, Elsayed, Marwa A., Zulkernine, Mohammad.  2020.  HBD-Authority: Streaming Access Control Model for Hadoop. 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys). :16–25.
Big data analytics, in essence, is becoming the revolution of business intelligence around the world. This momentum has given rise to the hype around analytic technologies, including Apache Hadoop. Hadoop was not originally developed with security in mind. Despite the evolving efforts to integrate security in Hadoop through developing new tools (e.g., Apache Sentry and Ranger) and employing traditional mechanisms (e.g., Kerberos and LDAP), they mainly focus on providing encryption and authentication features, albeit with limited authorization support. Existing solutions in the literature extended these evolving efforts. However, they suffer from limitations, hindering them from providing robust authorization that effectively meets the unique requirements of big data environments. Towards covering this gap, this paper proposes a hybrid authority (HBD-Authority) as a formal attribute-based access control model with context support. This model is established on a novel hybrid approach of authorization transparency that pertains to three fundamental properties of accuracy: correctness, security, and completeness. The model leverages streaming data analytics to foster distributed parallel processing capabilities that achieve multifold benefits: a) efficiently managing the security policies and promptly updating the privileges assigned to a high number of users interacting with the analytic services; b) swiftly deciding and enforcing authorization of requests over data characterized by the 5Vs; and c) providing dynamic protection for data which is frequently updated. The implementation details and experimental evaluation of the proposed model are presented, demonstrating its performance efficiency.
2021-08-11
Pan, Xiaoqin, Tang, Shaofei, Zhu, Zuqing.  2020.  Privacy-Preserving Multilayer In-Band Network Telemetry and Data Analytics. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :142—147.
As a new paradigm for the monitoring and troubleshooting of backbone networks, the multilayer in-band network telemetry (ML-INT) with deep learning (DL) based data analytics (DA) has recently been proven to be effective on realtime visualization and fine-grained monitoring. However, the existing studies on ML-INT&DA systems have overlooked the privacy and security issues, i.e., a malicious party can apply tapping in the data reporting channels between the data and control planes to illegally obtain plaintext ML-INT data in them. In this paper, we discuss a privacy-preserving DL-based ML-INT&DA system for realizing AI-assisted network automation in backbone networks in the form of IP-over-Optical. We first show a lightweight encryption scheme based on integer vector homomorphic encryption (IVHE), which is used to encrypt plaintext ML-INT data. Then, we architect a DL model for anomaly detection, which can directly analyze the ciphertext ML-INT data. Finally, we present the implementation and experimental demonstrations of the proposed system. The privacy-preserving DL-based ML-INT&DA system is realized in a real IP over elastic optical network (IP-over-EON) testbed, and the experimental results verify the feasibility and effectiveness of our proposal.
2021-08-02
Na, Yoonjong, Joo, Yejin, Lee, Heejo, Zhao, Xiangchen, Sajan, Kurian Karyakulam, Ramachandran, Gowri, Krishnamachari, Bhaskar.  2020.  Enhancing the Reliability of IoT Data Marketplaces through Security Validation of IoT Devices. 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). :265—272.
IoT data marketplaces are being developed to help cities and communities create large scale IoT applications. Such data marketplaces let the IoT device owners sell their data to the application developers. Following this application development model, the application developers need not deploy their own IoT devices when developing IoT applications; instead, they can buy data from a data marketplace. In a marketplace-based IoT application, the application developers are making critical business and operation decisions using the data produced by seller's IoT devices. Under these circumstances, it is crucial to verify and validate the security of IoT devices.In this paper, we assess the security of IoT data marketplaces. In particular, we discuss what kind of vulnerabilities exist in IoT data marketplaces using the well-known STRIDE model, and present a security assessment and certification framework for IoT data marketplaces to help the device owners to examine the security vulnerabilities of their devices. Most importantly, our solution certifies the IoT devices when they connect to the data marketplace, which helps the application developers to make an informed decision when buying and consuming data from a data marketplace. To demonstrate the effectiveness of the proposed approach, we have developed a proof-of-concept using I3 (Intelligent IoT Integrator), which is an open-source IoT data marketplace developed at the University of Southern California, and IoTcube, which is a vulnerability detection toolkit developed by researchers at Korea University. Through this work, we show that it is possible to increase the reliability of a IoT data marketplace while not damaging the convenience of the users.
Junchao, CHEN, Baorong, ZHAI, Yibing, DONG, Tao, WU, Kai, YOU.  2020.  Design Of TT amp;C Resource Automatic Scheduling Interface Middleware With High Concurrency and Security. 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS). :171—176.
In order to significantly improve the reliable interaction and fast processing when TT&C(Tracking, Telemetry and Command) Resource Scheduling and Management System (TRSMS) communicate with external systems which are diverse, multiple directional and high concurrent, this paper designs and implements a highly concurrent and secure middleware for TT&C Resource Automatic Scheduling Interface (TRASI). The middleware designs memory pool, data pool, thread pool and task pool to improve the efficiency of concurrent processing, uses the rule dictionary, communication handshake and wait retransmission mechanism to ensure the data interaction security and reliability. This middleware can effectively meet the requirements of TRASI for data exchange with external users and system, significantly improve the data processing speed and efficiency, and promote the information technology and automation level of Aerospace TT&C Network Management Center (TNMC).
2021-07-27
Ruiz-Martin, Cristina, Wainer, Gabriel, Lopez-Paredes, Adolfo.  2020.  Studying Communications Resiliency in Emergency Plans. 2020 Spring Simulation Conference (SpringSim). :1–12.
Recent disasters have shown that hazards can be unpredictable and can have catastrophic consequences. Emergency plans are key to dealing with these situations and communications play a key role in emergency management. In this paper, we provide a formalism to design resilient emergency plans in terms of communications. We exemplify how to use the formalism using a case study of a Nuclear Emergency Plan.
Xiao, Wenli, Jiang, Hao, Xia, Song.  2020.  A New Black Box Attack Generating Adversarial Examples Based on Reinforcement Learning. 2020 Information Communication Technologies Conference (ICTC). :141–146.
Machine learning can be misled by adversarial examples, which is formed by making small changes to the original data. Nowadays, there are kinds of methods to produce adversarial examples. However, they can not apply non-differentiable models, reduce the amount of calculations, and shorten the sample generation time at the same time. In this paper, we propose a new black box attack generating adversarial examples based on reinforcement learning. By using deep Q-learning network, we can train the substitute model and generate adversarial examples at the same time. Experimental results show that this method only needs 7.7ms to produce an adversarial example, which solves the problems of low efficiency, large amount of calculation and inapplicable to non-differentiable model.
Fan, Wenshu, Li, Hongwei, Jiang, Wenbo, Xu, Guowen, Lu, Rongxing.  2020.  A Practical Black-Box Attack Against Autonomous Speech Recognition Model. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1–6.
With the wild applications of machine learning (ML) technology, automatic speech recognition (ASR) has made great progress in recent years. Despite its great potential, there are various evasion attacks of ML-based ASR, which could affect the security of applications built upon ASR. Up to now, most studies focus on white-box attacks in ASR, and there is almost no attention paid to black-box attacks where attackers can only query the target model to get output labels rather than probability vectors in audio domain. In this paper, we propose an evasion attack against ASR in the above-mentioned situation, which is more feasible in realistic scenarios. Specifically, we first train a substitute model by using data augmentation, which ensures that we have enough samples to train with a small number of times to query the target model. Then, based on the substitute model, we apply Differential Evolution (DE) algorithm to craft adversarial examples and implement black-box attack against ASR models from the Speech Commands dataset. Extensive experiments are conducted, and the results illustrate that our approach achieves untargeted attacks with over 70% success rate while still maintaining the authenticity of the original data well.
Basu, Prithwish, Salonidis, Theodoros, Kraczek, Brent, Saghaian, Sayed M., Sydney, Ali, Ko, Bongjun, La Porta, Tom, Chan, Kevin.  2020.  Decentralized placement of data and analytics in wireless networks for energy-efficient execution. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :486—495.
We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints. We introduce an expressive analytics service hypergraph model for representing k-ary composability relationships (k ≥ 2) between various analytics and data components and leverage binary quadratic programming (BQP) to minimize the total energy consumption of a given placement of the analytics hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential energy functional Φ(·) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis Monte Carlo (MMC) sampling method which seeks to minimize Φ by moving analytics and data on the network. Although Φ is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium placement configuration. Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails.