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Publication Certificate IMPACT FACTOR 2.475 Certificate of publication for the article titled: TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems Authored by: Xiaolei Liu; Xiaosong Zhang; Nadra Guizani; Jiazhong Lu; Qingxin Zhu; Xiaojiang Du Published in: Sensors 2018, Volume 18, Issue 8, 2630 IMPACT FACTOR 2.475 Certificate of publication for the article titled: Adversarial Samples on Android Malware Detection Systems for IoT Systems Authored by: Xiaolei Liu; Xiaojiang Du; Xiaosong Zhang; Qingxin Zhu; Hao Wang; Mohsen Guizani Published in: Sensors 2019, Volume 19, Issue 4, 974 Available online at www.sciencedirect.com ScienceDirect Cognitive Systems Research 54 (2019) 83–89 www.elsevier.com/locate/cogsys Adversarial attacks against profile HMM website fingerprinting detection model Xiaolei Liu a,⇑, Zhongliu Zhuo b, Xiaojiang Du c, Xiaosong Zhang b, Qingxin Zhu a, Mohsen Guizani d a School of Information and Software Engineering, University of Electronic and Science Technology of China, Chengdu, China b Center for Cyber Security, University of Electronic and Science Technology of China, Chengdu, China c Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA d Dept. of Electrical and Computer Engineering, University of Idaho, Moscow, ID, USA Received 22 October 2018; received in revised form 24 November 2018; accepted 11 December 2018 Available online 21 December 2018 Abstract People are accustomed to using an anonymous network to protect their private information. The Profile HMM (Hidden Markov Model) Website Fingerprinting Detection algorithm can detect the website that the data stream accesses by pattern matching the cap- tured data traffic. This makes the anonymous network lose its effect. In order to bypass the detection of this model, we propose a method based on genetic algorithm to generate adversarial samples. By migrating the problem of adversarial samples in deep learning, our approach is used for the broader machine learning detection model to do traffic confusion, and then achieves the purpose of bypassing the Profile HMM model detection. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample at minimal cost. The experimental results show that the success rate of our traffic confusion method is as high as 97%. At the same time, we only need to add less perturbation traffic than the traditional traffic confusion method. Ó 2018 Elsevier B.V. All rights reserved. Keywords: Adversarial samples; PHMM; Traffic confusion 1. Introduction (Thurlow, Lengel, & Tomic, 2004), I2P (Zantout & Haraty, 2011), SSH or VPN tunneling, etc. Some articles In recent years, more and more people choose to use have studied their safety (Cheng, Fu, Du, Luo, & anonymous network to browse the web pages in order to Guizani, 2017; Du, Xiao, Guizani, & Chen, 2007; Hei, better protect their privacy information and avoid targeted Du, Lin, & Lee, 2013; Wu, Du, & Wu, 2016). For these advertising or even more unexpected hacker attacks (Kim, anonymous networks, researchers have proposed a number Han, Ha, Kim, & Han, 2017; Smith, Strohmeier, Lenders, of fingerprint-based attacks and Profile HMM Website & Martinovic, 2016). Common anonymous networks Fingerprinting Attack (Zhuo, Zhang, Zhang, Zhang, & include Tor (Syverson, Dingledine, & Mathewson, 2004), Zhang, 2018) is one of the most effective attacks. At the Shadowsocks (Clowwindy & Max, 2016), Anonymizer same time, with the development of deep learning technol- ogy, researchers find that the common deep learning mod- els show some vulnerability to the adversarial samples ⇑ Corresponding author. (Carlini & Wagner, 2017; Fawzi, Dezfooli, & Frossard, E-mail address: [email protected] (X. Liu). https://doi.org/10.1016/j.cogsys.2018.12.005 1389-0417/Ó 2018 Elsevier B.V. All rights reserved. Web of Science [v.5.22.3] - Web of Science 1/2 Web of Science TM InCites TM Journal Citation Reports ® Essential Science Indicators SM EndNote TM 1 1 EndNote online Modified t-Distribution Evolutionary Algorithm for Dynamic Deployment of Wireless Sensor Networks : Liu, XL (Liu, Xiaolei)[ 1 ] ; Zhang, XS (Zhang, Xiaosong)[ 2 ] ; Jiang, YQ (Jiang, Yiqi)[ 2 ] ; Zhu, QX 0 (Zhu, Qingxin)[ 1 ] 14 Related Records IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS : E99D : 6 : 1595-1602 DOI: 10.1587/transinf.2015EDP7464 ( Web of Science TM ) : JUN 2016 Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless 0 / sensor networks research, helps improve the coverage of the networks and the system reliability. In this 0 / Web of Science paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by 0 / BIOSIS Citation Index introducing a deployment optimization operator and an intelligent allocation operator. A directed 0 / perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to 0 / Data Citation Index 0 / Russian Science Citation Index speed up the convergence. In addition, with a new geometric sensor detection model instead of the old 0 / SciELO Citation Index probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively. Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value. 180 : 0 2013 : 0 : t-distribution; evolutionary algorithm; wireless sensor networks KeyWords Plus: SWARM OPTIMIZATION : Web of Science TM : Zhang, XS ( ) Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Big Data Res Ctr, Chengdu 611731, Peoples R China. : [ 1 ] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China [ 2 ] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Big Data Res Ctr, Chengdu 611731, Peoples R China : [email protected] 61572115 National Natural Science Foundation of China 61502086 61402080 Chinese Postdoctoral Science Foundation 2014M562307 IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN / http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=Genera... 2016/10/8 Web of Science [v.5.22.3] - Web of Science 2/2 : Computer Science Web of Science : Computer Science, Information Systems; Computer Science, Software Engineering : Article : English : WOS:000381562200022 ISSN: 1745-1361 Impact Factor ( ): Journal Citation Reports ® IDS : DT5ZM Web of Science " ": 14 Web of Science " ": 0 1 1 © 2016 THOMSON REUTERS http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=Genera... 2016/10/8 Computers and Electrical Engineering 80 (2019) 106493 Contents lists available at ScienceDirect Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng Evolution-algorithm-base d unmanne d aerial vehicles path ✩ planning in complex environment ∗ Xiaolei Liu a, , Xiaojiang Du b, Xiaosong Zhang c, Qingxin Zhu a, Mohsen Guizani d a School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China b Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA c Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu, China d Department of Computer Science and Engineering, Qatar University, Doha, Qatar a r t i c l e i n f o a b s t r a c t Article history: With the wide application of Unmanned Aerial Vehicles (UAVs) in production and life, Received 4 November 2018 more and more attention has been paid to the autonomous track planning of UAVs. When Revised 15 June 2019 UAV path planning algorithm is dealing with flying in an unknown complex environment, Accepted 12 October 2019 there are some problems, such as inability to dynamically plan the track and slow speed to calculate the path. This paper proposes a dynamic path planning based on an improved Keywords: evolutionary optimization algorithm. The experimental results show that the evolution- UAV ary optimization algorithm based on improved t-distribution can effectively deal with the Dynamic planning problems of high computational complexity and low search efficiency encountered in UAV Path planning dynamic track planning. It has strong robustness and can dynamically plan the appropriate Evolution algorithm track. © 2019 Elsevier Ltd. All rights reserved. 1. Introduction Unmanned Aerial Vehicle (UAVs) path planning refers to a feasible and satisfactory plan for UAVs under the premise of considering the maneuverability, the surrounding environment threats and the mission time. The flight route can ensure the safety of UAVs and can complete specific tasks. UAVs path planning is one of the cores of the Mission Planning System and is widely used in control systems for robots, drones, missiles, etc. [1,2] . Traditional route planning methods include sketch-based planning methods, cell-decomposition-based planning methods, artificial potential-based planning methods [3–7] , etc. The planning method based on the sketch map usually first converts the 3D scene into a 2D plan and then solves the problem by using the network map search method. This method is less efficient when dealing with high-dimensional problems, and it is not possible to update planned routes in real time based on environmental
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