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2019-05-01
Li, P., Liu, Q., Zhao, W., Wang, D., Wang, S..  2018.  Chronic Poisoning against Machine Learning Based IDSs Using Edge Pattern Detection. 2018 IEEE International Conference on Communications (ICC). :1-7.

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). Poisoning attack, which is one of the most recognized security threats towards machine learning- based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.

Lu, X., Wan, X., Xiao, L., Tang, Y., Zhuang, W..  2018.  Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals. 2018 IEEE International Conference on Communications (ICC). :1-6.
Edge computing for mobile devices in vehicular ad hoc networks (VANETs) has to address rogue edge attacks, in which a rogue edge node claims to be the serving edge in the vehicle to steal user secrets and help launch other attacks such as man-in-the-middle attacks. Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units (OBUs) and the large-scale network infrastructure with roadside units (RSUs). In this paper, we propose a physical (PHY)- layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control (MAC) address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. We adopt a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model. Simulation results show that the Q-learning based detection scheme can significantly reduce the detection error rate and increase the utility compared with existing schemes.
Chen, D., Chen, W., Chen, J., Zheng, P., Huang, J..  2018.  Edge Detection and Image Segmentation on Encrypted Image with Homomorphic Encryption and Garbled Circuit. 2018 IEEE International Conference on Multimedia and Expo (ICME). :1-6.
Edge detection is one of the most important topics of image processing. In the scenario of cloud computing, performing edge detection may also consider privacy protection. In this paper, we propose an edge detection and image segmentation scheme on an encrypted image with Sobel edge detector. We implement Gaussian filtering and Sobel operator on the image in the encrypted domain with homomorphic property. By implementing an adaptive threshold decision algorithm in the encrypted domain, we obtain a threshold determined by the image distribution. With the technique of garbled circuit, we perform comparison in the encrypted domain and obtain the edge of the image without decrypting the image in advanced. We then propose an image segmentation scheme on the encrypted image based on the detected edges. Our experiments demonstrate the viability and effectiveness of the proposed encrypted image edge detection and segmentation.
Rayavel, P., Rathnavel, P., Bharathi, M., Kumar, T. Siva.  2018.  Dynamic Traffic Control System Using Edge Detection Algorithm. 2018 International Conference on Soft-Computing and Network Security (ICSNS). :1-5.
As the traffic congestion increases on the transport network, Payable on the road to slower speeds, longer falter times, as a consequence bigger vehicular queuing, it's necessary to introduce smart way to reduce traffic. We are already edging closer to ``smart city-smart travel''. Today, a large number of smart phone applications and connected sat-naves will help get you to your destination in the quickest and easiest manner possible due to real-time data and communication from a host of sources. In present situation, traffic lights are used in each phase. The other way is to use electronic sensors and magnetic coils that detect the congestion frequency and monitor traffic, but found to be more expensive. Hence we propose a traffic control system using image processing techniques like edge detection. The vehicles will be detected using images instead of sensors. The cameras are installed alongside of the road and it will capture image sequence for every 40 seconds. The digital image processing techniques will be applied to analyse and process the image and according to that the traffic signal lights will be controlled.
Berjab, N., Le, H. H., Yu, C., Kuo, S., Yokota, H..  2018.  Hierarchical Abnormal-Node Detection Using Fuzzy Logic for ECA Rule-Based Wireless Sensor Networks. 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). :289-298.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Pratama, R. F., Suwastika, N. A., Nugroho, M. A..  2018.  Design and Implementation Adaptive Intrusion Prevention System (IPS) for Attack Prevention in Software-Defined Network (SDN) Architecture. 2018 6th International Conference on Information and Communication Technology (ICoICT). :299-304.
Intrusion Prevention System (IPS) is a tool for securing networks from any malicious packet that could be sent from specific host. IPS can be installed on SDN network that has centralized logic architecture, so that IPS doesnt need to be installed on lots of nodes instead it has to be installed alongside the controller as center of logic network. IPS still has a flaw and that is the block duration would remain the same no matter how often a specific host attacks. For this reason, writer would like to make a system that not only integrates IPS on the SDN, but also designs an adaptive IPS by utilizing a fuzzy logic that can decide how long blocks are based on the frequency variable and type of attacks. From the results of tests that have been done, SDN network that has been equipped with adaptive IPS has the ability to detect attacks and can block the attacker host with the duration based on the frequency and type of attacks. The final result obtained is to make the SDN network safer by adding 0.228 milliseconds as the execute time required for the fuzzy algorithm in one process.
Sowah, R., Ofoli, A., Koumadi, K., Osae, G., Nortey, G., Bempong, A. M., Agyarkwa, B., Apeadu, K. O..  2018.  Design and Implementation of a Fire Detection andControl System with Enhanced Security and Safety for Automobiles Using Neuro-Fuzzy Logic. 2018 IEEE 7th International Conference on Adaptive Science Technology (ICAST). :1-8.
Automobiles provide comfort and mobility to owners. While they make life more meaningful they also pose challenges and risks in their safety and security mechanisms. Some modern automobiles are equipped with anti-theft systems and enhanced safety measures to safeguard its drivers. But at times, these mechanisms for safety and secured operation of automobiles are insufficient due to various mechanisms used by intruders and car thieves to defeat them. Drunk drivers cause accidents on our roads and thus the need to safeguard the driver when he is intoxicated and render the car to be incapable of being driven. These issues merit an integrated approach to safety and security of automobiles. In the light of these challenges, an integrated microcontroller-based hardware and software system for safety and security of automobiles to be fixed into existing vehicle architecture, was designed, developed and deployed. The system submodules are: (1) Two-step ignition for automobiles, namely: (a) biometric ignition and (b) alcohol detection with engine control, (2) Global Positioning System (GPS) based vehicle tracking and (3) Multisensor-based fire detection using neuro-fuzzy logic. All submodules of the system were implemented using one microcontroller, the Arduino Mega 2560, as the central control unit. The microcontroller was programmed using C++11. The developed system performed quite well with the tests performed on it. Given the right conditions, the alcohol detection subsystem operated with a 92% efficiency. The biometric ignition subsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiency in locations registered with the neuro-fuzzy system. The vehicle tracking subsystem operated with an efficiency of 90%.
Nadeem, Humaira, Rabbani, Imran Mujaddid, Aslam, Muhammad, M, Martinez Enriquez A..  2018.  KNN-Fuzzy Classification for Cloud Service Selection. Proceedings of the 2Nd International Conference on Future Networks and Distributed Systems. :66:1-66:8.
Cloud computing is an emerging technology that provides services to its users via Internet. It also allows sharing of resources there by reducing cost, money and space. With the popularity of cloud and its advantages, the trend of information industry shifting towards cloud services is increasing tremendously. Different cloud service providers are there on internet to provide services to the users. These services provided have certain parameters to provide better usage. It is difficult for the users to select a cloud service that is best suited to their requirements. Our proposed approach is based on data mining classification technique with fuzzy logic. Proposed algorithm uses cloud service design factors (security, agility and assurance etc.) and international standards to suggest the cloud service. The main objective of this research is to enable the end cloud users to choose best service as per their requirements and meeting international standards. We test our system with major cloud provider Google, Microsoft and Amazon.
Kotenko, Igor, Ageev, Sergey, Saenko, Igor.  2018.  Implementation of Intelligent Agents for Network Traffic and Security Risk Analysis in Cyber-Physical Systems. Proceedings of the 11th International Conference on Security of Information and Networks. :22:1-22:4.
The paper offers an approach for implementation of intelligent agents intended for network traffic and security risk analysis in cyber-physical systems. The agents are based on the algorithm of pseudo-gradient adaptive anomaly detection and fuzzy logical inference. The suggested algorithm operates in real time. The fuzzy logical inference is used for regulation of algorithm parameters. The variants of the implementation are proposed. The experimental assessment of the approach confirms its high speed and adequate accuracy for network traffic analysis.
Shirsat, S. D..  2018.  Demonstrating Different Phishing Attacks Using Fuzzy Logic. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :57-61.
Phishing has increased tremendously over last few years and it has become a serious threat to global security and economy. Existing literature dealing with the problem of phishing is scarce. Phishing is a deception technique that uses a combination of technology and social engineering to acquire sensitive information such as online banking passwords, credit card or bank account details [2]. Phishing can be done through emails and websites to collect confidential information. Phishers design fraudulent websites which look similar to the legitimate websites and lure the user to visit the malicious website. Therefore, the users must be aware of malicious websites to protect their sensitive data [1]. But it is very difficult to distinguish between legitimate and fake website especially for nontechnical users [4]. Moreover, phishing sites are growing rapidly. The aim of this paper is to demonstrate phishing detection using fuzzy logic and interpreting results using different defuzzification methods.
Pillutla, H., Arjunan, A..  2018.  A Brief Review of Fuzzy Logic and Its Usage Towards Counter-Security Issues. 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). :1-6.
Nowadays, most of the world's population has become much dependent on computers for banking, healthcare, shopping, and telecommunication. Security has now become a basic norm for computers and its resources since it has become inherently insecure. Security issues like Denial of Service attacks, TCP SYN Flooding attacks, Packet Dropping attacks and Distributed Denial of Service attacks are some of the methods by which unauthorized users make the resource unavailable to authorized users. There are several security mechanisms like Intrusion Detection System, Anomaly detection and Trust model by which we can be able to identify and counter the abuse of computer resources by unauthorized users. This paper presents a survey of several security mechanisms which have been implemented using Fuzzy logic. Fuzzy logic is one of the rapidly developing technologies, which is used in a sophisticated control system. Fuzzy logic deals with the degree of truth rather than the Boolean logic, which carries the values of either true or false. So instead of providing only two values, we will be able to define intermediate values.
Naik, N., Jenkins, P., Kerby, B., Sloane, J., Yang, L..  2018.  Fuzzy Logic Aided Intelligent Threat Detection in Cisco Adaptive Security Appliance 5500 Series Firewalls. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-8.
Cisco Adaptive Security Appliance (ASA) 5500 Series Firewall is amongst the most popular and technically advanced for securing organisational networks and systems. One of its most valuable features is its threat detection function which is available on every version of the firewall running a software version of 8.0(2) or higher. Threat detection operates at layers 3 and 4 to determine a baseline for network traffic, analysing packet drop statistics and generating threat reports based on traffic patterns. Despite producing a large volume of statistical information relating to several security events, further effort is required to mine and visually report more significant information and conclude the security status of the network. There are several commercial off-the-shelf tools available to undertake this task, however, they are expensive and may require a cloud subscription. Furthermore, if the information transmitted over the network is sensitive or requires confidentiality, the involvement of a third party or a third-party tool may place organisational security at risk. Therefore, this paper presents a fuzzy logic aided intelligent threat detection solution, which is a cost-free, intuitive and comprehensible solution, enhancing and simplifying the threat detection process for all. In particular, it employs a fuzzy reasoning system based on the threat detection statistics, and presents results/threats through a developed dashboard user interface, for ease of understanding for administrators and users. The paper further demonstrates the successful utilisation of a fuzzy reasoning system for selected and prioritised security events in basic threat detection, although it can be extended to encompass more complex situations, such as complete basic threat detection, advanced threat detection, scanning threat detection, and customised feature based threat detection.
Douzi, S., Benchaji, I., ElOuahidi, B..  2018.  Hybrid Approach for Intrusion Detection Using Fuzzy Association Rules. 2018 2nd Cyber Security in Networking Conference (CSNet). :1-3.
Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.
Chen, Yudong, Su, Lili, Xu, Jiaming.  2018.  Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent. Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems. :96-96.
We consider the distributed statistical learning problem over decentralized systems that are prone to adversarial attacks. This setup arises in many practical applications, including Google's Federated Learning. Formally, we focus on a decentralized system that consists of a parameter server and m working machines; each working machine keeps N/m data samples, where N is the total number of samples. In each iteration, up to q of the m working machines suffer Byzantine faults – a faulty machine in the given iteration behaves arbitrarily badly against the system and has complete knowledge of the system. Additionally, the sets of faulty machines may be different across iterations. Our goal is to design robust algorithms such that the system can learn the underlying true parameter, which is of dimension d, despite the interruption of the Byzantine attacks. In this paper, based on the geometric median of means of the gradients, we propose a simple variant of the classical gradient descent method. We show that our method can tolerate q Byzantine failures up to 2(1+$ε$)q łe m for an arbitrarily small but fixed constant $ε$0. The parameter estimate converges in O(łog N) rounds with an estimation error on the order of max $\surd$dq/N, \textasciitilde$\surd$d/N , which is larger than the minimax-optimal error rate $\surd$d/N in the centralized and failure-free setting by at most a factor of $\surd$q . The total computational complexity of our algorithm is of O((Nd/m) log N) at each working machine and O(md + kd log 3 N) at the central server, and the total communication cost is of O(m d log N). We further provide an application of our general results to the linear regression problem. A key challenge arises in the above problem is that Byzantine failures create arbitrary and unspecified dependency among the iterations and the aggregated gradients. To handle this issue in the analysis, we prove that the aggregated gradient, as a function of model parameter, converges uniformly to the true gradient function.
Tsunashima, Hideki, Hoshi, Taisei, Chen, Qiu.  2018.  DzGAN: Improved Conditional Generative Adversarial Nets Using Divided Z-Vector. Proceedings of the 2018 International Conference on Computing and Big Data. :52-55.
Conditional Generative Adversarial Nets [1](cGAN) was recently proposed as a novel conditional learning method by feeding some extra information into the network. In this paper we propose an improved conditional GANs which use divided z-vector (DzGAN). The computation amount will be reduced because DzGAN can implement conditional learning using not images but one-hot vector by dividing the range of z-vector (e.g. -1\textasciitilde1 to -1\textasciitilde0 and 0\textasciitilde1). In the DzGAN, the discriminator is fed by the images with label using one-hot vector and the generator is fed by divided z-vector (e.g. there are 10 classes In MNIST dataset, the divided z-vector will be z1\textasciitildez10 accordingly) with corresponding label fed into the discriminator, thus we can implement conditional learning. In this paper we use conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) [7] instead of cGAN because cDCGAN can generate clear image better than cGAN. Heuristic experiments of conditional learning which compare the computation amount demonstrate that DzGAN is superior than cDCGAN.
Zhu, Dandan, Dai, Lei, Zhang, Guokai, Shao, Xuan, Luo, Ye, Lu, Jianwei.  2018.  MAFL: Multi-Scale Adversarial Feature Learning for Saliency Detection. Proceedings of the 2018 International Conference on Control and Computer Vision. :90-95.
Previous saliency detection methods usually focus on extracting features to deal with the complex background in an image. However, these methods cannot effectively capture the semantic information of images. In recent years, Generative Adversarial Network (GAN) has become a prevalent research topic. Experiments show that GAN has ability to generate high quality images that look like natural images. Inspired by the effectiveness of GAN feature learning, we propose a novel multi-scale adversarial feature learning (MAFL) model for saliency detection. In particular, we model the complete framework of saliency detection is based on two deep CNN modules: the multi-scale G-network takes natural images as inputs and generates corresponding synthetic saliency map, and we designed a novel layer in D-network, namely a correlation layer, which is used to determine whether one image is a synthetic saliency map or ground-truth saliency map. Quantitative and qualitative experiments on three benchmark datasets demonstrate that our method outperforms seven state-of-the-art methods.
Wang, Bingning, Liu, Kang, Zhao, Jun.  2018.  Deep Semantic Hashing with Multi-Adversarial Training. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. :1453-1462.
With the amount of data has been rapidly growing over recent decades, binary hashing has become an attractive approach for fast search over large databases, in which the high-dimensional data such as image, video or text is mapped into a low-dimensional binary code. Searching in this hamming space is extremely efficient which is independent of the data size. A lot of methods have been proposed to learn this binary mapping. However, to make the binary codes conserves the input information, previous works mostly resort to mean squared error, which is prone to lose a lot of input information [11]. On the other hand, most of the previous works adopt the norm constraint or approximation on the hidden representation to make it as close as possible to binary, but the norm constraint is too strict that harms the expressiveness and flexibility of the code. In this paper, to generate desirable binary codes, we introduce two adversarial training procedures to the hashing process. We replace the L2 reconstruction error with an adversarial training process to make the codes reserve its input information, and we apply another adversarial learning discriminator on the hidden codes to make it proximate to binary. With the adversarial training process, the generated codes are getting close to binary while also conserves the input information. We conduct comprehensive experiments on both supervised and unsupervised hashing applications and achieves a new state of the arts result on many image hashing benchmarks.
Seo, Sanghyun, Jeon, Yongjin, Kim, Juntae.  2018.  Meta Learning for Imbalanced Big Data Analysis by Using Generative Adversarial Networks. Proceedings of the 2018 International Conference on Big Data and Computing. :5-9.
Imbalanced big data means big data where the ratio of a certain class is relatively small compared to other classes. When the machine learning model is trained by using imbalanced big data, the problem with performance drops for the minority class occurs. For this reason, various oversampling methodologies have been proposed, but simple oversampling leads to problem of the overfitting. In this paper, we propose a meta learning methodology for efficient analysis of imbalanced big data. The proposed meta learning methodology uses the meta information of the data generated by the generative model based on Generative Adversarial Networks. It prevents the generative model from becoming too similar to the real data in minority class. Compared to the simple oversampling methodology for analyzing imbalanced big data, it is less likely to cause overfitting. Experimental results show that the proposed method can efficiently analyze imbalanced big data.
Cohen, Daniel, Mitra, Bhaskar, Hofmann, Katja, Croft, W. Bruce.  2018.  Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. :1025-1028.
Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly from the data, however, may come at a price. Without any special supervision, these models learn relationships that may hold only in the domain from which the training data is sampled, and generalize poorly to domains not observed during training. We study the effectiveness of adversarial learning as a cross domain regularizer in the context of the ranking task. We use an adversarial discriminator and train our neural ranking model on a small set of domains. The discriminator provides a negative feedback signal to discourage the model from learning domain specific representations. Our experiments show consistently better performance on held out domains in the presence of the adversarial discriminator–-sometimes up to 30% on precision\$@1\$.
Yu, Wenchao, Zheng, Cheng, Cheng, Wei, Aggarwal, Charu C., Song, Dongjin, Zong, Bo, Chen, Haifeng, Wang, Wei.  2018.  Learning Deep Network Representations with Adversarially Regularized Autoencoders. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2663-2671.
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.
Tirupattur, Praveen, Rawat, Yogesh Singh, Spampinato, Concetto, Shah, Mubarak.  2018.  ThoughtViz: Visualizing Human Thoughts Using Generative Adversarial Network. Proceedings of the 26th ACM International Conference on Multimedia. :950-958.
Studying human brain signals has always gathered great attention from the scientific community. In Brain Computer Interface (BCI) research, for example, changes of brain signals in relation to specific tasks (e.g., thinking something) are detected and used to control machines. While extracting spatio-temporal cues from brain signals for classifying state of human mind is an explored path, decoding and visualizing brain states is new and futuristic. Following this latter direction, in this paper, we propose an approach that is able not only to read the mind, but also to decode and visualize human thoughts. More specifically, we analyze brain activity, recorded by an ElectroEncephaloGram (EEG), of a subject while thinking about a digit, character or an object and synthesize visually the thought item. To accomplish this, we leverage the recent progress of adversarial learning by devising a conditional Generative Adversarial Network (GAN), which takes, as input, encoded EEG signals and generates corresponding images. In addition, since collecting large EEG signals in not trivial, our GAN model allows for learning distributions with limited training data. Performance analysis carried out on three different datasets – brain signals of multiple subjects thinking digits, characters, and objects – show that our approach is able to effectively generate images from thoughts of a person. They also demonstrate that EEG signals encode explicitly cues from thoughts which can be effectively used for generating semantically relevant visualizations.
Zhao, Bo, Wu, Xiao, Cheng, Zhi-Qi, Liu, Hao, Jie, Zequn, Feng, Jiashi.  2018.  Multi-View Image Generation from a Single-View. Proceedings of the 26th ACM International Conference on Multimedia. :383-391.
How to generate multi-view images with realistic-looking appearance from only a single view input is a challenging problem. In this paper, we attack this problem by proposing a novel image generation model termed VariGANs, which combines the merits of the variational inference and the Generative Adversarial Networks (GANs). It generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produces coarse images of different views. Conditioned on the generated coarse images, it then performs adversarial learning to fill details consistent with the input and generate the fine images. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that the generated images with the proposed VariGANs are more plausible than those generated by existing approaches, which provide more consistent global appearance as well as richer and sharper details.
2019-04-29
Champagne, Samuel, Makanju, Tokunbo, Yao, Chengchao, Zincir-Heywood, Nur, Heywood, Malcolm.  2018.  A Genetic Algorithm for Dynamic Controller Placement in Software Defined Networking. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1632–1639.
The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.
Chiu, Pei-Ling, Lee, Kai-Hui.  2018.  Optimization Based Adaptive Tagged Visual Cryptography. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :33–34.
The Tagged Visual Cryptography Scheme (TVCS)1 adds tag images to the noise-like shares generated by the traditional VCS to improve the shares management of the traditional VCS. However, the existing TVCSs suffers visual quality of the recovered secret image may be degraded and there may be pixel expansion. This study proposes a Threshold Adaptive Tagged Visual Cryptography Scheme ((k, n)-ATVCS) to solve the above-mentioned problems. The ATVCS encryption problem is formulated in a mathematical optimization model, and an evolutionary algorithm is developed to find the optimal solution to the problem. The proposed (k, n)-ATVCS enables the encryptor to adjust the visual quality between the tag image and the secret image by tuning parameters. Experimental results show the correctness and effectiveness of this study.
Harris, Sean, Michalak, Eric, Schoonover, Kevin, Gausmann, Adam, Reinbolt, Hannah, Herman, Joshua, Tauritz, Daniel, Rawlings, Chris, Pope, Aaron Scott.  2018.  Evolution of Network Enumeration Strategies in Emulated Computer Networks. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1640–1647.
Successful attacks on computer networks today do not often owe their victory to directly overcoming strong security measures set up by the defender. Rather, most attacks succeed because the number of possible vulnerabilities are too large for humans to fully protect without making a mistake. Regardless of the security elsewhere, a skilled attacker can exploit a single vulnerability in a defensive system and negate the benefits of those security measures. This paper presents an evolutionary framework for evolving attacker agents in a real, emulated network environment using genetic programming, as a foundation for coevolutionary systems which can automatically discover and mitigate network security flaws. We examine network enumeration, an initial network reconnaissance step, through our framework and present results demonstrating its success, indicating a broader applicability to further cyber-security tasks.