Visible to the public Biblio

Found 932 results

Filters: First Letter Of Title is D  [Clear All Filters]
A B C [D] E F G H I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
D
Chacon, H., Silva, S., Rad, P..  2019.  Deep Learning Poison Data Attack Detection. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). :971—978.

Deep neural networks are widely used in many walks of life. Techniques such as transfer learning enable neural networks pre-trained on certain tasks to be retrained for a new duty, often with much less data. Users have access to both pre-trained model parameters and model definitions along with testing data but have either limited access to training data or just a subset of it. This is risky for system-critical applications, where adversarial information can be maliciously included during the training phase to attack the system. Determining the existence and level of attack in a model is challenging. In this paper, we present evidence on how adversarially attacking training data increases the boundary of model parameters using as an example of a CNN model and the MNIST data set as a test. This expansion is due to new characteristics of the poisonous data that are added to the training data. Approaching the problem from the feature space learned by the network provides a relation between them and the possible parameters taken by the model on the training phase. An algorithm is proposed to determine if a given network was attacked in the training by comparing the boundaries of parameters distribution on intermediate layers of the model estimated by using the Maximum Entropy Principle and the Variational inference approach.

Yang, J., Kang, X., Wong, E. K., Shi, Y..  2018.  Deep Learning with Feature Reuse for JPEG Image Steganalysis. 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). :533–538.
It is challenging to detect weak hidden information in a JPEG compressed image. In this paper, we propose a 32-layer convolutional neural networks (CNNs) with feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient and information, and the shared features and bottleneck layers in the proposed CNN model further reduce the number of parameters dramatically. The experimental results shown that the proposed method significantly reduce the detection error rate compared with the existing JPEG steganalysis methods, e.g. state-of-the-art XuNet method and the conventional SCA-GFR method. Compared with XuNet method and conventional method SCA-GFR in detecting J-UNIWARD at 0.1 bpnzAC (bit per non-zero AC DCT coefficient), the proposed method can reduce detection error rate by 4.33% and 6.55% respectively.
Ge, Mengmeng, Fu, Xiping, Syed, Naeem, Baig, Zubair, Teo, Gideon, Robles-Kelly, Antonio.  2019.  Deep Learning-Based Intrusion Detection for IoT Networks. 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC). :256—25609.

Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.

You, L., Li, Y., Wang, Y., Zhang, J., Yang, Y..  2016.  A deep learning-based RNNs model for automatic security audit of short messages. 2016 16th International Symposium on Communications and Information Technologies (ISCIT). :225–229.

The traditional text classification methods usually follow this process: first, a sentence can be considered as a bag of words (BOW), then transformed into sentence feature vector which can be classified by some methods, such as maximum entropy (ME), Naive Bayes (NB), support vector machines (SVM), and so on. However, when these methods are applied to text classification, we usually can not obtain an ideal result. The most important reason is that the semantic relations between words is very important for text categorization, however, the traditional method can not capture it. Sentiment classification, as a special case of text classification, is binary classification (positive or negative). Inspired by the sentiment analysis, we use a novel deep learning-based recurrent neural networks (RNNs)model for automatic security audit of short messages from prisons, which can classify short messages(secure and non-insecure). In this paper, the feature of short messages is extracted by word2vec which captures word order information, and each sentence is mapped to a feature vector. In particular, words with similar meaning are mapped to a similar position in the vector space, and then classified by RNNs. RNNs are now widely used and the network structure of RNNs determines that it can easily process the sequence data. We preprocess short messages, extract typical features from existing security and non-security short messages via word2vec, and classify short messages through RNNs which accept a fixed-sized vector as input and produce a fixed-sized vector as output. The experimental results show that the RNNs model achieves an average 92.7% accuracy which is higher than SVM.

Yang, Lingxiao, Liu, Risheng, Zhang, David, Zhang, Lei.  2017.  Deep Location-Specific Tracking. Proceedings of the 25th ACM International Conference on Multimedia. :1309–1317.

Convolutional Neural Network (CNN) based methods have shown significant performance gains in the problem of visual tracking in recent years. Due to many uncertain changes of objects online, such as abrupt motion, background clutter and large deformation, the visual tracking is still a challenging task. We propose a novel algorithm, namely Deep Location-Specific Tracking, which decomposes the tracking problem into a localization task and a classification task, and trains an individual network for each task. The localization network exploits the information in the current frame and provides a specific location to improve the probability of successful tracking, while the classification network finds the target among many examples generated around the target location in the previous frame, as well as the one estimated from the localization network in the current frame. CNN based trackers often have massive number of trainable parameters, and are prone to over-fitting to some particular object states, leading to less precision or tracking drift. We address this problem by learning a classification network based on 1 × 1 convolution and global average pooling. Extensive experimental results on popular benchmark datasets show that the proposed tracker achieves competitive results without using additional tracking videos for fine-tuning. The code is available at https://github.com/ZjjConan/DLST

Hitaj, Briland, Ateniese, Giuseppe, Perez-Cruz, Fernando.  2017.  Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :603–618.

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

Hou, Shifu, Saas, Aaron, Chen, Lingwei, Ye, Yanfang, Bourlai, Thirimachos.  2017.  Deep Neural Networks for Automatic Android Malware Detection. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. :803–810.
Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated API call blocks, we then explore deep neural networks (i.e., Deep Belief Network (DBN) and Stacked AutoEncoders (SAEs)) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. The experimental results demonstrate that (1) our proposed feature extraction method (i.e., using API call blocks) outperforms using API calls directly in Android malware detection; (2) DBN works better than SAEs in this application; and (3) the detection performance of deep neural networks is better than shallow learning architectures.
Ceška, Milan, Havlena, Vojtech, Holík, Lukáš, Korenek, Jan, Lengál, Ondrej, Matoušek, Denis, Matoušek, Jirí, Semric, Jakub, Vojnar, Tomáš.  2019.  Deep Packet Inspection in FPGAs via Approximate Nondeterministic Automata. 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). :109—117.

Deep packet inspection via regular expression (RE) matching is a crucial task of network intrusion detection systems (IDSes), which secure Internet connection against attacks and suspicious network traffic. Monitoring high-speed computer networks (100 Gbps and faster) in a single-box solution demands that the RE matching, traditionally based on finite automata (FAs), is accelerated in hardware. In this paper, we describe a novel FPGA architecture for RE matching that is able to process network traffic beyond 100 Gbps. The key idea is to reduce the required FPGA resources by leveraging approximate nondeterministic FAs (NFAs). The NFAs are compiled into a multi-stage architecture starting with the least precise stage with a high throughput and ending with the most precise stage with a low throughput. To obtain the reduced NFAs, we propose new approximate reduction techniques that take into account the profile of the network traffic. Our experiments showed that using our approach, we were able to perform matching of large sets of REs from SNORT, a popular IDS, on unprecedented network speeds.

Nyasore, O. N., Zavarsky, P., Swar, B., Naiyeju, R., Dabra, S..  2020.  Deep Packet Inspection in Industrial Automation Control System to Mitigate Attacks Exploiting Modbus/TCP Vulnerabilities. 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :241–245.
Modbus TCP/IP protocol is a commonly used protocol in industrial automation control systems, systems responsible for sensitive operations such as gas turbine operation and refinery control. The protocol was designed decades ago with no security features in mind. Denial of service attack and malicious parameter command injection are examples of attacks that can exploit vulnerabilities in industrial control systems that use Modbus/TCP protocol. This paper discusses and explores the use of intrusion detection and prevention systems (IDPS) with deep packet inspection (DPI) capabilities and DPI industrial firewalls that have capability to detect and stop highly specialized attacks hidden deep in the communication flow. The paper has the following objectives: (i) to develop signatures for IDPS for common attacks on Modbus/TCP based network architectures; (ii) to evaluate performance of three IDPS - Snort, Suricata and Bro - in detecting and preventing common attacks on Modbus/TCP based control systems; and (iii) to illustrate and emphasize that the IDPS and industrial firewalls with DPI capabilities are not preventing but only mitigating likelihood of exploitation of Modbus/TCP vulnerabilities in the industrial and automation control systems. The results presented in the paper illustrate that it might be challenging task to achieve requirements on real-time communication in some industrial and automation control systems in case the DPI is implemented because of the latency and jitter introduced by these IDPS and DPI industrial firewall.
Renukadevi, B., Raja, S. D. M..  2017.  Deep packet inspection Management application in SDN. 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). :256–259.

DPI Management application which resides on the north-bound of SDN architecture is to analyze the application signature data from the network. The data being read and analyzed are of format JSON for effective data representation and flows provisioned from North-bound application is also of JSON format. The data analytic engine analyzes the data stored in the non-relational data base and provides the information about real-time applications used by the network users. Allows the operator to provision flows dynamically with the data from the network to allow/block flows and also to boost the bandwidth. The DPI Management application allows decoupling of application with the controller; thus providing the facility to run it in any hyper-visor within network. Able to publish SNMP trap notifications to the network operators with application threshold and flow provisioning behavior. Data purging from non-relational database at frequent intervals to remove the obsolete analyzed data.

León, Raquel, Domínguez, Adrián, Carballo, Pedro P., Núñez, Antonio.  2019.  Deep Packet Inspection Through Virtual Platforms using System-On-Chip FPGAs. 2019 XXXIV Conference on Design of Circuits and Integrated Systems (DCIS). :1—6.

Virtual platforms provide a full hardware/software platform to study device limitations in an early stages of the design flow and to develop software without requiring a physical implementation. This paper describes the development process of a virtual platform for Deep Packet Inspection (DPI) hardware accelerators by using Transaction Level Modeling (TLM). We propose two DPI architectures oriented to System-on-Chip FPGA. The first architecture, CPU-DMA based architecture, is a hybrid CPU/FPGA where the packets are filtered in the software domain. The second architecture, Hardware-IP based architecture, is mainly implemented in the hardware domain. We have created two virtual platforms and performed the simulation, the debugging and the analysis of the hardware/software features, in order to compare results for both architectures.

Al-hisnawi, M., Ahmadi, M..  2017.  Deep packet inspection using Cuckoo filter. 2017 Annual Conference on New Trends in Information Communications Technology Applications (NTICT). :197–202.

Nowadays, Internet Service Providers (ISPs) have been depending on Deep Packet Inspection (DPI) approaches, which are the most precise techniques for traffic identification and classification. However, constructing high performance DPI approaches imposes a vigilant and an in-depth computing system design because the demands for the memory and processing power. Membership query data structures, specifically Bloom filter (BF), have been employed as a matching check tool in DPI approaches. It has been utilized to store signatures fingerprint in order to examine the presence of these signatures in the incoming network flow. The main issue that arise when employing Bloom filter in DPI approaches is the need to use k hash functions which, in turn, imposes more calculations overhead that degrade the performance. Consequently, in this paper, a new design and implementation for a DPI approach have been proposed. This DPI utilizes a membership query data structure called Cuckoo filter (CF) as a matching check tool. CF has many advantages over BF like: less memory consumption, less false positive rate, higher insert performance, higher lookup throughput, support delete operation. The achieved experiments show that the proposed approach offers better performance results than others that utilize Bloom filter.

Bai, Jiale, Ni, Bingbing, Wang, Minsi, Shen, Yang, Lai, Hanjiang, Zhang, Chongyang, Mei, Lin, Hu, Chuanping, Yao, Chen.  2017.  Deep Progressive Hashing for Image Retrieval. Proceedings of the 2017 ACM on Multimedia Conference. :208–216.

This paper proposes a novel recursive hashing scheme, in contrast to conventional "one-off" based hashing algorithms. Inspired by human's "nonsalient-to-salient" perception path, the proposed hashing scheme generates a series of binary codes based on progressively expanded salient regions. Built on a recurrent deep network, i.e., LSTM structure, the binary codes generated from later output nodes naturally inherit information aggregated from previously codes while explore novel information from the extended salient region, and therefore it possesses good scalability property. The proposed deep hashing network is trained via minimizing a triplet ranking loss, which is end-to-end trainable. Extensive experimental results on several image retrieval benchmarks demonstrate good performance gain over state-of-the-art image retrieval methods and its scalability property.

Zhang, Dajun, Yu, F. Richard, Yang, Ruizhe, Tang, Helen.  2018.  A Deep Reinforcement Learning-based Trust Management Scheme for Software-defined Vehicular Networks. Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications. :1–7.
Vehicular ad hoc networks (VANETs) have become a promising technology in intelligent transportation systems (ITS) with rising interest of expedient, safe, and high-efficient transportation. VANETs are vulnerable to malicious nodes and result in performance degradation because of dynamicity and infrastructure-less. In this paper, we propose a trust based dueling deep reinforcement learning approach (T-DDRL) for communication of connected vehicles, we deploy a dueling network architecture into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the most trusted routing path by deep neural network (DNN) in VANETs, where the trust model is designed to evaluate neighbors' behaviour of forwarding routing information. Simulation results are presented to show the effectiveness of the proposed T-DDRL framework.
Kadoguchi, M., Kobayashi, H., Hayashi, S., Otsuka, A., Hashimoto, M..  2020.  Deep Self-Supervised Clustering of the Dark Web for Cyber Threat Intelligence. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

In recent years, cyberattack techniques have become more and more sophisticated each day. Even if defense measures are taken against cyberattacks, it is difficult to prevent them completely. It can also be said that people can only fight defensively against cyber criminals. To address this situation, it is necessary to predict cyberattacks and take appropriate measures in advance, and the use of intelligence is important to make this possible. In general, many malicious hackers share information and tools that can be used for attacks on the dark web or in the specific communities. Therefore, we assume that a lot of intelligence, including this illegal content exists in cyber space. By using the threat intelligence, detecting attacks in advance and developing active defense is expected these days. However, such intelligence is currently extracted manually. In order to do this more efficiently, we apply machine learning to various forum posts that exist on the dark web, with the aim of extracting forum posts containing threat information. By doing this, we expect that detecting threat information in cyber space in a timely manner will be possible so that the optimal preventive measures will be taken in advance.

Qiu, Zhaofan, Pan, Yingwei, Yao, Ting, Mei, Tao.  2017.  Deep Semantic Hashing with Generative Adversarial Networks. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. :225–234.

Hashing has been a widely-adopted technique for nearest neighbor search in large-scale image retrieval tasks. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, the cost of annotating data is often an obstacle when applying supervised hashing to a new domain. Moreover, the results can suffer from the robustness problem as the data at training and test stage may come from different distributions. This paper studies the exploration of generating synthetic data through semi-supervised generative adversarial networks (GANs), which leverages largely unlabeled and limited labeled training data to produce highly compelling data with intrinsic invariance and global coherence, for better understanding statistical structures of natural data. We demonstrate that the above two limitations can be well mitigated by applying the synthetic data for hashing. Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an adversary stream to distinguish synthetic images from real ones, a hash stream for encoding image representations to hash codes and a classification stream. The whole architecture is trained end-to-end by jointly optimizing three losses, i.e., adversarial loss to correct label of synthetic or real for each sample, triplet ranking loss to preserve the relative similarity ordering in the input real-synthetic triplets and classification loss to classify each sample accurately. Extensive experiments conducted on both CIFAR-10 and NUS-WIDE image benchmarks validate the capability of exploiting synthetic images for hashing. Our framework also achieves superior results when compared to state-of-the-art deep hash models.

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.
Abbasi, Milad Haji, Majidi, Babak, Eshghi, Moahmmad, Abbasi, Ebrahim Haji.  2019.  Deep Visual Privacy Preserving for Internet of Robotic Things. 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). :292—296.

In the past few years, visual information collection and transmission is increased significantly for various applications. Smart vehicles, service robotic platforms and surveillance cameras for the smart city applications are collecting a large amount of visual data. The preservation of the privacy of people presented in this data is an important factor in storage, processing, sharing and transmission of visual data across the Internet of Robotic Things (IoRT). In this paper, a novel anonymisation method for information security and privacy preservation in visual data in sharing layer of the Web of Robotic Things (WoRT) is proposed. The proposed framework uses deep neural network based semantic segmentation to preserve the privacy in video data base of the access level of the applications and users. The data is anonymised to the applications with lower level access but the applications with higher legal access level can analyze and annotated the complete data. The experimental results show that the proposed method while giving the required access to the authorities for legal applications of smart city surveillance, is capable of preserving the privacy of the people presented in the data.

Ranakoti, P., Yadav, S., Apurva, A., Tomer, S., Roy, N. R..  2017.  Deep Web Online Anonymity. 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN). :215–219.

Deep web, a hidden and encrypted network that crawls beneath the surface web today has become a social hub for various criminals who carry out their crime through the cyber space and all the crime is being conducted and hosted on the Deep Web. This research paper is an effort to bring forth various techniques and ways in which an internet user can be safe online and protect his privacy through anonymity. Understanding how user's data and private information is phished and what are the risks of sharing personal information on social media.

Chen, Huili, Fu, Cheng, Rouhani, Bita Darvish, Zhao, Jishen, Koushanfar, Farinaz.  2019.  DeepAttest: An End-to-End Attestation Framework for Deep Neural Networks. 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA). :487–498.
Emerging hardware architectures for Deep Neural Networks (DNNs) are being commercialized and considered as the hardware- level Intellectual Property (IP) of the device providers. However, these intelligent devices might be abused and such vulnerability has not been identified. The unregulated usage of intelligent platforms and the lack of hardware-bounded IP protection impair the commercial advantage of the device provider and prohibit reliable technology transfer. Our goal is to design a systematic methodology that provides hardware-level IP protection and usage control for DNN applications on various platforms. To address the IP concern, we present DeepAttest, the first on-device DNN attestation method that certifies the legitimacy of the DNN program mapped to the device. DeepAttest works by designing a device-specific fingerprint which is encoded in the weights of the DNN deployed on the target platform. The embedded fingerprint (FP) is later extracted with the support of the Trusted Execution Environment (TEE). The existence of the pre-defined FP is used as the attestation criterion to determine whether the queried DNN is authenticated. Our attestation framework ensures that only authorized DNN programs yield the matching FP and are allowed for inference on the target device. DeepAttest provisions the device provider with a practical solution to limit the application usage of her manufactured hardware and prevents unauthorized or tampered DNNs from execution. We take an Algorithm/Software/Hardware co-design approach to optimize DeepAttest's overhead in terms of latency and energy consumption. To facilitate the deployment, we provide a high-level API of DeepAttest that can be seamlessly integrated into existing deep learning frameworks and TEEs for hardware-level IP protection and usage control. Extensive experiments corroborate the fidelity, reliability, security, and efficiency of DeepAttest on various DNN benchmarks and TEE-supported platforms.
Lu, Chris Xiaoxuan, Du, Bowen, Zhao, Peijun, Wen, Hongkai, Shen, Yiran, Markham, Andrew, Trigoni, Niki.  2018.  Deepauth: In-situ Authentication for Smartwatches via Deeply Learned Behavioural Biometrics. Proceedings of the 2018 ACM International Symposium on Wearable Computers. :204–207.

This paper proposes DeepAuth, an in-situ authentication framework that leverages the unique motion patterns when users entering passwords as behavioural biometrics. It uses a deep recurrent neural network to capture the subtle motion signatures during password input, and employs a novel loss function to learn deep feature representations that are robust to noise, unseen passwords, and malicious imposters even with limited training data. DeepAuth is by design optimised for resource constrained platforms, and uses a novel split-RNN architecture to slim inference down to run in real-time on off-the-shelf smartwatches. Extensive experiments with real-world data show that DeepAuth outperforms the state-of-the-art significantly in both authentication performance and cost, offering real-time authentication on a variety of smartwatches.

Khediri, Abderrazak, Laouar, Mohamed Ridda.  2018.  Deep-Belief Network Based Prediction Model for Power Outage in Smart Grid. Proceedings of the 4th ACM International Conference of Computing for Engineering and Sciences. :4:1-4:6.

The power outages of the last couple of years around the world introduce the indispensability of technological development to improve the traditional power grids. Early warnings of imminent failures represent one of the major required improvements. Costly blackouts throughout the world caused by the different severe incidents in traditional power grids have motivated researchers to diagnose and investigate previous blackouts and propose a prediction model that enables to prevent power outages. Although, in the new generation of power grid, the smart grid's (SG) real time data can be used from smart meters (SMs) and phasor measurement unit sensors (PMU) to prevent blackout, it demands high reliability and stability against power outages. This paper implements a proactive prediction model based on deep-belief networks that can predict imminent blackout. The proposed model is evaluated on a real smart grid dataset. Promising results are reported in the case study.

Anderson, Hyrum S., Woodbridge, Jonathan, Filar, Bobby.  2016.  DeepDGA: Adversarially-Tuned Domain Generation and Detection. Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. :13–21.

Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network. In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.

Yadav, D., Salmani, S..  2019.  Deepfake: A Survey on Facial Forgery Technique Using Generative Adversarial Network. 2019 International Conference on Intelligent Computing and Control Systems (ICCS). :852—857.
"Deepfake" it is an incipiently emerging face video forgery technique predicated on AI technology which is used for creating the fake video. It takes images and video as source and it coalesces these to make a new video using the generative adversarial network and the output is very convincing. This technique is utilized for generating the unauthentic spurious video and it is capable of making it possible to generate an unauthentic spurious video of authentic people verbally expressing and doing things that they never did by swapping the face of the person in the video. Deepfake can create disputes in countries by influencing their election process by defaming the character of the politician. This technique is now being used for character defamation of celebrities and high-profile politician just by swapping the face with someone else. If it is utilized in unethical ways, this could lead to a serious problem. Someone can use this technique for taking revenge from the person by swapping face in video and then posting it to a social media platform. In this paper, working of Deepfake technique along with how it can swap faces with maximum precision in the video has been presented. Further explained are the different ways through which we can identify if the video is generated by Deepfake and its advantages and drawback have been listed.
Zhu, K., Wu, B., Wang, B..  2020.  Deepfake Detection with Clustering-based Embedding Regularization. 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). :257—264.
In recent months, AI-synthesized face swapping videos referred to as deepfake have become an emerging problem. False video is becoming more and more difficult to distinguish, which brings a series of challenges to social security. Some scholars are devoted to studying how to improve the detection accuracy of deepfake video. At the same time, in order to conduct better research, some datasets for deepfake detection are made. Companies such as Google and Facebook have also spent huge sums of money to produce datasets for deepfake video detection, as well as holding deepfake detection competitions. The continuous advancement of video tampering technology and the improvement of video quality have also brought great challenges to deepfake detection. Some scholars have achieved certain results on existing datasets, while the results on some high-quality datasets are not as good as expected. In this paper, we propose new method with clustering-based embedding regularization for deepfake detection. We use open source algorithms to generate videos which can simulate distinctive artifacts in the deepfake videos. To improve the local smoothness of the representation space, we integrate a clustering-based embedding regularization term into the classification objective, so that the obtained model learns to resist adversarial examples. We evaluate our method on three latest deepfake datasets. Experimental results demonstrate the effectiveness of our method.