Biblio

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2021-06-24
Angermeir, Florian, Voggenreiter, Markus, Moyón, Fabiola, Mendez, Daniel.  2021.  Enterprise-Driven Open Source Software: A Case Study on Security Automation. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). :278—287.
Agile and DevOps are widely adopted by the industry. Hence, integrating security activities with industrial practices, such as continuous integration (CI) pipelines, is necessary to detect security flaws and adhere to regulators’ demands early. In this paper, we analyze automated security activities in CI pipelines of enterprise-driven open source software (OSS). This shall allow us, in the long-run, to better understand the extent to which security activities are (or should be) part of automated pipelines. In particular, we mine publicly available OSS repositories and survey a sample of project maintainers to better understand the role that security activities and their related tools play in their CI pipelines. To increase transparency and allow other researchers to replicate our study (and to take different perspectives), we further disclose our research artefacts.Our results indicate that security activities in enterprise-driven OSS projects are scarce and protection coverage is rather low. Only 6.83% of the analyzed 8,243 projects apply security automation in their CI pipelines, even though maintainers consider security to be rather important. This alerts industry to keep the focus on vulnerabilities of 3rd Party software and it opens space for other improvements of practice which we outline in this manuscript.
2021-03-22
Kellogg, M., Schäf, M., Tasiran, S., Ernst, M. D..  2020.  Continuous Compliance. 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). :511–523.
Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions. We propose continuous compliance to guarantee that the codebase stays compliant on each code change using lightweight verification tools. Continuous compliance increases assurance and reduces costs. Continuous compliance is applicable to any source-code compliance requirement. To illustrate our approach, we built verification tools for five common audit controls related to data security: cryptographically unsafe algorithms must not be used, keys must be at least 256 bits long, credentials must not be hard-coded into program text, HTTPS must always be used instead of HTTP, and cloud data stores must not be world-readable. We evaluated our approach in three ways. (1) We applied our tools to over 5 million lines of open-source software. (2) We compared our tools to other publicly-available tools for detecting misuses of encryption on a previously-published benchmark, finding that only ours are suitable for continuous compliance. (3) We deployed a continuous compliance process at AWS, a large cloud-services company: we integrated verification tools into the compliance process (including auditors accepting their output as evidence) and ran them on over 68 million lines of code. Our tools and the data for the former two evaluations are publicly available.
2021-05-13
Wenhui, Sun, Kejin, Wang, Aichun, Zhu.  2020.  The Development of Artificial Intelligence Technology And Its Application in Communication Security. 2020 International Conference on Computer Engineering and Application (ICCEA). :752—756.
Artificial intelligence has been widely used in industries such as smart manufacturing, medical care and home furnishings. Among them, the value of the application in communication security is very important. This paper makes a further exploration of the artificial intelligence technology and its application, and gives a detailed analysis of its development, standardization and the application.
2021-05-03
Le, Son N., Srinivasan, Sudarshan K., Smith, Scott C..  2020.  Exploiting Dual-Rail Register Invariants for Equivalence Verification of NCL Circuits. 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS). :21–24.
Equivalence checking is one of the most scalable and useful verification techniques in industry. NULL Convention Logic (NCL) circuits utilize dual-rail signals (i.e., two wires to represent one bit of DATA), where the wires are inverses of each other during a DATA wavefront. In this paper, a technique that exploits this invariant at NCL register boundaries is proposed to improve the efficiency of equivalence verification of NCL circuits.
2021-05-05
Rathod, Jash, Joshi, Chaitali, Khochare, Janavi, Kazi, Faruk.  2020.  Interpreting a Black-Box Model used for SCADA Attack detection in Gas Pipelines Control System. 2020 IEEE 17th India Council International Conference (INDICON). :1—7.
Various Machine Learning techniques are considered to be "black-boxes" because of their limited interpretability and explainability. This cannot be afforded, especially in the domain of Cyber-Physical Systems, where there can be huge losses of infrastructure of industries and Governments. Supervisory Control And Data Acquisition (SCADA) systems need to detect and be protected from cyber-attacks. Thus, we need to adopt approaches that make the system secure, can explain predictions made by model, and interpret the model in a human-understandable format. Recently, Autoencoders have shown great success in attack detection in SCADA systems. Numerous interpretable machine learning techniques are developed to help us explain and interpret models. The work presented here is a novel approach to use techniques like Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP) for interpretation of Autoencoder networks trained on a Gas Pipelines Control System to detect attacks in the system.
2021-03-01
Kerim, A., Genc, B..  2020.  Mobile Games Success and Failure: Mining the Hidden Factors. 2020 7th International Conference on Soft Computing Machine Intelligence (ISCMI). :167–171.
Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released each day. However, a few of them succeed while the majority fail. Towards the goal of investigating the potential correlation between the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousands games were considered for that reason. We show that specific game attributes, such as number of IAPs (In-App Purchases), belonging to the puzzle genre, supporting different languages and being produced by a mature developer highly and positively affect the success of the game in the future. Moreover, we show that releasing the game in July and not including any IAPs seems to be highly associated with the game’s failure. Our second main contribution, is the proposal of a novel success score metric that reflects multiple objectives, in contrast to evaluating only revenue, average rating or rating count. We also employ different machine learning models, namely, SVM (Support Vector Machine), RF (Random Forest) and Deep Learning (DL) to predict this success score metric of a mobile game given its attributes. The trained models were able to predict this score, as well as the rating average and rating count of a mobile game with more than 70% accuracy. This prediction can help developers before releasing their game to the market to avoid any potential disappointments.
2021-03-17
Lee, Y., Woo, S., Song, Y., Lee, J., Lee, D. H..  2020.  Practical Vulnerability-Information-Sharing Architecture for Automotive Security-Risk Analysis. IEEE Access. 8:120009—120018.
Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanical devices to electronic control devices, and is not moving to Internet of Things devices connected to 5G networks. Owing to the convergence of automobile-information and communication technology (ICT), the safety and convenience features of automobiles have improved significantly. However, cyberattacks that occur in the existing ICT environment and can occur in the upcoming 5G network are being replicated in the automobile environment. In a hyper-connected society where 5G networks are commercially available, automotive security is extremely important, as vehicles become the center of vehicle to everything (V2X) communication connected to everything around them. Designing, developing, and deploying information security techniques for vehicles require a systematic security-risk-assessment and management process throughout the vehicle's lifecycle. To do this, a security risk analysis (SRA) must be performed, which requires an analysis of cyber threats on automotive vehicles. In this study, we introduce a cyber kill chain-based cyberattack analysis method to create a formal vulnerability-analysis system. We can also analyze car-hacking studies that were conducted on real cars to identify the characteristics of the attack stages of existing car-hacking techniques and propose the minimum but essential measures for defense. Finally, we propose an automotive common-vulnerabilities-and-exposure system to manage and share evolving vehicle-related cyberattacks, threats, and vulnerabilities.
2021-03-04
Kalin, J., Ciolino, M., Noever, D., Dozier, G..  2020.  Black Box to White Box: Discover Model Characteristics Based on Strategic Probing. 2020 Third International Conference on Artificial Intelligence for Industries (AI4I). :60—63.

In Machine Learning, White Box Adversarial Attacks rely on knowing underlying knowledge about the model attributes. This works focuses on discovering to distrinct pieces of model information: the underlying architecture and primary training dataset. With the process in this paper, a structured set of input probes and the output of the model become the training data for a deep classifier. Two subdomains in Machine Learning are explored - image based classifiers and text transformers with GPT-2. With image classification, the focus is on exploring commonly deployed architectures and datasets available in popular public libraries. Using a single transformer architecture with multiple levels of parameters, text generation is explored by fine tuning off different datasets. Each dataset explored in image and text are distinguishable from one another. Diversity in text transformer outputs implies further research is needed to successfully classify architecture attribution in text domain.

2021-01-11
Rajapkar, A., Binnar, P., Kazi, F..  2020.  Design of Intrusion Prevention System for OT Networks Using Deep Neural Networks. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.

The Automation industries that uses Supervisory Control and Data Acquisition (SCADA) systems are highly vulnerable for Network threats. Systems that are air-gapped and isolated from the internet are highly affected due to insider attacks like Spoofing, DOS and Malware threats that affects confidentiality, integrity and availability of Operational Technology (OT) system elements and degrade its performance even though security measures are taken. In this paper, a behavior-based intrusion prevention system (IPS) is designed for OT networks. The proposed system is implemented on SCADA test bed with two systems replicates automation scenarios in industry. This paper describes 4 main classes of cyber-attacks with their subclasses against SCADA systems and methodology with design of components of IPS system, database creation, Baselines and deployment of system in environment. IPS system identifies not only IT protocols but also Industry Control System (ICS) protocols Modbus and DNP3 with their inside communication fields using deep packet inspection (DPI). The analytical results show 99.89% accuracy on binary classification and 97.95% accuracy on multiclass classification of different attack vectors performed on network with low false positive rate. These results are also validated by actual deployment of IPS in SCADA systems with the prevention of DOS attack.

2020-10-30
Pearce, Hammond, Pinisetty, Srinivas, Roop, Partha S., Kuo, Matthew M. Y., Ukil, Abhisek.  2020.  Smart I/O Modules for Mitigating Cyber-Physical Attacks on Industrial Control Systems. IEEE Transactions on Industrial Informatics. 16:4659—4669.

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.

2021-03-29
DiMase, D., Collier, Z. A., Chandy, J., Cohen, B. S., D'Anna, G., Dunlap, H., Hallman, J., Mandelbaum, J., Ritchie, J., Vessels, L..  2020.  A Holistic Approach to Cyber Physical Systems Security and Resilience. 2020 IEEE Systems Security Symposium (SSS). :1—8.

A critical need exists for collaboration and action by government, industry, and academia to address cyber weaknesses or vulnerabilities inherent to embedded or cyber physical systems (CPS). These vulnerabilities are introduced as we leverage technologies, methods, products, and services from the global supply chain throughout a system's lifecycle. As adversaries are exploiting these weaknesses as access points for malicious purposes, solutions for system security and resilience become a priority call for action. The SAE G-32 Cyber Physical Systems Security Committee has been convened to address this complex challenge. The SAE G-32 will take a holistic systems engineering approach to integrate system security considerations to develop a Cyber Physical System Security Framework. This framework is intended to bring together multiple industries and develop a method and common language which will enable us to more effectively, efficiently, and consistently communicate a risk, cost, and performance trade space. The standard will allow System Integrators to make decisions utilizing a common framework and language to develop affordable, trustworthy, resilient, and secure systems.

2021-07-28
Vinzamuri, Bhanukiran, Khabiri, Elham, Bhamidipaty, Anuradha, Mckim, Gregory, Gandhi, Biren.  2020.  An End-to-End Context Aware Anomaly Detection System. 2020 IEEE International Conference on Big Data (Big Data). :1689—1698.
Anomaly detection (AD) is very important across several real-world problems in the heavy industries and Internet-of-Things (IoT) domains. Traditional methods so far have categorized anomaly detection into (a) unsupervised, (b) semi-supervised and (c) supervised techniques. A relatively unexplored direction is the development of context aware anomaly detection systems which can build on top of any of these three techniques by using side information. Context can be captured from a different modality such as semantic graphs encoding grouping of sensors governed by the physics of the asset. Process flow diagrams of an operational plant depicting causal relationships between sensors can also provide useful context for ML algorithms. Capturing such semantics by itself can be pretty challenging, however, our paper mainly focuses on, (a) designing and implementing effective anomaly detection pipelines using sparse Gaussian Graphical Models with various statistical distance metrics, and (b) differentiating these pipelines by embedding contextual semantics inferred from graphs so as to obtain better KPIs in practice. The motivation for the latter of these two has been explained above, and the former in particular is well motivated by the relatively mediocre performance of highly parametric deep learning methods for small tabular datasets (compared to images) such as IoT sensor data. In contrast to such traditional automated deep learning (AutoAI) techniques, our anomaly detection system is based on developing semantics-driven industry specific ML pipelines which perform scalable computation evaluating several models to identify the best model. We benchmark our AD method against state-of-the-art AD techniques on publicly available UCI datasets. We also conduct a case study on IoT sensor and semantic data procured from a large thermal energy asset to evaluate the importance of semantics in enhancing our pipelines. In addition, we also provide explainable insights for our model which provide a complete perspective to a reliability engineer.
2020-12-28
Lee, H., Cho, S., Seong, J., Lee, S., Lee, W..  2020.  De-identification and Privacy Issues on Bigdata Transformation. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :514—519.

As the number of data in various industries and government sectors is growing exponentially, the `7V' concept of big data aims to create a new value by indiscriminately collecting and analyzing information from various fields. At the same time as the ecosystem of the ICT industry arrives, big data utilization is treatened by the privacy attacks such as infringement due to the large amount of data. To manage and sustain the controllable privacy level, there need some recommended de-identification techniques. This paper exploits those de-identification processes and three types of commonly used privacy models. Furthermore, this paper presents use cases which can be adopted those kinds of technologies and future development directions.

2021-05-05
Singh, Sukhpreet, Jagdev, Gagandeep.  2020.  Execution of Big Data Analytics in Automotive Industry using Hortonworks Sandbox. 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). :158—163.

The market landscape has undergone dramatic change because of globalization, shifting marketing conditions, cost pressure, increased competition, and volatility. Transforming the operation of businesses has been possible because of the astonishing speed at which technology has witnessed the change. The automotive industry is on the edge of a revolution. The increased customer expectations, changing ownership, self-driving vehicles and much more have led to the transformation of automobiles, applications, and services from artificial intelligence, sensors, RFID to big data analysis. Large automobiles industries have been emphasizing the collection of data to gain insight into customer's expectations, preferences, and budgets alongside competitor's policies. Statistical methods can be applied to historical data, which has been gathered from various authentic sources and can be used to identify the impact of fixed and variable marketing investments and support automakers to come up with a more effective, precise, and efficient approach to target customers. Proper analysis of supply chain data can disclose the weak links in the chain enabling to adopt timely countermeasures to minimize the adverse effects. In order to fully gain benefit from analytics, the collaboration of a detailed set of capabilities responsible for intersecting and integrating with multiple functions and teams across the business is required. The effective role played by big data analysis in the automobile industry has also been expanded in the research paper. The research paper discusses the scope and challenges of big data. The paper also elaborates on the working technology behind the concept of big data. The paper illustrates the working of MapReduce technology that executes in the back end and is responsible for performing data mining.

Coulter, Rory, Zhang, Jun, Pan, Lei, Xiang, Yang.  2020.  Unmasking Windows Advanced Persistent Threat Execution. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :268—276.

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.

2021-03-30
Pyatnisky, I. A., Sokolov, A. N..  2020.  Assessment of the Applicability of Autoencoders in the Problem of Detecting Anomalies in the Work of Industrial Control Systems.. 2020 Global Smart Industry Conference (GloSIC). :234—239.

Deep learning methods are increasingly becoming solutions to complex problems, including the search for anomalies. While fully-connected and convolutional neural networks have already found their application in classification problems, their applicability to the problem of detecting anomalies is limited. In this regard, it is proposed to use autoencoders, previously used only in problems of reducing the dimension and removing noise, as a method for detecting anomalies in the industrial control system. A new method based on autoencoders is proposed for detecting anomalies in the operation of industrial control systems (ICS). Several neural networks based on auto-encoders with different architectures were trained, and the effectiveness of each of them in the problem of detecting anomalies in the work of process control systems was evaluated. Auto-encoders can detect the most complex and non-linear dependencies in the data, and as a result, can show the best quality for detecting anomalies. In some cases, auto-encoders require fewer machine resources.

2021-06-24
King, Andrew, Kaleem, Faisal, Rabieh, Khaled.  2020.  A Survey on Privacy Issues of Augmented Reality Applications. 2020 IEEE Conference on Application, Information and Network Security (AINS). :32—40.
Privacy is one of the biggest concerns of the coming decade, ranking third among concerns of consumers. Data breaches and leaks are constantly in the news with companies like Facebook and Amazon being outed for their excessive data collection. With companies and governmental agencies tracking and monitoring individuals to a great degree, there are concerns that contemporary technologies that feed into these systems can be misused or misappropriated further. Frameworks currently in place fail to address many of these consumer's concerns and even the legal framework could use further elaboration to better control the way data is handled. In this paper, We address the current industrial standards, frameworks, and concerns of one of the biggest technology trends right now, the Augmented Reality. The expected prevalence of augmented reality applications necessitates a deeper study not only of their security but the expected challenges of users using such applications as well.
2021-02-03
Rehan, S., Singh, R..  2020.  Industrial and Home Automation, Control, Safety and Security System using Bolt IoT Platform. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :787—793.
This paper describes a system that comprises of control, safety and security subsystem for industries and homes. The entire system is based on the Bolt IoT platform. Using this system, the user can control the devices such as LEDs, speed of the fan or DC motor, monitor the temperature of the premises with an alert sub-system for critical temperatures through SMS and call, monitor the presence of anyone inside the premises with an alert sub-system about any intrusion through SMS and call. If the system is used specifically in any industry then instead of monitoring the temperature any other physical quantity, which is critical for that industry, can be monitored using suitable sensors. In addition, the cloud connectivity is provided to the system using the Bolt IoT module and temperature data is sent to the cloud where using machine-learning algorithm the future temperature is predicted to avoid any accidents in the future.
2021-02-01
Ng, M., Coopamootoo, K. P. L., Toreini, E., Aitken, M., Elliot, K., Moorsel, A. van.  2020.  Simulating the Effects of Social Presence on Trust, Privacy Concerns Usage Intentions in Automated Bots for Finance. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :190–199.
FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.
2021-04-27
Javid, T., Faris, M., Beenish, H., Fahad, M..  2020.  Cybersecurity and Data Privacy in the Cloudlet for Preliminary Healthcare Big Data Analytics. 2020 International Conference on Computing and Information Technology (ICCIT-1441). :1—4.

In cyber physical systems, cybersecurity and data privacy are among most critical considerations when dealing with communications, processing, and storage of data. Geospatial data and medical data are examples of big data that require seamless integration with computational algorithms as outlined in Industry 4.0 towards adoption of fourth industrial revolution. Healthcare Industry 4.0 is an application of the design principles of Industry 4.0 to the medical domain. Mobile applications are now widely used to accomplish important business functions in almost all industries. These mobile devices, however, are resource poor and proved insufficient for many important medical applications. Resource rich cloud services are used to augment poor mobile device resources for data and compute intensive applications in the mobile cloud computing paradigm. However, the performance of cloud services is undesirable for data-intensive, latency-sensitive mobile applications due increased hop count between the mobile device and the cloud server. Cloudlets are virtual machines hosted in server placed nearby the mobile device and offer an attractive alternative to the mobile cloud computing in the form of mobile edge computing. This paper outlines cybersecurity and data privacy aspects for communications of measured patient data from wearable wireless biosensors to nearby cloudlet host server in order to facilitate the cloudlet based preliminary and essential complex analytics for the medical big data.

2021-07-27
Shere, A. R. K., Nurse, J. R. C., Flechais, I..  2020.  "Security should be there by default": Investigating how journalists perceive and respond to risks from the Internet of Things. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :240—249.
Journalists have long been the targets of both physical and cyber-attacks from well-resourced adversaries. Internet of Things (IoT) devices are arguably a new avenue of threat towards journalists through both targeted and generalised cyber-physical exploitation. This study comprises three parts: First, we interviewed 11 journalists and surveyed 5 further journalists, to determine the extent to which journalists perceive threats through the IoT, particularly via consumer IoT devices. Second, we surveyed 34 cyber security experts to establish if and how lay-people can combat IoT threats. Third, we compared these findings to assess journalists' knowledge of threats, and whether their protective mechanisms would be effective against experts' depictions and predictions of IoT threats. Our results indicate that journalists generally are unaware of IoT-related risks and are not adequately protecting themselves; this considers cases where they possess IoT devices, or where they enter IoT-enabled environments (e.g., at work or home). Expert recommendations spanned both immediate and longterm mitigation methods, including practical actions that are technical and socio-political in nature. However, all proposed individual mitigation methods are likely to be short-term solutions, with 26 of 34 (76.5%) of cyber security experts responding that within the next five years it will not be possible for the public to opt-out of interaction with the IoT.
2020-05-18
Xiaolei, WANG, Zhengning, YU, Xuemin, NIU, Xianfeng, LU, Hao, YANG, Zhongjiawen, LIU.  2019.  Combination Multiple Faults Diagnosis Method Applied to the Aero-engine Based on Improved Signed Directed Graph. 2019 4th International Conference on Measurement, Information and Control (ICMIC). :1–10.
In signed directed graph (SDG) fault diagnosis model, only single fault can be diagnosed. In order to meet the requirements of multiple faults diagnosis, in this paper, improved signed directed graph (ISDG) fault diagnosis model was proposed. The logic and influence between nodes were included in ISDG model. With ISDG model, complex logic can be shown, multiple faults can be diagnosed and the optimal sequence can be determined. Two algorithms are proposed in this paper. One algorithm can obtain the multiple faults combine logic, and the other algorithm can obtain the optimal path of fault diagnosis. According to these two algorithms, the efficiency was improved and the cost was reduced in the multiple fault diagnosis process. Finally, the faults of an aircraft engine bleed system were diagnosed with the interactive algorithm. The proposed algorithms can obtain a diagnosis result effectively. The results of two cases prove that these algorithms can be used for multiple fault diagnosis.
2020-10-19
Engoulou, Richard Gilles, Bellaiche, Martine, Halabi, Talal, Pierre, Samuel.  2019.  A Decentralized Reputation Management System for Securing the Internet of Vehicles. 2019 International Conference on Computing, Networking and Communications (ICNC). :900–904.
The evolution of the Internet of Vehicles (IoV) paradigm has recently attracted a lot of researchers and industries. Vehicular Ad Hoc Networks (VANET) is the networking model that lies at the heart of this technology. It enables the vehicles to exchange relevant information concerning road conditions and safety. However, ensuring communication security has been and still is one of the main challenges to vehicles' interconnection. To secure the interconnected vehicular system, many cryptography techniques, communication protocols, and certification and reputation-based security approaches were proposed. Nonetheless, some limitations are still present, preventing the practical implementation of such approaches. In this paper, we first define a set of locally-perceived behavioral reputation parameters that enable a distributed evaluation of vehicles' reputation. Then, we integrate these parameters into the design of a reputation management system to exclude malicious or faulty vehicles from the IoV network. Our system can help in the prevention of several attacks on the VANET environment such as Sybil and Denial of Service attacks, and can be implemented in a fully decentralized fashion.
2020-02-17
Skopik, Florian, Filip, Stefan.  2019.  Design principles for national cyber security sensor networks: Lessons learned from small-scale demonstrators. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.
The timely exchange of information on new threats and vulnerabilities has become a cornerstone of effective cyber defence in recent years. Especially national authorities increasingly assume their role as information brokers through national cyber security centres and distribute warnings on new attack vectors and vital recommendations on how to mitigate them. Although many of these initiatives are effective to some degree, they also suffer from severe limitations. Many steps in the exchange process require extensive human involvement to manually review, vet, enrich, analyse and distribute security information. Some countries have therefore started to adopt distributed cyber security sensor networks to enable the automatic collection, analysis and preparation of security data and thus effectively overcome limiting scalability factors. The basic idea of IoC-centric cyber security sensor networks is that the national authorities distribute Indicators of Compromise (IoCs) to organizations and receive sightings in return. This effectively helps them to estimate the spreading of malware, anticipate further trends of spreading and derive vital findings for decision makers. While this application case seems quite simple, there are some tough questions to be answered in advance, which steer the further design decisions: How much can the monitored organization be trusted to be a partner in the search for malware? How much control of the scanning process should be delegated to the organization? What is the right level of search depth? How to deal with confidential indicators? What can be derived from encrypted traffic? How are new indicators distributed, prioritized, and scan targets selected in a scalable manner? What is a good strategy to re-schedule scans to derive meaningful data on trends, such as rate of spreading? This paper suggests a blueprint for a sensor network and raises related questions, outlines design principles, and discusses lessons learned from small-scale pilots.
2020-05-22
Ranjan, G S K, Kumar Verma, Amar, Radhika, Sudha.  2019.  K-Nearest Neighbors and Grid Search CV Based Real Time Fault Monitoring System for Industries. 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). :1—5.
Fault detection in a machine at earlier stage can prevent severe damage and loss to the industries. Fault detection techniques are broadly classified into three categories; signature extraction-based, model-based and knowledge-based approach. Model-based techniques are efficient for raising an alarm signal if there is any fault in the machine. This paper focuses on one such model based-technique to identify the internal faults of induction machine. The model developed is deployed in the end to make it feasible to use in real time. K-Nearest Neighbors (KNN) and grid search cross validation (CV) have been used to train and optimize the model to give the best results. The advantage of proposed algorithm is the accuracy in prediction which has been seen to be 80%. Finally, a user friendly interface has been built using Flask, a python web framework.