Visible to the public Biblio

Found 12254 results

Kumar, Rahul, Sethi, Kamalakanta, Prajapati, Nishant, Rout, Rashmi Ranjan, Bera, Padmalochan.  2020.  Machine Learning based Malware Detection in Cloud Environment using Clustering Approach. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—7.

Enforcing security and resilience in a cloud platform is an essential but challenging problem due to the presence of a large number of heterogeneous applications running on shared resources. A security analysis system that can detect threats or malware must exist inside the cloud infrastructure. Much research has been done on machine learning-driven malware analysis, but it is limited in computational complexity and detection accuracy. To overcome these drawbacks, we proposed a new malware detection system based on the concept of clustering and trend micro locality sensitive hashing (TLSH). We used Cuckoo sandbox, which provides dynamic analysis reports of files by executing them in an isolated environment. We used a novel feature extraction algorithm to extract essential features from the malware reports obtained from the Cuckoo sandbox. Further, the most important features are selected using principal component analysis (PCA), random forest, and Chi-square feature selection methods. Subsequently, the experimental results are obtained for clustering and non-clustering approaches on three classifiers, including Decision Tree, Random Forest, and Logistic Regression. The model performance shows better classification accuracy and false positive rate (FPR) as compared to the state-of-the-art works and non-clustering approach at significantly lesser computation cost.

Cano M, Jeimy J..  2020.  Sandbox: Revindicate failure as the foundation of learning. 2020 IEEE World Conference on Engineering Education (EDUNINE). :1—6.

In an increasingly asymmetric context of both instability and permanent innovation, organizations demand new capacities and learning patterns. In this sense, supervisors have adopted the metaphor of the "sandbox" as a strategy that allows their regulated parties to experiment and test new proposals in order to study them and adjust to the established compliance frameworks. Therefore, the concept of the "sandbox" is of educational interest as a way to revindicate failure as a right in the learning process, allowing students to think, experiment, ask questions and propose ideas outside the known theories, and thus overcome the mechanistic formation rooted in many of the higher education institutions. Consequently, this article proposes the application of this concept for educational institutions as a way of resignifying what students have learned.

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.

Chi, Po-Wen, Wang, Ming-Hung, Zheng, Yu.  2020.  SandboxNet: An Online Malicious SDN Application Detection Framework for SDN Networking. 2020 International Computer Symposium (ICS). :397—402.

Software Defined Networking (SDN) is a concept that decouples the control plane and the user plane. So the network administrator can easily control the network behavior through its own programs. However, the administrator may unconsciously apply some malicious programs on SDN controllers so that the whole network may be under the attacker’s control. In this paper, we discuss the malicious software issue on SDN networks. We use the idea of sandbox to propose a sandbox network called SanboxNet. We emulate a virtual isolated network environment to verify the SDN application functions. With continuous monitoring, we can locate the suspicious SDN applications. We also consider the sandbox-evading issue in our framework. The emulated networks and the real world networks will be indistinguishable to the SDN controller.

Poudyal, Subash, Dasgupta, Dipankar.  2020.  AI-Powered Ransomware Detection Framework. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :1154—1161.

Ransomware attacks are taking advantage of the ongoing pandemics and attacking the vulnerable systems in business, health sector, education, insurance, bank, and government sectors. Various approaches have been proposed to combat ransomware, but the dynamic nature of malware writers often bypasses the security checkpoints. There are commercial tools available in the market for ransomware analysis and detection, but their performance is questionable. This paper aims at proposing an AI-based ransomware detection framework and designing a detection tool (AIRaD) using a combination of both static and dynamic malware analysis techniques. Dynamic binary instrumentation is done using PIN tool, function call trace is analyzed leveraging Cuckoo sandbox and Ghidra. Features extracted at DLL, function call, and assembly level are processed with NLP, association rule mining techniques and fed to different machine learning classifiers. Support vector machine and Adaboost with J48 algorithms achieved the highest accuracy of 99.54% with 0.005 false-positive rates for a multi-level combined term frequency approach.

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.

Chalkiadakis, Nikolaos, Deyannis, Dimitris, Karnikis, Dimitris, Vasiliadis, Giorgos, Ioannidis, Sotiris.  2020.  The Million Dollar Handshake: Secure and Attested Communications in the Cloud. 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). :63—70.

The number of applications and services that are hosted on cloud platforms is constantly increasing. Nowadays, more and more applications are hosted as services on cloud platforms, co-existing with other services in a mutually untrusted environment. Facilities such as virtual machines, containers and encrypted communication channels aim to offer isolation between the various applications and protect sensitive user data. However, such techniques are not always able to provide a secure execution environment for sensitive applications nor they offer guarantees that data are not monitored by an honest but curious provider once they reach the cloud infrastructure. The recent advancements of trusted execution environments within commodity processors, such as Intel SGX, provide a secure reverse sandbox, where code and data are isolated even from the underlying operating system. Moreover, Intel SGX provides a remote attestation mechanism, allowing the communicating parties to verify their identity as well as prove that code is executed on hardware-assisted software enclaves. Many approaches try to ensure code and data integrity, as well as enforce channel encryption schemes such as TLS, however, these techniques are not enough to achieve complete isolation and secure communications without hardware assistance or are not efficient in terms of performance. In this work, we design and implement a practical attestation system that allows the service provider to offer a seamless attestation service between the hosted applications and the end clients. Furthermore, we implement a novel caching system that is capable to eliminate the latencies introduced by the remote attestation process. Our approach allows the parties to attest one another before each communication attempt, with improved performance when compared to a standard TLS handshake.

Zhang, Yunxiang, Rao, Zhuyi.  2020.  Research on Information Security Evaluation Based on Artificial Neural Network. 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :424–428.

In order to improve the information security ability of the network information platform, the information security evaluation method is proposed based on artificial neural network. Based on the comprehensive analysis of the security events in the construction of the network information platform, the risk assessment model of the network information platform is constructed based on the artificial neural network theory. The weight calculation algorithm of artificial neural network and the minimum artificial neural network pruning algorithm are also given, which can realize the quantitative evaluation of network information security. The fuzzy neural network weighted control method is used to control the information security, and the non-recursive traversal method is adopted to realize the adaptive training of information security assessment process. The adaptive learning of the artificial neural network is carried out according to the conditions, and the ability of information encryption and transmission is improved. The information security assessment is realized. The simulation results show that the method is accurate and ensures the information security.

Sheng, Mingren, Liu, Hongri, Yang, Xu, Wang, Wei, Huang, Junheng, Wang, Bailing.  2020.  Network Security Situation Prediction in Software Defined Networking Data Plane. 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA). :475–479.
Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability.
Nakhushev, Rakhim S., Sukhanova, Natalia V..  2020.  Application of the Neural Networks for Cryptographic Information Security. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT QM IS). :421–423.
The object of research is information security. The tools used for research are artificial neural networks. The goal is to increase the cryptography security. The problems are: the big volume of information, the expenses for neural networks design and training. It is offered to use the neural network for the cryptographic transformation of information.
Sheptunov, Sergey A., Sukhanova, Natalia V..  2020.  The Problems of Design and Application of Switching Neural Networks in Creation of Artificial Intelligence. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT QM IS). :428–431.
The new switching architecture of the neural networks was proposed. The switching neural networks consist of the neurons and the switchers. The goal is to reduce expenses on the artificial neural network design and training. For realization of complex models, algorithms and methods of management the neural networks of the big size are required. The number of the interconnection links “everyone with everyone” grows with the number of neurons. The training of big neural networks requires the resources of supercomputers. Time of training of neural networks also depends on the number of neurons in the network. Switching neural networks are divided into fragments connected by the switchers. Training of switcher neuron network is provided by fragments. On the basis of switching neural networks the devices of associative memory were designed with the number of neurons comparable to the human brain.
Li, Yizhi.  2020.  Research on Application of Convolutional Neural Network in Intrusion Detection. 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). :720–723.
At present, our life is almost inseparable from the network, the network provides a lot of convenience for our life. However, a variety of network security incidents occur very frequently. In recent years, with the continuous development of neural network technology, more and more researchers have applied neural network to intrusion detection, which has developed into a new research direction in intrusion detection. As long as the neural network is provided with input data including network data packets, through the process of self-learning, the neural network can separate abnormal data features and effectively detect abnormal data. Therefore, the article innovatively proposes an intrusion detection method based on deep convolutional neural networks (CNN), which is used to test on public data sets. The results show that the model has a higher accuracy rate and a lower false negative rate than traditional intrusion detection methods.
Monakhov, Yuri, Monakhov, Mikhail, Telny, Andrey, Mazurok, Dmitry, Kuznetsova, Anna.  2020.  Improving Security of Neural Networks in the Identification Module of Decision Support Systems. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :571–574.
In recent years, neural networks have been implemented while solving various tasks. Deep learning algorithms provide state of the art performance in computer vision, NLP, speech recognition, speaker recognition and many other fields. In spite of the good performance, neural networks have significant drawback- they have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. While being imperceptible to a human eye, such perturbations lead to significant drop in classification accuracy. It is demonstrated by many studies related to neural network security. Considering the pros and cons of neural networks, as well as a variety of their applications, developing of the methods to improve the robustness of neural networks against adversarial attacks becomes an urgent task. In the article authors propose the “minimalistic” attacker model of the decision support system identification unit, adaptive recommendations on security enhancing, and a set of protective methods. Suggested methods allow for significant increase in classification accuracy under adversarial attacks, as it is demonstrated by an experiment outlined in this article.
Liu, Shuyong, Jiang, Hongrui, Li, Sizhao, Yang, Yang, Shen, Linshan.  2020.  A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks. 2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :39–42.
Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%.
Venceslai, Valerio, Marchisio, Alberto, Alouani, Ihsen, Martina, Maurizio, Shafique, Muhammad.  2020.  NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.

Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques.

Mahmoud, Loreen, Praveen, Raja.  2020.  Artificial Neural Networks for detecting Intrusions: A survey. 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). :41–48.
Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.
Wang, Xiaoyu, Gao, Yuanyuan, Zhang, Guangna, Guo, Mingxi.  2020.  Prediction of Optimal Power Allocation for Enhancing Security-Reliability Tradeoff with the Application of Artificial Neural Networks. 2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC). :40–45.
In this paper, we propose a power allocation scheme in order to improve both secure and reliable performance in the wireless two-hop threshold-selection decode-and-forward (DF) relaying networks, which is so crucial to set a threshold value related the signal-to-noise ratio (SNR) of the source signal at relay nodes for perfect decoding. We adapt the maximal-ratio combining (MRC) receiving SNR from the direct and relaying paths both at the destination and at the eavesdropper. Particularly worth mentioning is that the closed expression form of outage probability and intercept probability is driven, which can quantify the security and reliability, respectively. We also make endeavors to utilize a metric to tradeoff the security and the reliability (SRT) and find out the relevance between them in the balanced case. But beyond that, in the pursuit of tradeoff performance, power allocation tends to depend on the threshold value. In other words, it provides a new method optimizing total power to the source and the relay by the threshold value. The results are obtained from analysis, confirmed by simulation, and predicted by artificial neural networks (ANNs), which is trained with back propagation (BP) algorithm, and thus the feasibility of the proposed method is verified.
Everson, Douglas, Cheng, Long.  2020.  Network Attack Surface Simplification for Red and Blue Teams. 2020 IEEE Secure Development (SecDev). :74–80.
Network port scans are a key first step to developing a true understanding of a network-facing attack surface. However in large-scale networks, the data resulting from such scans can be too numerous for Red Teams to process for manual and semiautomatic testing. Indiscriminate port scans can also compromise a Red Team seeking to quickly gain a foothold on a network. A large attack surface can even complicate Blue Team activities like threat hunting. In this paper we provide a cluster analysis methodology designed to group similar hosts to reduce security team workload and Red Team observability. We also measure the Internet-facing network attack surface of 13 organizations by clustering their hosts based on similarity. Through a case study we demonstrate how the output of our clustering technique provides new insight to both Red and Blue Teams, allowing them to quickly identify potential high-interest points on the attack surface.
Liu, Xinlin, Huang, Jianhua, Luo, Weifeng, Chen, Qingming, Ye, Peishan, Wang, Dingbo.  2020.  Research on Attack Mechanism using Attack Surface. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :137–141.
A approach to research on the attack mechanism designs through attack surface technology due to the complexity of the attack mechanism. The attack mechanism of a mimic architecture is analyzed in a relative way using attack surface metrics to indicate whether mimic architectures are safer than non-mimic architectures. The definition of the architectures attack surface in terms of the mimic brackets along three abstract dimensions referenced the system attack surface. The larger the attack surface, the more likely the architecture will be attacked.
Lit, Yanyan, Kim, Sara, Sy, Eric.  2021.  A Survey on Amazon Alexa Attack Surfaces. 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). :1–7.
Since being launched in 2014, Alexa, Amazon's versatile cloud-based voice service, is now active in over 100 million households worldwide [1]. Alexa's user-friendly, personalized vocal experience offers customers a more natural way of interacting with cutting-edge technology by allowing the ability to directly dictate commands to the assistant. Now in the present year, the Alexa service is more accessible than ever, available on hundreds of millions of devices from not only Amazon but third-party device manufacturers. Unfortunately, that success has also been the source of concern and controversy. The success of Alexa is based on its effortless usability, but in turn, that has led to a lack of sufficient security. This paper surveys various attacks against Amazon Alexa ecosystem including attacks against the frontend voice capturing and the cloud backend voice command recognition and processing. Overall, we have identified six attack surfaces covering the lifecycle of Alexa voice interaction that spans several stages including voice data collection, transmission, processing and storage. We also discuss the potential mitigation solutions for each attack surface to better improve Alexa or other voice assistants in terms of security and privacy.
Nie, Guanglai, Zhang, Zheng, Zhao, Yufeng.  2020.  The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface. 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA). :281–287.
In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm.
Zhang, Yaqin, Ma, Duohe, Sun, Xiaoyan, Chen, Kai, Liu, Feng.  2020.  WGT: Thwarting Web Attacks Through Web Gene Tree-based Moving Target Defense. 2020 IEEE International Conference on Web Services (ICWS). :364–371.
Moving target defense (MTD) suggests a game-changing way of enhancing web security by increasing uncertainty and complexity for attackers. A good number of web MTD techniques have been investigated to counter various types of web attacks. However, in most MTD techniques, only fixed attributes of the attack surface are shifted, leaving the rest exploitable by the attackers. Currently, there are few mechanisms to support the whole attack surface movement and solve the partial coverage problem, where only a fraction of the possible attributes shift in the whole attack surface. To address this issue, this paper proposes a Web Gene Tree (WGT) based MTD mechanism. The key point is to extract all potential exploitable key attributes related to vulnerabilities as web genes, and mutate them using various MTD techniques to withstand various attacks. Experimental results indicate that, by randomly shifting web genes and diversely inserting deceptive ones, the proposed WGT mechanism outperforms other existing schemes and can significantly improve the security of web applications.
Liu, Xinghua, Bai, Dandan, Jiang, Rui.  2020.  Load Frequency Control of Multi-area Power Systems under Deception Attacks*. 2020 Chinese Automation Congress (CAC). :3851–3856.
This paper investigated the sliding mode load frequency control (LFC) for an multi-area power system (MPS) under deception attacks (DA). A Luenberger observer is designed to obtain the state estimate of MPS. By using the Lyapunov-Krasovskii method, a sliding mode surface (SMS) is designed to ensure the stability. Then the accessibility analysis ensures that the trajectory of the MPS can reach the specified SMS. Finally, the serviceability of the method is explained by providing a case study.
Luo, Yukui, Gongye, Cheng, Ren, Shaolei, Fei, Yunsi, Xu, Xiaolin.  2020.  Stealthy-Shutdown: Practical Remote Power Attacks in Multi - Tenant FPGAs. 2020 IEEE 38th International Conference on Computer Design (ICCD). :545–552.
With the deployment of artificial intelligent (AI) algorithms in a large variety of applications, there creates an increasing need for high-performance computing capabilities. As a result, different hardware platforms have been utilized for acceleration purposes. Among these hardware-based accelerators, the field-programmable gate arrays (FPGAs) have gained a lot of attention due to their re-programmable characteristics, which provide customized control logic and computing operators. For example, FPGAs have recently been adopted for on-demand cloud services by the leading cloud providers like Amazon and Microsoft, providing acceleration for various compute-intensive tasks. While the co-residency of multiple tenants on a cloud FPGA chip increases the efficiency of resource utilization, it also creates unique attack surfaces that are under-explored. In this paper, we exploit the vulnerability associated with the shared power distribution network on cloud FPGAs. We present a stealthy power attack that can be remotely launched by a malicious tenant, shutting down the entire chip and resulting in denial-of-service for other co-located benign tenants. Specifically, we propose stealthy-shutdown: a well-timed power attack that can be implemented in two steps: (1) an attacker monitors the realtime FPGA power-consumption detected by ring-oscillator-based voltage sensors, and (2) when capturing high power-consuming moments, i.e., the power consumption by other tenants is above a certain threshold, she/he injects a well-timed power load to shut down the FPGA system. Note that in the proposed attack strategy, the power load injected by the attacker only accounts for a small portion of the overall power consumption; therefore, such attack strategy remains stealthy to the cloud FPGA operator. We successfully implement and validate the proposed attack on three FPGA evaluation kits with running real-world applications. The proposed attack results in a stealthy-shutdown, demonstrating severe security concerns of co-tenancy on cloud FPGAs. We also offer two countermeasures that can mitigate such power attacks.
Bradbury, Matthew, Maple, Carsten, Yuan, Hu, Atmaca, Ugur Ilker, Cannizzaro, Sara.  2020.  Identifying Attack Surfaces in the Evolving Space Industry Using Reference Architectures. 2020 IEEE Aerospace Conference. :1–20.
The space environment is currently undergoing a substantial change and many new entrants to the market are deploying devices, satellites and systems in space; this evolution has been termed as NewSpace. The change is complicated by technological developments such as deploying machine learning based autonomous space systems and the Internet of Space Things (IoST). In the IoST, space systems will rely on satellite-to-x communication and interactions with wider aspects of the ground segment to a greater degree than existing systems. Such developments will inevitably lead to a change in the cyber security threat landscape of space systems. Inevitably, there will be a greater number of attack vectors for adversaries to exploit, and previously infeasible threats can be realised, and thus require mitigation. In this paper, we present a reference architecture (RA) that can be used to abstractly model in situ applications of this new space landscape. The RA specifies high-level system components and their interactions. By instantiating the RA for two scenarios we demonstrate how to analyse the attack surface using attack trees.