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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.
bin Asad, Ashub, Mansur, Raiyan, Zawad, Safir, Evan, Nahian, Hossain, Muhammad Iqbal.  2020.  Analysis of Malware Prediction Based on Infection Rate Using Machine Learning Techniques. 2020 IEEE Region 10 Symposium (TENSYMP). :706–709.
In this modern, technological age, the internet has been adopted by the masses. And with it, the danger of malicious attacks by cybercriminals have increased. These attacks are done via Malware, and have resulted in billions of dollars of financial damage. This makes the prevention of malicious attacks an essential part of the battle against cybercrime. In this paper, we are applying machine learning algorithms to predict the malware infection rates of computers based on its features. We are using supervised machine learning algorithms and gradient boosting algorithms. We have collected a publicly available dataset, which was divided into two parts, one being the training set, and the other will be the testing set. After conducting four different experiments using the aforementioned algorithms, it has been discovered that LightGBM is the best model with an AUC Score of 0.73926.
Patil, Rajvardhan, Deng, Wei.  2020.  Malware Analysis using Machine Learning and Deep Learning techniques. 2020 SoutheastCon. 2:1–7.
In this era, where the volume and diversity of malware is rising exponentially, new techniques need to be employed for faster and accurate identification of the malwares. Manual heuristic inspection of malware analysis are neither effective in detecting new malware, nor efficient as they fail to keep up with the high spreading rate of malware. Machine learning approaches have therefore gained momentum. They have been used to automate static and dynamic analysis investigation where malware having similar behavior are clustered together, and based on the proximity unknown malwares get classified to their respective families. Although many such research efforts have been conducted where data-mining and machine-learning techniques have been applied, in this paper we show how the accuracy can further be improved using deep learning networks. As deep learning offers superior classification by constructing neural networks with a higher number of potentially diverse layers it leads to improvement in automatic detection and classification of the malware variants.In this research, we present a framework which extracts various feature-sets such as system calls, operational codes, sections, and byte codes from the malware files. In the experimental and result section, we compare the accuracy obtained from each of these features and demonstrate that feature vector for system calls yields the highest accuracy. The paper concludes by showing how deep learning approach performs better than the traditional shallow machine learning approaches.
Brzezinski Meyer, Maria Laura, Labit, Yann.  2020.  Combining Machine Learning and Behavior Analysis Techniques for Network Security. 2020 International Conference on Information Networking (ICOIN). :580–583.
Network traffic attacks are increasingly common and varied, this is a big problem especially when the target network is centralized. The creation of IDS (Intrusion Detection Systems) capable of detecting various types of attacks is necessary. Machine learning algorithms are widely used in the classification of data, bringing a good result in the area of computer networks. In addition, the analysis of entropy and distance between data sets are also very effective in detecting anomalies. However, each technique has its limitations, so this work aims to study their combination in order to improve their performance and create a new intrusion detection system capable of well detect some of the most common attacks. Reliability indices will be used as metrics to the combination decision and they will be updated in each new dataset according to the decision made earlier.
Wu, Qiang, Zhang, Jiliang.  2020.  CT PUF: Configurable Tristate PUF against Machine Learning Attacks. 2020 IEEE International Symposium on Circuits and Systems (ISCAS). :1–5.
Strong physical unclonable function (PUF) is a promising lightweight hardware security primitive for device authentication. However, it is vulnerable to machine learning attacks. This paper demonstrates that even a recently proposed dual-mode PUF is still can be broken. In order to improve the security, this paper proposes a highly flexible machine learning resistant configurable tristate (CT) PUF which utilizes the response generated in the working state of Arbiter PUF to XOR the challenge input and response output of other two working states (ring oscillator (RO) PUF and bitable ring (BR) PUF). The proposed CT PUF is implemented on Xilinx Artix-7 FPGAs and the experiment results show that the modeling accuracy of logistic regression and artificial neural network is reduced to the mid-50%.
Zhao, Quanling, Sun, Jiawei, Ren, Hongjia, Sun, Guodong.  2020.  Machine-Learning Based TCP Security Action Prediction. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :1329–1333.
With the continuous growth of Internet technology and the increasingly broadening applications of The Internet, network security incidents as well as cyber-attacks are also showing a growing trend. Consequently, computer network security is becoming increasingly important. TCP firewall is a computer network security system, and it allows or denies the transmission of data according to specific rules for providing security for the computer network. Traditional firewalls rely on network administrators to set security rules for them, and network administrators sometimes need to choose to allow and deny packets to keep computer networks secure. However, due to the huge amount of data on the Internet, network administrators have a huge task. Therefore, it is particularly important to solve this problem by using the machine learning method of computer technology. This study aims to predict TCP security action based on the TCP transmission characteristics dataset provided by UCI machine learning repository by implementing machine learning models such as neural network, support vector machine (SVM), AdaBoost, and Logistic regression. Processes including evaluating various models and interpretability analysis. By utilizing the idea of ensemble-learning, the final result has an accuracy score of over 98%.
Guo, Minghao, Yang, Yuzhe, Xu, Rui, Liu, Ziwei, Lin, Dahua.  2020.  When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :628–637.
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom. The sampled architectures together with the accuracies they achieve provide a rich basis for our study. Our ''robust architecture Odyssey'' reveals several valuable observations: 1) densely connected patterns result in improved robustness; 2) under computational budget, adding convolution operations to direct connection edge is effective; 3) flow of solution procedure (FSP) matrix is a good indicator of network robustness. Based on these observations, we discover a family of robust architectures (RobNets). On various datasets, including CIFAR, SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness performance to other widely used architectures. Notably, RobNets substantially improve the robust accuracy ( 5% absolute gains) under both white-box and black-box attacks, even with fewer parameter numbers. Code is available at
Vamsi, G Krishna, Rasool, Akhtar, Hajela, Gaurav.  2020.  Chatbot: A Deep Neural Network Based Human to Machine Conversation Model. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.
A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. The crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. This is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost every customer communication platform, like social networks. At present, there are two basic models used in developing a chatbot. Generative based models and Retrieval based models. The recent advancements in deep learning and artificial intelligence, such as the end-to-end trainable neural networks have rapidly replaced earlier methods based on hand-written instructions and patterns or statistical methods. This paper proposes a new method of creating a chatbot using a deep neural learning method. In this method, a neural network with multiple layers is built to learn and process the data.
Kalkan, Soner Can, Sahingoz, Ozgur Koray.  2020.  In-Vehicle Intrusion Detection System on Controller Area Network with Machine Learning Models. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.
Parallel with the developing world, transportation technologies have started to expand and change significantly year by year. This change brings with it some inevitable problems. Increasing human population and growing transportation-needs result many accidents in urban and rural areas, and this recursively results extra traffic problems and fuel consumption. It is obvious that the issues brought by this spiral loop needed to be solved with the use of some new technological achievements. In this context, self-driving cars or automated vehicles concepts are seen as a good solution. However, this also brings some additional problems with it. Currently many cars are provided with some digital security systems, which are examined in two phases, internal and external. These systems are constructed in the car by using some type of embedded system (such as the Controller Area Network (CAN)) which are needed to be protected form outsider cyberattacks. These attack can be detected by several ways such as rule based system, anomaly based systems, list based systems, etc. The current literature showed that researchers focused on the use of some artificial intelligence techniques for the detection of this type of attack. In this study, an intrusion detection system based on machine learning is proposed for the CAN security, which is the in-vehicle communication structure. As a result of the study, it has been observed that the decision tree-based ensemble learning models results the best performance in the tested models. Additionally, all models have a very good accuracy levels.
Manavi, Farnoush, Hamzeh, Ali.  2020.  A New Method for Ransomware Detection Based on PE Header Using Convolutional Neural Networks. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :82–87.
With the spread of information technology in human life, data protection is a critical task. On the other hand, malicious programs are developed, which can manipulate sensitive and critical data and restrict access to this data. Ransomware is an example of such a malicious program that encrypts data, restricts users' access to the system or their data, and then request a ransom payment. Many types of research have been proposed for ransomware detection. Most of these methods attempt to identify ransomware by relying on program behavior during execution. The main weakness of these methods is that it is not clear how long the program should be monitored to show its real behavior. Therefore, sometimes, these researches cannot early detect ransomware. In this paper, a new method for ransomware detection is proposed that does not require running the program and uses the PE header of the executable files. To extract effective features from the PE header files, an image based on PE header is constructed. Then, according to the advantages of Convolutional Neural Networks in extracting features from images and classifying them, CNN is used. The proposed method achieves 93.33% accuracy. Our results indicate the usefulness and practicality method for ransomware detection.
S, Kanthimathi, Prathuri, Jhansi Rani.  2020.  Classification of Misbehaving nodes in MANETS using Machine Learning Techniques. 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). :1–2.
Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput.
Bao, Zhida, Zhao, Haojun.  2020.  Evaluation of Adversarial Attacks Based on DL in Communication Networks. 2020 7th International Conference on Dependable Systems and Their Applications (DSA). :251–252.
Deep Neural Networks (DNN) have strong capabilities of memories, feature identifications and automatic analyses, solving various complex problems. However, DNN classifiers have obvious fragility that adding several unnoticeable perturbations to the original examples will lead to the errors in the classifier identification. In the field of communications, the adversarial examples will greatly reduce the accuracy of the signal identification, causing great information security risks. Considering the adversarial examples pose a serious threat to the security of the DNN models, studying their generation mechanisms and testing their attack effects are critical to ensuring the information security of the communication networks. This paper will study the generation of the adversarial examples and the influences of the adversarial examples on the accuracy of the DNN-based communication signal identification. Meanwhile, this paper will study the influences of the adversarial examples under the white-box models and black-box models, and explore the adversarial attack influences of the factors such as perturbation levels and iterative steps. The insights of this study would be helpful for ensuring the security of information networks and designing robust DNN communication networks.
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.
Kim, Hyeji, Jiang, Yihan, Kannan, Sreeram, Oh, Sewoong, Viswanath, Pramod.  2020.  Deepcode: Feedback Codes via Deep Learning. IEEE Journal on Selected Areas in Information Theory. 1:194—206.
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly outperforms state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability and achieve a 3dB gain in terms of SNR. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, and (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research.
Wang, Chenguang, Pan, Kaikai, Tindemans, Simon, Palensky, Peter.  2020.  Training Strategies for Autoencoder-based Detection of False Data Injection Attacks. 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :1—5.
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.
Zhao, Yi, Jia, Xian, An, Dou, Yang, Qingyu.  2020.  LSTM-Based False Data Injection Attack Detection in Smart Grids. 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC). :638—644.
As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach.
Li, Meng, Zhong, Qi, Zhang, Leo Yu, Du, Yajuan, Zhang, Jun, Xiang, Yong.  2020.  Protecting the Intellectual Property of Deep Neural Networks with Watermarking: The Frequency Domain Approach. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :402–409.
Similar to other digital assets, deep neural network (DNN) models could suffer from piracy threat initiated by insider and/or outsider adversaries due to their inherent commercial value. DNN watermarking is a promising technique to mitigate this threat to intellectual property. This work focuses on black-box DNN watermarking, with which an owner can only verify his ownership by issuing special trigger queries to a remote suspicious model. However, informed attackers, who are aware of the watermark and somehow obtain the triggers, could forge fake triggers to claim their ownerships since the poor robustness of triggers and the lack of correlation between the model and the owner identity. This consideration calls for new watermarking methods that can achieve better trade-off for addressing the discrepancy. In this paper, we exploit frequency domain image watermarking to generate triggers and build our DNN watermarking algorithm accordingly. Since watermarking in the frequency domain is high concealment and robust to signal processing operation, the proposed algorithm is superior to existing schemes in resisting fraudulent claim attack. Besides, extensive experimental results on 3 datasets and 8 neural networks demonstrate that the proposed DNN watermarking algorithm achieves similar performance on functionality metrics and better performance on security metrics when compared with existing algorithms.
Habib ur Rehman, Muhammad, Mukhtar Dirir, Ahmed, Salah, Khaled, Svetinovic, Davor.  2020.  FairFed: Cross-Device Fair Federated Learning. 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). :1–7.
Federated learning (FL) is the rapidly developing machine learning technique that is used to perform collaborative model training over decentralized datasets. FL enables privacy-preserving model development whereby the datasets are scattered over a large set of data producers (i.e., devices and/or systems). These data producers train the learning models, encapsulate the model updates with differential privacy techniques, and share them to centralized systems for global aggregation. However, these centralized models are always prone to adversarial attacks (such as data-poisoning and model poisoning attacks) due to a large number of data producers. Hence, FL methods need to ensure fairness and high-quality model availability across all the participants in the underlying AI systems. In this paper, we propose a novel FL framework, called FairFed, to meet fairness and high-quality data requirements. The FairFed provides a fairness mechanism to detect adversaries across the devices and datasets in the FL network and reject their model updates. We use a Python-simulated FL framework to enable large-scale training over MNIST dataset. We simulate a cross-device model training settings to detect adversaries in the training network. We used TensorFlow Federated and Python to implement the fairness protocol, the deep neural network, and the outlier detection algorithm. We thoroughly test the proposed FairFed framework with random and uniform data distributions across the training network and compare our initial results with the baseline fairness scheme. Our proposed work shows promising results in terms of model accuracy and loss.
Połap, Dawid, Srivastava, Gautam, Jolfaei, Alireza, Parizi, Reza M..  2020.  Blockchain Technology and Neural Networks for the Internet of Medical Things. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :508–513.
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
Dang, Tran Khanh, Truong, Phat T. Tran, Tran, Pi To.  2020.  Data Poisoning Attack on Deep Neural Network and Some Defense Methods. 2020 International Conference on Advanced Computing and Applications (ACOMP). :15–22.
In recent years, Artificial Intelligence has disruptively changed information technology and software engineering with a proliferation of technologies and applications based-on it. However, recent researches show that AI models in general and the most greatest invention since sliced bread - Deep Learning models in particular, are vulnerable to being hacked and can be misused for bad purposes. In this paper, we carry out a brief review of data poisoning attack - one of the two recently dangerous emerging attacks - and the state-of-the-art defense methods for this problem. Finally, we discuss current challenges and future developments.
Ali, Muhammad, Hu, Yim-Fun, Luong, Doanh Kim, Oguntala, George, Li, Jian-Ping, Abdo, Kanaan.  2020.  Adversarial Attacks on AI based Intrusion Detection System for Heterogeneous Wireless Communications Networks. 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). :1–6.
It has been recognized that artificial intelligence (AI) will play an important role in future societies. AI has already been incorporated in many industries to improve business processes and automation. Although the aviation industry has successfully implemented flight management systems or autopilot to automate flight operations, it is expected that full embracement of AI remains a challenge. Given the rigorous validation process and the requirements for the highest level of safety standards and risk management, AI needs to prove itself being safe to operate. This paper addresses the safety issues of AI deployment in an aviation network compatible with the Future Communication Infrastructure that utilizes heterogeneous wireless access technologies for communications between the aircraft and the ground networks. It further considers the exploitation of software defined networking (SDN) technologies in the ground network while the adoption of SDN in the airborne network can be optional. Due to the nature of centralized management in SDN-based network, the SDN controller can become a single point of failure or a target for cyber attacks. To countermeasure such attacks, an intrusion detection system utilises AI techniques, more specifically deep neural network (DNN), is considered. However, an adversary can target the AI-based intrusion detection system. This paper examines the impact of AI security attacks on the performance of the DNN algorithm. Poisoning attacks targeting the DSL-KDD datasets which were used to train the DNN algorithm were launched at the intrusion detection system. Results showed that the performance of the DNN algorithm has been significantly degraded in terms of the mean square error, accuracy rate, precision rate and the recall rate.
Lee, Dongseop, Kim, Hyunjin, Ryou, Jaecheol.  2020.  Poisoning Attack on Show and Tell Model and Defense Using Autoencoder in Electric Factory. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :538–541.
Recently, deep neural network technology has been developed and used in various fields. The image recognition model can be used for automatic safety checks at the electric factory. However, as the deep neural network develops, the importance of security increases. A poisoning attack is one of security problems. It is an attack that breaks down by entering malicious data into the training data set of the model. This paper generates adversarial data that modulates feature values to different targets by manipulating less RGB values. Then, poisoning attacks in one of the image recognition models, the show and tell model. Then use autoencoder to defend adversarial data.
Boursinos, Dimitrios, Koutsoukos, Xenofon.  2020.  Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems. 2020 IEEE Security and Privacy Workshops (SPW). :228—233.

Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks. However, DNNs are typically non-transparent making reasoning about their predictions very difficult, and hence their application to safety-critical systems is very challenging. LECs could be integrated easier into CPS if their predictions could be complemented with a confidence measure that quantifies how much we trust their output. The paper presents an approach for computing confidence bounds based on Inductive Conformal Prediction (ICP). We train a Triplet Network architecture to learn representations of the input data that can be used to estimate the similarity between test examples and examples in the training data set. Then, these representations are used to estimate the confidence of set predictions from a classifier that is based on the neural network architecture used in the triplet. The approach is evaluated using a robotic navigation benchmark and the results show that we can computed trusted confidence bounds efficiently in real-time.

Cai, Feiyang, Li, Jiani, Koutsoukos, Xenofon.  2020.  Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression. 2020 IEEE Security and Privacy Workshops (SPW). :208–214.

Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. However, it has been shown that LECs such as deep neural networks (DNN) are not robust and adversarial examples can cause the model to make a false prediction. The paper considers the problem of efficiently detecting adversarial examples in LECs used for regression in CPS. The proposed approach is based on inductive conformal prediction and uses a regression model based on variational autoencoder. The architecture allows to take into consideration both the input and the neural network prediction for detecting adversarial, and more generally, out-of-distribution examples. We demonstrate the method using an advanced emergency braking system implemented in an open source simulator for self-driving cars where a DNN is used to estimate the distance to an obstacle. The simulation results show that the method can effectively detect adversarial examples with a short detection delay.

Mheisn, Alaa, Shurman, Mohammad, Al-Ma’aytah, Abdallah.  2020.  WSNB: Wearable Sensors with Neural Networks Located in a Base Station for IoT Environment. 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :1—4.
The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.