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2022-03-15
Cristescu, Mihai-Corneliu, Bob, Cristian.  2021.  Flexible Framework for Stimuli Redundancy Reduction in Functional Verification Using Artificial Neural Networks. 2021 International Symposium on Signals, Circuits and Systems (ISSCS). :1—4.
Within the ASIC development process, the phase of functional verification is a major bottleneck that affects the product time to market. A technique that decreases the time cost for reaching functional coverage closure is reducing the stimuli redundancy during the test regressions. This paper addresses such a solution and presents a novel, efficient, and scalable implementation that harnesses the power of artificial neural networks. This article outlines the concept strategy, highlights the framework structure, lists the experimental results, and underlines future research directions.
2022-03-14
Li, Xiang, Liu, Baojun, Zheng, Xiaofeng, Duan, Haixin, Li, Qi, Huang, Youjun.  2021.  Fast IPv6 Network Periphery Discovery and Security Implications. 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :88–100.
Numerous measurement researches have been performed to discover the IPv4 network security issues by leveraging the fast Internet-wide scanning techniques. However, IPv6 brings the 128-bit address space and renders brute-force network scanning impractical. Although significant efforts have been dedicated to enumerating active IPv6 hosts, limited by technique efficiency and probing accuracy, large-scale empirical measurement studies under the increasing IPv6 networks are infeasible now. To fill this research gap, by leveraging the extensively adopted IPv6 address allocation strategy, we propose a novel IPv6 network periphery discovery approach. Specifically, XMap, a fast network scanner, is developed to find the periphery, such as a home router. We evaluate it on twelve prominent Internet service providers and harvest 52M active peripheries. Grounded on these found devices, we explore IPv6 network risks of the unintended exposed security services and the flawed traffic routing strategies. First, we demonstrate the unintended exposed security services in IPv6 networks, such as DNS, and HTTP, have become emerging security risks by analyzing 4.7M peripheries. Second, by inspecting the periphery's packet routing strategies, we present the flawed implementations of IPv6 routing protocol affecting 5.8M router devices. Attackers can exploit this common vulnerability to conduct effective routing loop attacks, inducing DoS to the ISP's and home routers with an amplification factor of \textbackslashtextbackslashgt 200. We responsibly disclose those issues to all involved vendors and ASes and discuss mitigation solutions. Our research results indicate that the security community should revisit IPv6 network strategies immediately.
Ouyang, Yuankai, Li, Beibei, Kong, Qinglei, Song, Han, Li, Tao.  2021.  FS-IDS: A Novel Few-Shot Learning Based Intrusion Detection System for SCADA Networks. ICC 2021 - IEEE International Conference on Communications. :1—6.

Supervisory control and data acquisition (SCADA) networks provide high situational awareness and automation control for industrial control systems, whilst introducing a wide range of access points for cyber attackers. To address these issues, a line of machine learning or deep learning based intrusion detection systems (IDSs) have been presented in the literature, where a large number of attack examples are usually demanded. However, in real-world SCADA networks, attack examples are not always sufficient, having only a few shots in many cases. In this paper, we propose a novel few-shot learning based IDS, named FS-IDS, to detect cyber attacks against SCADA networks, especially when having only a few attack examples in the defenders’ hands. Specifically, a new method by orchestrating one-hot encoding and principal component analysis is developed, to preprocess SCADA datasets containing sufficient examples for frequent cyber attacks. Then, a few-shot learning based preliminary IDS model is designed and trained using the preprocessed data. Last, a complete FS-IDS model for SCADA networks is established by further training the preliminary IDS model with a few examples for cyber attacks of interest. The high effectiveness of the proposed FS-IDS, in detecting cyber attacks against SCADA networks with only a few examples, is demonstrated by extensive experiments on a real SCADA dataset.

Hahanov, V.I., Saprykin, A.S..  2021.  Federated Machine Learning Architecture for Searching Malware. 2021 IEEE East-West Design Test Symposium (EWDTS). :1—4.
Modern technologies for searching viruses, cloud-edge computing, and also federated algorithms and machine learning architectures are shown. The architectures for searching malware based on the xor metric applied in the design and test of computing systems are proposed. A Federated ML method is proposed for searching for malware, which significantly speeds up learning without the private big data of users. A federated infrastructure of cloud-edge computing is described. The use of signature analysis and the assertion engine for searching malware is shown. The paradigm of LTF-computing for searching destructive components in software applications is proposed.
2022-03-01
Zhang, Zilin, Li, Yan, Gao, Meiguo.  2021.  Few-Shot Learning of Signal Modulation Recognition Based on Attention Relation Network. 2020 28th European Signal Processing Conference (EUSIPCO). :1372–1376.
Most of existing signal modulation recognition methods attempt to establish a machine learning mechanism by training with a large number of annotated samples, which is hardly applied to the real-world electronic reconnaissance scenario where only a few samples can be intercepted in advance. Few-Shot Learning (FSL) aims to learn from training classes with a lot of samples and transform the knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a more effective feature representation of support samples. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust to low signal-to-noise-ratio conditions.
Hui, Wang, Dongming, Wang, Dejian, Li, Lin, Zeng, Zhe, Wang.  2021.  A Framework For Network Intrusion Detection Based on Unsupervised Learning. 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID). :188–193.
Anomaly detection is the primary method of detecting intrusion. Unsupervised models, such as auto-encoders network, auto-encoder, and GMM, are currently the most widely used anomaly detection techniques. In reality, the samples used to train the unsupervised model may not be pure enough and may include some abnormal samples. However, the classification effect is poor since these approaches do not completely understand the association between reconstruction errors, reconstruction characteristics, and irregular sample density distribution. This paper proposes a novel intrusion detection system architecture that includes data collection, processing, and feature extraction by integrating data reconstruction features, reconstruction errors, auto-encoder parameters, and GMM. Our system outperforms other unsupervised learning-based detection approaches in terms of accuracy, recall, F1-score, and other assessment metrics after training and testing on multiple intrusion detection data sets.
2022-02-24
Ajit, Megha, Sankaran, Sriram, Jain, Kurunandan.  2021.  Formal Verification of 5G EAP-AKA Protocol. 2021 31st International Telecommunication Networks and Applications Conference (ITNAC). :140–146.
The advent of 5G, one of the most recent and promising technologies currently under deployment, fulfills the emerging needs of mobile subscribers by introducing several new technological advancements. However, this may lead to numerous attacks in the emerging 5G networks. Thus, to guarantee the secure transmission of user data, 5G Authentication protocols such as Extensible Authentication Protocol - Authenticated Key Agreement Protocol (EAP-AKA) were developed. These protocols play an important role in ensuring security to the users as well as their data. However, there exists no guarantees about the security of the protocols. Thus formal verification is necessary to ensure that the authentication protocols are devoid of vulnerabilities or security loopholes. Towards this goal, we formally verify the security of the 5G EAP-AKA protocol using an automated verification tool called ProVerif. ProVerif identifies traces of attacks and checks for security loopholes that can be accessed by the attackers. In addition, we model the complete architecture of the 5G EAP-AKA protocol using the language called typed pi-calculus and analyze the protocol architecture through symbolic model checking. Our analysis shows that some cryptographic parameters in the architecture can be accessed by the attackers which cause the corresponding security properties to be violated.
Klenze, Tobias, Sprenger, Christoph, Basin, David.  2021.  Formal Verification of Secure Forwarding Protocols. 2021 IEEE 34th Computer Security Foundations Symposium (CSF). :1–16.
Today's Internet is built on decades-old networking protocols that lack scalability, reliability, and security. In response, the networking community has developed path-aware Internet architectures that solve these issues while simultaneously empowering end hosts. In these architectures, autonomous systems construct authenticated forwarding paths based on their routing policies. Each end host then selects one of these authorized paths and includes it in the packet header, thus allowing routers to efficiently determine how to forward the packet. A central security property of these architectures is path authorization, requiring that packets can only travel along authorized paths. This property protects the routing policies of autonomous systems from malicious senders.The fundamental role of packet forwarding in the Internet and the complexity of the authentication mechanisms employed call for a formal analysis. In this vein, we develop in Isabelle/HOL a parameterized verification framework for path-aware data plane protocols. We first formulate an abstract model without an attacker for which we prove path authorization. We then refine this model by introducing an attacker and by protecting authorized paths using (generic) cryptographic validation fields. This model is parameterized by the protocol's authentication mechanism and assumes five simple verification conditions that are sufficient to prove the refinement of the abstract model. We validate our framework by instantiating it with several concrete protocols from the literature and proving that they each satisfy the verification conditions and hence path authorization. No invariants must be proven for the instantiation. Our framework thus supports low-effort security proofs for data plane protocols. The results hold for arbitrary network topologies and sets of authorized paths, a guarantee that state-of-the-art automated security protocol verifiers cannot currently provide.
2022-02-10
LAPIQUE, Maxime, GAVAGSAZ-GHOACHANI, Roghayeh, MARTIN, Jean-Philippe, PIERFEDERICI, Serge, ZAIM, Sami.  2020.  Flatness-based control of a 3-phases PWM rectifier with LCL-filter amp; disturbance observer. IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. :4685–4690.
In more electrical aircraft, the embedded electrical network is handling more and more vital functions, being more and more strained as well. Attenuation of switching harmonics is a key step in the network reliability, thus filtering elements play a central role. To keep the weight of the embedded network reasonable, weakly damped high-order filters shall be preferred. Flatness-based control (FBC) can offer both high bandwidth regulation and large signal stability proof. This make FBC a good candidate to handle the inherent oscillating behavior of aforementioned filters. However, this control strategy can be tricky to implement, especially with high order systems. Moreover, FBC is more sensor demanding than classic PI-based control. This paper address these two drawbacks. First, a novel trajectory planning for high order systems is proposed. This method does not require multiple derivations. Then the input sensors are removed thanks to a parameters estimator. Feasibility and performances are verified with experimental results. Performances comparison with cascaded-loop topologies are given in final section to prove the relevance of the proposed control strategy.
ISSN: 2577-1647
2022-02-07
Todorov, Z., Efnusheva, D., Nikolic, T..  2021.  FPGA Implementation of Computer Network Security Protection with Machine Learning. 2021 IEEE 32nd International Conference on Microelectronics (MIEL). :263–266.
Network intrusion detection systems (NIDS) are widely used solutions targeting the security of any network device connected to the Internet and are taking the lead in the battle against intruders. This paper addresses the network security issues by implementing a hardware-based NIDS solution with a Naïve Bayes machine learning (ML) algorithm for classification using NSL Knowledge Discovery in Databases (KDD) dataset. The proposed FPGA implementation of the Naive Bayes classifier focuses on low latency and provides intrusion detection in just 240ns, with accuracy/precision of 70/97%, occupying 1 % of the Virtex7 VC709 FPGA chip area.
2022-02-04
Rabari, Jeet, Kumar, Arun Raj P..  2021.  FIFA: Fighting against Interest Flooding Attack in NDN-based VANET. 2021 International Wireless Communications and Mobile Computing (IWCMC). :1539–1544.
A vehicular Ad-hoc network (VANET) allows groups of autonomous or semi-autonomous vehicles to share information and content with each other and infrastructure. Named Data Networking (NDN) is recently proposed as one of the future internet architectures, which allows communication in network-based upon content name. It has originated from Information-centric networking (ICN). NDN-based VANET uses NDN as an underlying communication paradigm. NDN-based VANET suffers from several security attacks, one such attack is the Interest Flooding Attack (IFA) that targets the core forwarding mechanism of NDN-based VANET. This paper focuses on the detection and mitigation of IFA in NDN-based VANET. We proposed a method FIFA to detect and mitigate IFA. Our proposed method is capable of detecting normal IFA as well as a low-rate IFA. Along with that FIFA also ensures non-repudiation in networks. We have compared our proposed method with the existing mechanism to detect and mitigate IFA named IFAMS. Experiment results show that our method detects and mitigates normal IFA and low-rate IFA in the network.
Kuber, Sughosh, Sharma, Mohit, Gonzalez, Abel.  2021.  Factors influencing CT saturation and its implications on Distance Protection Scheme-Analysis and Testing. 2021 74th Conference for Protective Relay Engineers (CPRE). :1–11.
The behavior of the Current Transformer (CTs) is of utmost importance for protection engineers to ensure reliable operation of power system. CT magnetic saturation is a well-known phenomenon when analyzing its performance characteristics. Nevertheless, transient conditions in the system might be different every time. A good understanding of the magnetic saturation of different CT designs and the effect of saturation on the protection schemes is imperative for developing a robust and dependable protection system. In this paper, various factors that affect CT saturation like X/R ratio, large current magnitudes, DC offset, burden and magnetization remanence are discussed. Analysis of CT saturation based on changes to burden and remanence is performed. In addition to that, the effect of saturation due to these factors on distance protection are presented with test results and analysis. Saturation conditions are analyzed on mho distance elements during phase to ground and three phase faults. Finally, a practical approach to efficiently test the performance of protection schemes under CT saturation conditions is proposed using COMTRADE play back. COMTRADE play back files for various scenarios of CT saturation conditions are generated and used for testing the performance of the protection scheme.
2022-02-03
Souto, Alexandre, Prates, Pedro Alexandre, Lourenço, André, Al Maamari, Mazoon S., Marques, Francisco, Taranta, David, DoÓ, Luís, Mendonça, Ricardo, Barata, José.  2021.  Fleet Management System for Autonomous Mobile Robots in Secure Shop-floor Environments. 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). :1—6.
This paper presents a management system for a fleet of autonomous mobile robots performing logistics in security-heterogeneous factories. Loading and unloading goods and parts between workstations in these dynamic environments often demands from the mobile robots to share space and resources such as corridors, interlocked security doors and elevators among themselves. This model explores a dynamic task scheduling and assignment to the robots taking into account their location, tasks previously assigned and battery levels, all the while being aware of the physical constraints of the installation. The benefits of the proposed architecture were validated through a set of experiments in a mockup of INCM's shop-floor environment. During these tests 3 robots operated continuously for several hours, self-charging without any human intervention.
2022-01-25
Jha, Ashish, Novikova, Evgeniya S., Tokarev, Dmitry, Fedorchenko, Elena V..  2021.  Feature Selection for Attacker Attribution in Industrial Automation amp; Control Systems. 2021 IV International Conference on Control in Technical Systems (CTS). :220–223.
Modern Industrial Automation & Control Systems (IACS) are essential part of the critical infrastructures and services. They are used in health, power, water, and transportation systems, and the impact of cyberattacks on IACS could be severe, resulting, for example, in damage to the environment, public or employee safety or health. Thus, building IACS safe and secure against cyberattacks is extremely important. The attacker model is one of the key elements in risk assessment and other security related information system management tasks. The aim of the study is to specify the attacker's profile based on the analysis of network and system events. The paper presents an approach to the selection of attacker's profile attributes from raw network and system events of the Linux OS. To evaluate the approach the experiments were performed on data collected within the Global CPTC 2019 competition.
Jinhui, Yuan, Hongwei, Zhou, Laishun, Zhang.  2021.  F-SGX: Next Generation SGX for Trusted Computing. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). :673–677.
The existing methods of constructing a trusted computing environment do not fully meet the requirements. Intel SGX provides a new hardware foundation for the construction of trusted computing environment. However, existing SGX still faces problems such as side channel attacks. To overcome it, this paper present F-SGX which is the future SGX for trusting computing. In our opinion, F-SGX hold stronger isolation than current SGX, and reduce the dependence of enclave on host operating system. Furthermore, F-SGX hold a private key for the attestation. We believe that F-SGX can further provide better support for trusting computing environments while there is a good balance between isolation and dependencies.
Azevedo, João, Faria, Pedro, Romero, Luís.  2021.  Framework for Creating Outdoors Augmented and Virtual Reality. 2021 16th Iberian Conference on Information Systems and Technologies (CISTI). :1—6.
In this article we propose the architecture of a system in which its central objective is focused on creating a complete framework for creating outdoor environments of Augmented Reality (AR) and Virtual Reality (VR) allowing its users to digitize reality for hypermedia format. Subsequently, there will be an internal process with the objective of merging / grouping these 3D models, thus enabling clear and intuitive navigation within infinite virtual realities (based on the captured real world). In this way, the user is able to create points of interest within their parallel realities, being able to navigate and traverse their new worlds through these points.
2022-01-11
McCarthy, Andrew, Andriotis, Panagiotis, Ghadafi, Essam, Legg, Phil.  2021.  Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks. 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–8.
Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craft a data observation whereby the data features purposefully trigger the classifier to yield incorrect responses. This has been observed in various image classification tasks, including falsifying road sign detection and facial recognition, which could have severe consequences in real-world deployment. In this work, we investigate how these attacks could impact on network traffic analysis, and how a system could perform misclassification of common network attacks such as DDoS attacks. Using the CICIDS2017 data, we examine how vulnerable the data features used for intrusion detection are to perturbation attacks using FGSM adversarial examples. As a result, our method provides a defensive approach for assessing feature robustness that seeks to balance between classification accuracy whilst minimising the attack surface of the feature space.
2022-01-10
Alamaniotis, Miltiadis.  2021.  Fuzzy Integration of Kernel-Based Gaussian Processes Applied to Anomaly Detection in Nuclear Security. 2021 12th International Conference on Information, Intelligence, Systems Applications (IISA). :1–4.
Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.
2021-12-21
Ayed, Mohamed Ali, Talhi, Chamseddine.  2021.  Federated Learning for Anomaly-Based Intrusion Detection. 2021 International Symposium on Networks, Computers and Communications (ISNCC). :1–8.
We are attending a severe zero-day cyber attacks. Machine learning based anomaly detection is definitely the most efficient defence in depth approach. It consists to analyzing the network traffic in order to distinguish the normal behaviour from the abnormal one. This approach is usually implemented in a central server where all the network traffic is analyzed which can rise privacy issues. In fact, with the increasing adoption of Cloud infrastructures, it is important to reduce as much as possible the outsourcing of such sensitive information to the several network nodes. A better approach is to ask each node to analyze its own data and then to exchange its learning finding (model) with a coordinator. In this paper, we investigate the application of federated learning for network-based intrusion detection. Our experiment was conducted based on the C ICIDS2017 dataset. We present a f ederated learning on a deep learning algorithm C NN based on model averaging. It is a self-learning system for detecting anomalies caused by malicious adversaries without human intervention and can cope with new and unknown attacks without decreasing performance. These experimentation demonstrate that this approach is effective in detecting intrusion.
Bandi, Nahid, Tajbakhsh, Hesam, Analoui, Morteza.  2021.  FastMove: Fast IP Switching Moving Target Defense to Mitigate DDOS Attacks. 2021 IEEE Conference on Dependable and Secure Computing (DSC). :1–7.
Distributed denial of service attacks are still one of the greatest threats for computer systems and networks. We propose an intelligent moving target solution against DDOS flooding attacks. Our solution will use a fast-flux approach combined with moving target techniques to increase attack cost and complexity by bringing dynamics and randomization in network address space. It continually increases attack costs and makes it harder and almost infeasible for botnets to launch an attack. Along with performing selective proxy server replication and shuffling clients among this proxy, our solution can successfully separate and isolate attackers from benign clients and mitigate large-scale and complex flooding attacks. Our approach effectively stops both network and application-layer attacks at a minimum cost. However, while we try to make prevalent attack launches difficult and expensive for Bot Masters, this approach is good enough to combat zero-day attacks, too. Using DNS capabilities to change IP addresses frequently along with the proxy servers included in the proposed architecture, it is possible to hide the original server address from the attacker and invalidate the data attackers gathered during the reconnaissance phase of attack and make them repeat this step over and over. Our simulations demonstrate that we can mitigate large-scale attacks with minimum possible cost and overhead.
2021-12-20
Sahay, Rajeev, Brinton, Christopher G., Love, David J..  2021.  Frequency-based Automated Modulation Classification in the Presence of Adversaries. ICC 2021 - IEEE International Conference on Communications. :1–6.
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning to achieve robust AMC performance using raw in-phase and quadrature (IQ) time samples. Yet, deep learning models are highly susceptible to adversarial interference, which cause intelligent prediction models to misclassify received samples with high confidence. Furthermore, adversarial interference is often transferable, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification network. In this work, we present a novel receiver architecture consisting of deep learning models capable of withstanding transferable adversarial interference. Specifically, we show that adversarial attacks crafted to fool models trained on time-domain features are not easily transferable to models trained using frequency-domain features. In this capacity, we demonstrate classification performance improvements greater than 30% on recurrent neural networks (RNNs) and greater than 50% on convolutional neural networks (CNNs). We further demonstrate our frequency feature-based classification models to achieve accuracies greater than 99% in the absence of attacks.
Luo, Xinjian, Wu, Yuncheng, Xiao, Xiaokui, Ooi, Beng Chin.  2021.  Feature Inference Attack on Model Predictions in Vertical Federated Learning. 2021 IEEE 37th International Conference on Data Engineering (ICDE). :181–192.
Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information of the attack target's data distribution. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.
Hasan, Md. Mahmudul, Jahan, Mosarrat, Kabir, Shaily, Wagner, Christian.  2021.  A Fuzzy Logic-Based Trust Estimation in Edge-Enabled Vehicular Ad Hoc Networks. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.

Trust estimation of vehicles is vital for the correct functioning of Vehicular Ad Hoc Networks (VANETs) as it enhances their security by identifying reliable vehicles. However, accurate trust estimation still remains distant as existing works do not consider all malicious features of vehicles, such as dropping or delaying packets, altering content, and injecting false information. Moreover, data consistency of messages is not guaranteed here as they pass through multiple paths and can easily be altered by malicious relay vehicles. This leads to difficulty in measuring the effect of content tampering in trust calculation. Further, unreliable wireless communication of VANETs and unpredictable vehicle behavior may introduce uncertainty in the trust estimation and hence its accuracy. In this view, we put forward three trust factors - captured by fuzzy sets to adequately model malicious properties of a vehicle and apply a fuzzy logic-based algorithm to estimate its trust. We also introduce a parameter to evaluate the impact of content modification in trust calculation. Experimental results reveal that the proposed scheme detects malicious vehicles with high precision and recall and makes decisions with higher accuracy compared to the state-of-the-art.

Singleton, Larry, Zhao, Rui, Siy, Harvey, Song, Myoungkyu.  2021.  FireBugs: Finding and Repairing Cryptography API Misuses in Mobile Applications. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :1194–1201.
In this paper, we present FireBugs for Finding and Repairing Bugs based on security patterns. For the common misuse patterns of cryptography APIs (crypto APIs), we encode common cryptography rules into the pattern representations for bug detection and program repair regarding cryptography rule violations. In the evaluation, we conducted a case study to assess the bug detection capability by applying FireBugs to datasets mined from both open source and commercial projects. Also, we conducted a user study with professional software engineers at Mutual of Omaha Insurance Company to estimate the program repair capability. This evaluation showed that FireBugs can help professional engineers develop various cryptographic requirements in a resilient application.
2021-12-02
Wang, Zhiwen, Hu, Jiqiang, Sun, Hongtao.  2020.  False Data Injection Attacks in Smart Grid Using Gaussian Mixture Model. 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). :830–837.
The application of network technology and high-tech equipment in power systems has increased the degree of grid intelligence, and malicious attacks on smart grids have also increased year by year. The wrong data injection attack launched by the attacker will destroy the integrity of the data by changing the data of the sensor and controller, which will lead to the wrong decision of the control system and even paralyze the power transmission network. This paper uses the measured values of smart grid sensors as samples, analyzes the attack vectors maliciously injected by attackers and the statistical characteristics of system data, and proposes a false data injection attack detection strategy. It is considered that the measured values of sensors have spatial distribution characteristics, the Gaussian mixture model of grid node feature vectors is obtained by training sample values, the test measurement values are input into the Gaussian mixture model, and the knowledge of clustering is used to detect whether the power grid is malicious data attacks. The power supplies of IEEE-18 and IEEE-30 simulation systems was tested, and the influence of the system statistical measurement characteristics on the detection accuracy was analyzed. The results show that the proposed strategy has better detection performance than the support vector machine method.