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Titouna, Chafiq, Na\"ıt-Abdesselam, Farid, Moungla, Hassine.  2020.  An Online Anomaly Detection Approach For Unmanned Aerial Vehicles. 2020 International Wireless Communications and Mobile Computing (IWCMC). :469–474.
A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.
Kelly, Martin S., Mayes, Keith.  2020.  High Precision Laser Fault Injection Using Low-Cost Components.. 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :219–228.
This paper demonstrates that it is possible to execute sophisticated and powerful fault injection attacks on microcontrollers using low-cost equipment and readily available components. Earlier work had implied that powerful lasers and high grade optics frequently used to execute such attacks were being underutilized and that attacks were equally effective when using low-power settings and imprecise focus. This work has exploited these earlier findings to develop a low-cost laser workstation capable of generating multiple discrete faults with timing accuracy capable of targeting consecutive instruction cycles. We have shown that the capabilities of this new device exceed those of the expensive laboratory equipment typically used in related work. We describe a simplified fault model to categorize the effects of induced errors on running code and use it, along with the new device, to reevaluate the efficacy of different defensive coding techniques. This has enabled us to demonstrate an efficient hybrid defense that outperforms the individual defenses on our chosen target. This approach enables device programmers to select an appropriate compromise between the extremes of undefended code and unusable overdefended code, to do so specifically for their chosen device and without the need for prohibitively expensive equipment. This work has particular relevance in the burgeoning IoT world where many small companies with limited budgets are deploying low-cost microprocessors in ever more security sensitive roles.
Wang, Guoqing, Zhuang, Lei, Liu, Taotao, Li, Shuxia, Yang, Sijin, Lan, Julong.  2020.  Formal Analysis and Verification of Industrial Control System Security via Timed Automata. 2020 International Conference on Internet of Things and Intelligent Applications (ITIA). :1–5.
The industrial Internet of Things (IIoT) can facilitate industrial upgrading, intelligent manufacturing, and lean production. Industrial control system (ICS) is a vital support mechanism for many key infrastructures in the IIoT. However, natural defects in the ICS network security mechanism and the susceptibility of the programmable logic controller (PLC) program to malicious attack pose a threat to the safety of national infrastructure equipment. To improve the security of the underlying equipment in ICS, a model checking method based on timed automata is proposed in this work, which can effectively model the control process and accurately simulate the system state when incorporating time factors. Formal analysis of the ICS and PLC is then conducted to formulate malware detection rules which can constrain the normal behavior of the system. The model checking tool UPPAAL is then used to verify the properties by detecting whether there is an exception in the system and determine the behavior of malware through counter-examples. The chemical reaction control system in Tennessee-Eastman process is taken as an example to carry out modeling, characterization, and verification, and can effectively detect multiple patterns of malware and propose relevant security policy recommendations.
Pan, Zhicheng, Deng, Jun, Chu, Jinwei, Zhang, Zhanlong, Dong, Zijian.  2020.  Research on Correlation Analysis of Vibration Signals at Multiple Measuring Points and Black Box Model of Flexible-DC Transformer. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). :3238–3242.
The internal structure of the flexible-DC transformer is complicated and the lack of a reliable vibration calculation model limits the application of the vibration analysis method in the fault diagnosis of the flexible-DC transformer. In response to this problem, this paper analyzes the correlation between the vibration signals of multiple measuring points and establishes a ``black box'' model of transformer vibration detection. Using the correlation analysis of multiple measuring points and BP neural network, a ``black box'' model that simulates the internal vibration transmission relationship of the transformer is established. The vibration signal of the multiple measuring points can be used to calculate the vibration signal of the target measuring point under specific working conditions. This can provide effective information for fault diagnosis and judgment of the running status of the flexible-DC transformer.
Mezzah, Ibrahim, Kermia, Omar, Chemali, Hamimi.  2020.  Extensive Fault Emulation on RFID Tags. 2020 15th Design Technology of Integrated Systems in Nanoscale Era (DTIS). :1–2.
Radio frequency identification (RFID) is widespread and still necessary in many important applications. However, and in various significant cases, the use of this technology faces multiple security issues that must be addressed. This is mainly related to the use of RFID tags (transponders) which are electronic components communicating wirelessly, and hence they are vulnerable to multiple attacks through several means. In this work, an extensive fault analysis is performed on a tag architecture in order to evaluate its hardness. Tens of millions of single-bit upset (SBU) and multiple-bit upset (MBU) faults are emulated randomly on this tag architecture using an FPGA-based emulation platform. The emulated faults are classified under five groups according to faults effect on the tag behaviour. The obtained results show the faults effect variation in function of the number of MBU affected bits. The interpretation of this variation allows evaluating the tag robustness. The proposed approach represents an efficient mean that permits to study tag architectures at the design level and evaluating their robustness and vulnerability to fault attacks.
Boespflug, Etienne, Ene, Cristian, Mounier, Laurent, Potet, Marie-Laure.  2020.  Countermeasures Optimization in Multiple Fault-Injection Context. 2020 Workshop on Fault Detection and Tolerance in Cryptography (FDTC). :26–34.
Fault attacks consist in changing the program behavior by injecting faults at run-time, either at hardware or at software level. Their goal is to change the correct progress of the algorithm and hence, either to allow gaining some privilege access or to allow retrieving some secret information based on an analysis of the deviation of the corrupted behavior with respect to the original one. Countermeasures have been proposed to protect embedded systems by adding spatial, temporal or information redundancy at hardware or software level. First we define Countermeasures Check Point (CCP) and CCPs-based countermeasures as an important subclass of countermeasures. Then we propose a methodology to generate an optimal protection scheme for CCPs-based countermeasure. Finally we evaluate our work on a benchmark of code examples with respect to several Control Flow Integrity (CFI) oriented existing protection schemes.
Engels, Susanne, Schellenberg, Falk, Paar, Christof.  2020.  SPFA: SFA on Multiple Persistent Faults. 2020 Workshop on Fault Detection and Tolerance in Cryptography (FDTC). :49–56.
For classical fault analysis, a transient fault is required to be injected during runtime, e.g., only at a specific round. Instead, Persistent Fault Analysis (PFA) introduces a powerful class of fault attacks that allows for a fault to be present throughout the whole execution. One limitation of original PFA as introduced by Zhang et al. at CHES'18 is that the adversary needs know (or brute-force) the faulty values prior to the analysis. While this was addressed at a follow-up work at CHES'20, the solution is only applicable to a single faulty value. Instead, we use the potency of Statistical Fault Analysis (SFA) in the persistent fault setting, presenting Statistical Persistent Fault Analysis (SPFA) as a more general approach of PFA. As a result, any or even a multitude of unknown faults that cause an exploitable bias in the targeted round can be used to recover the cipher's secret key. Indeed, the undesired faults in the other rounds that occur due the persistent nature of the attack converge to a uniform distribution as required by SFA. We verify the effectiveness of our attack against LED and AES.
Bagbaba, Ahmet Cagri, Jenihhin, Maksim, Ubar, Raimund, Sauer, Christian.  2020.  Representing Gate-Level SET Faults by Multiple SEU Faults at RTL. 2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS). :1–6.
The advanced complex electronic systems increasingly demand safer and more secure hardware parts. Correspondingly, fault injection became a major verification milestone for both safety- and security-critical applications. However, fault injection campaigns for gate-level designs suffer from huge execution times. Therefore, designers need to apply early design evaluation techniques to reduce the execution time of fault injection campaigns. In this work, we propose a method to represent gate-level Single-Event Transient (SET) faults by multiple Single-Event Upset (SEU) faults at the Register-Transfer Level. Introduced approach is to identify true and false logic paths for each SET in the flip-flops' fan-in logic cones to obtain more accurate sets of flip-flops for multiple SEUs injections at RTL. Experimental results demonstrate the feasibility of the proposed method to successfully reduce the fault space and also its advantage with respect to state of the art. It was shown that the approach is able to reduce the fault space, and therefore the fault-injection effort, by up to tens to hundreds of times.
Hou, Qilin, Wang, Jinglin, Shen, Yong.  2020.  Multiple Sensors Fault Diagnosis for Rolling Bearing Based on Variational Mode Decomposition and Convolutional Neural Networks. 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan). :450–455.
The reliability of mechanical equipment is very important for the security operation of large-scale equipment. This paper presents a rolling bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). This proposed method includes using VMD and CNN to extend multi-sensor data, extracting detailed features and achieve more robust sensor fusion. Representative features can be extracted automatically from the raw signals. The proposed method can extract features directly from data without prior knowledge. The effectiveness of this method is verified on Case Western Reserve University (CWRU) dataset. Compared with one sensor and traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. Because of the end-to-end feature learning ability, this method can be extended to other kinds of sensor mechanical fault diagnosis.
Lim, Wei Yang Bryan, Xiong, Zehui, Niyato, Dusit, Huang, Jianqiang, Hua, Xian-Sheng, Miao, Chunyan.  2020.  Incentive Mechanism Design for Federated Learning in the Internet of Vehicles. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). :1—5.
In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.
Lahiri, Pralay Kumar, Das, Debashis, Mansoor, Wathiq, Banerjee, Sourav, Chatterjee, Pushpita.  2020.  A Trustworthy Blockchain based framework for Impregnable IoV in Edge Computing. 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). :26—31.
The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.
Mershad, Khaleel, Said, Bilal.  2020.  A Blockchain Model for Secure Communications in Internet of Vehicles. 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). :1—6.
The wide expansion of the Internet of Things is pushing the growth of vehicular ad-hoc networks (VANETs) into the Internet of Vehicles (IoV). Secure data communication is vital to the success and stability of the IoV and should be integrated into its various operations and aspects. In this paper, we present a framework for secure IoV communications by utilizing the High Performance Blockchain Consensus (HPBC) algorithm. Based on a previously published communication model for VANETs that uses an efficient routing protocol for transmitting packets between vehicles, we describe in this paper how to integrate a blockchain model on top of the IoV communications system. We illustrate the method that we used to implement HPBC within the IoV nodes. In order to prove the efficiency of the proposed model, we carry out extensive simulations that test the proposed model and study its overhead on the IoV network. The simulation results demonstrated the good performance of the HPBC algorithm when implemented within the IoV environment.
Yan, Chenyang, Zhang, Yulei, Wang, Hongshuo, Yu, Shaoyang.  2020.  A Safe and Efficient Message Authentication Scheme In The Internet Of Vehicles. 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS). :10—13.
In order to realize the security authentication of information transmission between vehicle nodes in the vehicular ad hoc network, based on the certificateless public key cryptosystem and aggregate signature, a privacy-protected certificateless aggregate signature scheme is proposed, which eliminates the complicated certificate maintenance cost. This solution also solves the key escrow problem. By Communicating with surrounding nodes through the pseudonym of the vehicle, the privacy protection of vehicle users is realized. The signature scheme satisfies the unforgeability of an adaptive selective message attack under a random prophetic machine. The scheme meets message authentication, identity privacy protection, resistance to reply attacks.
Aswal, Kiran, Dobhal, Dinesh C., Pathak, Heman.  2020.  Comparative analysis of machine learning algorithms for identification of BOT attack on the Internet of Vehicles (IoV). 2020 International Conference on Inventive Computation Technologies (ICICT). :312—317.
In this digital era, technology is upgrading day by day and becoming more agile and intelligent. Smart devices and gadgets are now being used to find solutions to complex problems in various domains such as health care, industries, entertainment, education, etc. The Transport system, which is the biggest challenge for any governing authority of a state, is also not untouched with this development. There are numerous challenges and issues with the existing transport system, which can be addressed by developing intelligent and autonomous vehicles. The existing vehicles can be upgraded to use sensors and the latest communication techniques. The advancements in the Internet of Things (IoT) have the potential to completely transform the existing transport system to a more advanced and intelligent transport system that is the Internet of Vehicles (IoV). Due to the connectivity with the Internet, the Internet of Vehicles (IoV) is exposed to various security threats. Security is the primary issue, which requires to be addressed for success and adoption of the IoV. In this paper, the applicability of machine learning based solutions to address the security issue of IoV is analyzed. The performance of six machine-learning algorithms to detect Bot threats is validated by the k-fold cross-validation method in python.
Xu, Yue, Ni, Ming, Ying, Fei, Zhang, Jingwen.  2020.  Security Optimization Based on Mimic Common Operating Environment for the Internet of Vehicles. 2020 2nd International Conference on Computer Communication and the Internet (ICCCI). :18—23.
The increasing vehicles have brought convenience to people as well as many traffic problems. The Internet of Vehicles (IoV) is an extension of the intelligent transportation system based on the Internet of Things (IoT), which is the omnibearing network connection among “Vehicles, Loads, Clouds”. However, IoV also faces threats from various known and unknown security vulnerabilities. Traditional security defense methods can only deal with known attacks, while there is no effective way to deal with unknown attacks. In this paper, we show an IoV system deployed on a Mimic Common Operating Environment (MCOE). At the sensing layer, we introduce a lightweight cryptographic algorithm, LBlock, to encrypt the data collected by the hardware. Thus, we can prevent malicious tampering of information such as vehicle conditions. At the application layer, we firstly put the IoV system platform into MCOE to make it dynamic, heterogeneous and redundant. Extensive experiments prove that the sensing layer can encrypt data reliably and energy-efficiently. And we prove the feasibility and security of the Internet of Vehicles system platform on MCOE.
Ma, Ruhui, Cao, Jin, Feng, Dengguo, Li, Hui, Niu, Ben, Li, Fenghua, Yin, Lihua.  2020.  A Secure Authentication Scheme for Remote Diagnosis and Maintenance in Internet of Vehicles. 2020 IEEE Wireless Communications and Networking Conference (WCNC). :1—7.
Due to the low latency and high speed of 5G networks, the Internet of Vehicles (IoV) under the 5G network has been rapidly developed and has broad application prospects. The Third Generation Partnership Project (3GPP) committee has taken remote diagnosis as one of the development cores of IoV. However, how to ensure the security of remote diagnosis and maintenance services is also a key point to ensure vehicle safety, which is directly related to the safety of vehicle passengers. In this paper, we propose a secure and efficient authentication scheme based on extended chebyshev chaotic maps for remote diagnosis and maintenance in IoVs. In the proposed scheme, to provide strong security, anyone, such as the vehicle owner or the employee of the Vehicle Service Centre (VSC), must enter the valid biometrics and password in order to enjoy or provide remote diagnosis and maintenance services, and the vehicle and the VSC should authenticate each other to ensure that they are legitimate. The security analysis and performance evaluation results show that the proposed scheme can provide robust security with ideal efficiency.
Sikarwar, Himani, Nahar, Ankur, Das, Debasis.  2020.  LABVS: Lightweight Authentication and Batch Verification Scheme for Universal Internet of Vehicles (UIoV). 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). :1—6.
With the rapid technological advancement of the universal internet of vehicles (UIoV), it becomes crucial to ensure safe and secure communication over the network, in an effort to achieve the implementation objective of UIoV effectively. A UIoV is characterized by highly dynamic topology, scalability, and thus vulnerable to various types of security and privacy attacks (i.e., replay attack, impersonation attack, man-in-middle attack, non-repudiation, and modification). Since the components of UIoV are constrained by numerous factors (e.g., low memory devices, low power), which makes UIoV highly susceptible. Therefore, existing schemes to address the privacy and security facets of UIoV exhibit an enormous scope of improvement in terms of time complexity and efficiency. This paper presents a lightweight authentication and batch verification scheme (LABVS) for UIoV using a bilinear map and cryptographic operations (i.e., one-way hash function, concatenation, XOR) to minimize the rate of message loss occurred due to delay in response time as in single message verification scheme. Subsequently, the scheme results in a high level of security and privacy. Moreover, the performance analysis substantiates that LABVS minimizes the computational delay and has better performance in the delay-sensitive network in terms of security and privacy as compared to the existing schemes.
Sikarwar, Himani, Das, Debasis.  2020.  An Efficient Lightweight Authentication and Batch Verification Scheme for Universal Internet of Vehicles (UIoV). 2020 International Wireless Communications and Mobile Computing (IWCMC). :1266—1271.
Ensuring secure transmission over the communication channel is a fundamental responsibility to achieve the implementation objective of universal internet of vehicles (UIoV) efficiently. Characteristics like highly dynamic topology and scalability of UIoV makes it more vulnerable to different types of privacy and security attacks. Considerable scope of improvement in terms of time complexity and performance can be observed within the existing schemes that address the privacy and security aspects of UIoV. In this paper, we present an improvised authentication and lightweight batch verification method for security and privacy in UIoV. The suggested method reduces the message loss rate, which occurred due to the response time delay by implementing some low-cost cryptographic operations like one-way hash function, concatenation, XOR, and bilinear map. Furthermore, the performance analysis proves that the proposed method is more reliable that reduces the computational delay and has a better performance in the delay-sensitive network as compared to the existing schemes. The experimental results are obtained by implementing the proposed scheme on a desktop-based configuration as well as Raspberry Pi 4.
Chen, Jichang, Lu, Zhixiang, Zhu, Xueping.  2020.  A Lightweight Dual Authentication Protocol for the Internet of Vehicles. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). :17—22.
With the development of 5G communication technology, the status of the Internet of Vehicles in people's lives is greatly improved in the general trend of intelligent transportation. The combination of vehicles and Radio Frequency Identification (RFID) makes the application prospects of vehicle networking gradually expand. However, the wireless network of the Internet of Vehicles is open and mobile, so it can be easily stolen or tampered with by attackers. Moreover, it will cause serious traffic security problems and even threat people's lives. In this paper, we propose a lightweight authentication protocol for the Internet of Vehicles based on a mobile RFID system and give corresponding security requirements for modeling potential attacks. The protocol is based on the three-party mutual authentication, and uses bit-operated left-cycle shift operations and hetero-oriented operations to generate encrypted data. The simultaneous inclusion of triparty shared key information and random numbers makes the protocol resistant to counterfeit attacks, violent attacks, replay attacks and desynchronization attacks. Finally, a simulation analysis of the security protocol using the ProVerif tool shows that the protocol secures is not accessible to attackers during the data transfer, and achieve the three-party authentication between sensor nodes (SN), vehicle nodes (Veh) and backend servers.
Biroon, Roghieh A., Pisu, Pierluigi, Abdollahi, Zoleikha.  2020.  Real-time False Data Injection Attack Detection in Connected Vehicle Systems with PDE modeling. 2020 American Control Conference (ACC). :3267—3272.
Connected vehicles as a promising concept of Intelligent Transportation System (ITS), are a potential solution to address some of the existing challenges of emission, traffic congestion as well as fuel consumption. To achieve these goals, connectivity among vehicles through the wireless communication network is essential. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with purposes of disrupting the performance of the connected vehicles, lead to catastrophic collision and traffic congestion. In this study, we consider a platoon of connected vehicles equipped with Cooperative Adaptive Cruise Control (CACC) which are subjected to a specific type of cyber-attack namely "False Data Injection" attack. We developed a novel method to model the attack with ghost vehicles injected into the connected vehicles network to disrupt the performance of the whole system. To aid the analysis, we use a Partial Differential Equation (PDE) model. Furthermore, we present a PDE model-based diagnostics scheme capable of detecting the false data injection attack and isolating the injection point of the attack in the platoon system. The proposed scheme is designed based on a PDE observer with measured velocity and acceleration feedback. Lyapunov stability theory has been utilized to verify the analytically convergence of the observer under no attack scenario. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation study.
Liu, Ming, Chen, Shichao, Lu, Fugang, Xing, Mengdao, Wei, Jingbiao.  2020.  A Target Detection Method in SAR Images Based on Superpixel Segmentation. 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT). :528—530.
A synthetic aperture radar (SAR) target detection method based on the fusion of multiscale superpixel segmentations is proposed in this paper. SAR images are segmented between land and sea firstly by using superpixel technology in different scales. Secondly, image segmentation results together with the constant false alarm rate (CFAR) detection result are coalesced. Finally, target detection is realized by fusing different scale results. The effectiveness of the proposed algorithm is tested on Sentinel-1A data.
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.
Gonçalves, Charles F., Menasche, Daniel S., Avritzer, Alberto, Antunes, Nuno, Vieira, Marco.  2020.  A Model-Based Approach to Anomaly Detection Trading Detection Time and False Alarm Rate. 2020 Mediterranean Communication and Computer Networking Conference (MedComNet). :1—8.
The complexity and ubiquity of modern computing systems is a fertile ground for anomalies, including security and privacy breaches. In this paper, we propose a new methodology that addresses the practical challenges to implement anomaly detection approaches. Specifically, it is challenging to define normal behavior comprehensively and to acquire data on anomalies in diverse cloud environments. To tackle those challenges, we focus on anomaly detection approaches based on system performance signatures. In particular, performance signatures have the potential of detecting zero-day attacks, as those approaches are based on detecting performance deviations and do not require detailed knowledge of attack history. The proposed methodology leverages an analytical performance model and experimentation, and allows to control the rate of false positives in a principled manner. The methodology is evaluated using the TPCx-V workload, which was profiled during a set of executions using resource exhaustion anomalies that emulate the effects of anomalies affecting system performance. The proposed approach was able to successfully detect the anomalies, with a low number of false positives (precision 90%-98%).
Wang, Chenguang, Tindemans, Simon, Pan, Kaikai, Palensky, Peter.  2020.  Detection of False Data Injection Attacks Using the Autoencoder Approach. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). :1—6.
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
ur Rahman, Hafiz, Duan, Guihua, Wang, Guojun, Bhuiyan, Md Zakirul Alam, Chen, Jianer.  2020.  Trustworthy Data Acquisition and Faulty Sensor Detection using Gray Code in Cyber-Physical System. 2020 IEEE 23rd International Conference on Computational Science and Engineering (CSE). :58—65.
Due to environmental influence and technology limitation, a wireless sensor/sensors module can neither store or process all raw data locally nor reliably forward it to a destination in heterogeneous IoT environment. As a result, the data collected by the IoT's sensors are inherently noisy, unreliable, and may trigger many false alarms. These false or misleading data can lead to wrong decisions once the data reaches end entities. Therefore, it is highly recommended and desirable to acquire trustworthy data before data transmission, aggregation, and data storing at the end entities/cloud. In this paper, we propose an In-network Generalized Trustworthy Data Collection (IGTDC) framework for trustworthy data acquisition and faulty sensor detection in the IoT environment. The key idea of IGTDC is to allow a sensor's module to examine locally whether the raw data is trustworthy before transmitting towards upstream nodes. It further distinguishes whether the acquired data can be trusted or not before data aggregation at the sink/edge node. Besides, IGTDC helps to recognize a faulty or compromised sensor. For a reliable data collection, we use collaborative IoT technique, gate-level modeling, and programmable logic device (PLD) to ensure that the acquired data is reliable before transmitting towards upstream nodes/cloud. We use a hardware-based technique called “Gray Code” to detect a faulty sensor. Through simulations we reveal that the acquired data in IGTDC framework is reliable that can make a trustworthy data collection for event detection, and assist to distinguish a faulty sensor.