Visible to the public Biblio

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Kriaa, S., Papillon, S., Jagadeesan, L., Mendiratta, V..  2020.  Better Safe than Sorry: Modeling Reliability and Security in Replicated SDN Controllers. 2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020. :1—6.
Software-defined networks (SDN), through their programmability, significantly increase network resilience by enabling dynamic reconfiguration of network topologies in response to faults and potentially malicious attacks detected in real-time. Another key trend in network softwarization is cloud-native software, which, together with SDN, will be an integral part of the core of future 5G networks. In SDN, the control plane forms the "brain" of the software-defined network and is typically implemented as a set of distributed controller replicas to avoid a single point of failure. Distributed consensus algorithms are used to ensure agreement among the replicas on key data even in the presence of faults. Security is also a critical concern in ensuring that attackers cannot compromise the SDN control plane; byzantine fault tolerance algorithms can provide protection against compromised controller replicas. However, while reliability/availability and security form key attributes of resilience, they are typically modeled separately in SDN, without consideration of the potential impacts of their interaction. In this paper we present an initial framework for a model that unifies reliability, availability, and security considerations in distributed consensus. We examine – via simulation of our model – some impacts of the interaction between accidental faults and malicious attacks on SDN and suggest potential mitigations unique to cloud-native software.
Nweke, L. O., Weldehawaryat, G. Kahsay, Wolthusen, S. D..  2020.  Adversary Model for Attacks Against IEC 61850 Real-Time Communication Protocols. 2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020. :1—8.

Adversarial models are well-established for cryptographic protocols, but distributed real-time protocols have requirements that these abstractions are not intended to cover. The IEEE/IEC 61850 standard for communication networks and systems for power utility automation in particular not only requires distributed processing, but in case of the generic object oriented substation events and sampled value (GOOSE/SV) protocols also hard real-time characteristics. This motivates the desire to include both quality of service (QoS) and explicit network topology in an adversary model based on a π-calculus process algebraic formalism based on earlier work. This allows reasoning over process states, placement of adversarial entities and communication behaviour. We demonstrate the use of our model for the simple case of a replay attack against the publish/subscribe GOOSE/SV subprotocol, showing bounds for non-detectability of such an attack.

Sultana, K. Z., Williams, B. J., Bosu, A..  2018.  A Comparison of Nano-Patterns vs. Software Metrics in Vulnerability Prediction. 2018 25th Asia-Pacific Software Engineering Conference (APSEC). :355—364.

Context: Software security is an imperative aspect of software quality. Early detection of vulnerable code during development can better ensure the security of the codebase and minimize testing efforts. Although traditional software metrics are used for early detection of vulnerabilities, they do not clearly address the granularity level of the issue to precisely pinpoint vulnerabilities. The goal of this study is to employ method-level traceable patterns (nano-patterns) in vulnerability prediction and empirically compare their performance with traditional software metrics. The concept of nano-patterns is similar to design patterns, but these constructs can be automatically recognized and extracted from source code. If nano-patterns can better predict vulnerable methods compared to software metrics, they can be used in developing vulnerability prediction models with better accuracy. Aims: This study explores the performance of method-level patterns in vulnerability prediction. We also compare them with method-level software metrics. Method: We studied vulnerabilities reported for two major releases of Apache Tomcat (6 and 7), Apache CXF, and two stand-alone Java web applications. We used three machine learning techniques to predict vulnerabilities using nano-patterns as features. We applied the same techniques using method-level software metrics as features and compared their performance with nano-patterns. Results: We found that nano-patterns show lower false negative rates for classifying vulnerable methods (for Tomcat 6, 21% vs 34.7%) and therefore, have higher recall in predicting vulnerable code than the software metrics used. On the other hand, software metrics show higher precision than nano-patterns (79.4% vs 76.6%). Conclusion: In summary, we suggest developers use nano-patterns as features for vulnerability prediction to augment existing approaches as these code constructs outperform standard metrics in terms of prediction recall.

Al-Far, A., Qusef, A., Almajali, S..  2018.  Measuring Impact Score on Confidentiality, Integrity, and Availability Using Code Metrics. 2018 International Arab Conference on Information Technology (ACIT). :1—9.

Confidentiality, Integrity, and Availability are principal keys to build any secure software. Considering the security principles during the different software development phases would reduce software vulnerabilities. This paper measures the impact of the different software quality metrics on Confidentiality, Integrity, or Availability for any given object-oriented PHP application, which has a list of reported vulnerabilities. The National Vulnerability Database was used to provide the impact score on confidentiality, integrity, and availability for the reported vulnerabilities on the selected applications. This paper includes a study for these scores and its correlation with 25 code metrics for the given vulnerable source code. The achieved results were able to correlate 23.7% of the variability in `Integrity' to four metrics: Vocabulary Used in Code, Card and Agresti, Intelligent Content, and Efferent Coupling metrics. The Length (Halstead metric) could alone predict about 24.2 % of the observed variability in ` Availability'. The results indicate no significant correlation of `Confidentiality' with the tested code metrics.

Ong, Desmond, Soh, Harold, Zaki, Jamil, Goodman, Noah.  2019.  Applying Probabilistic Programming to Affective Computing. IEEE Transactions on Affective Computing. :1—1.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach

Roth, Thomas, Burns, Martin.  2018.  A gateway to easily integrate simulation platforms for co-simulation of cyber-physical systems. 2018 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES). :1—6.

Cyber-physical systems (CPS) research leverages the expertise of researchers from multiple domains to engineer complex systems of interacting physical and computational components. An approach called co-simulation is often used in CPS conceptual design to integrate the specialized tools and simulators from each of these domains into a joint simulation for the evaluation of design decisions. Many co-simulation platforms are being developed to expedite CPS conceptualization and realization, but most use intrusive modeling and communication libraries that require researchers to either abandon their existing models or spend considerable effort to integrate them into the platform. A significant number of these co-simulation platforms use the High Level Architecture (HLA) standard that provides a rich set of services to facilitate distributed simulation. This paper introduces a simple gateway that can be readily implemented without co-simulation expertise to adapt existing models and research infrastructure for use in HLA. An open-source implementation of the gateway has been developed for the National Institute of Standards and Technology (NIST) co-simulation platform called the Universal CPS Environment for Federation (UCEF).

Hansch, Gerhard, Schneider, Peter, Fischer, Kai, Böttinger, Konstantin.  2019.  A Unified Architecture for Industrial IoT Security Requirements in Open Platform Communications. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). :325—332.

We present a unified communication architecture for security requirements in the industrial internet of things. Formulating security requirements in the language of OPC UA provides a unified method to communicate and compare security requirements within a heavily heterogeneous landscape of machines in the field. Our machine-readable data model provides a fully automatable approach for security requirement communication within the rapidly evolving fourth industrial revolution, which is characterized by high-grade interconnection of industrial infrastructures and self-configuring production systems. Capturing security requirements in an OPC UA compliant and unified data model for industrial control systems enables strong use cases within modern production plants and future supply chains. We implement our data model as well as an OPC UA server that operates on this model to show the feasibility of our approach. Further, we deploy and evaluate our framework within a reference project realized by 14 industrial partners and 7 research facilities within Germany.

Chen, Lin, William Atwood, J..  2018.  Performance Evaluation for Secure Internet Group Management Protocol and Group Security Association Management Protocol. 2018 IEEE Canadian Conference on Electrical Computer Engineering (CCECE). :1—5.

Multicast distribution employs the model of many-to-many so that it is a more efficient way of data delivery compared to traditional one-to-one unicast distribution, which can benefit many applications such as media streaming. However, the lack of security features in its nature makes multicast technology much less popular in an open environment such as the Internet. Internet Service Providers (ISPs) take advantage of IP multicast technology's high efficiency of data delivery to provide Internet Protocol Television (IPTV) to their users. But without the full control on their networks, ISPs cannot collect revenue for the services they provide. Secure Internet Group Management Protocol (SIGMP), an extension of Internet Group Management Protocol (IGMP), and Group Security Association Management Protocol (GSAM), have been proposed to enforce receiver access control at the network level of IP multicast. In this paper, we analyze operational details and issues of both SIGMP and GSAM. An examination of the performance of both protocols is also conducted.

Zhang, Lichen.  2018.  Modeling Cloud Based Cyber Physical Systems Based on AADL. 2018 24th International Conference on Automation and Computing (ICAC). :1—6.

Cloud-based cyber-physical systems, like vehicle and intelligent transportation systems, are now attracting much more attentions. These systems usually include large-scale distributed sensor networks covering various components and producing enormous measurement data. Lots of modeling languages are put to use for describing cyber-physical systems or its aspects, bringing contribution to the development of cyber-physical systems. But most of the modeling techniques only focuse on software aspect so that they could not exactly express the whole cloud-based cyber-physical systems, which require appropriate views and tools in its design; but those tools are hard to be used under systemic or object-oriented methods. For example, the widest used modeling language, UML, could not fulfil the above design's requirements by using the foremer's standard form. This paper presents a method designing the cloud-based cyber-physical systems with AADL, by which we can analyse, model and apply those requirements on cloud platforms ensuring QoS in a relatively highly extensible way at the mean time.

Zhang, Lei, Zhang, Jianqing, Chen, Yong, Liao, Shaowen.  2018.  Research on the Simulation Algorithm of Object-Oriented Language. 2018 3rd International Conference on Smart City and Systems Engineering (ICSCSE). :902—904.

Security model is an important subject in the field of low energy independence complexity theory. It takes security strategy as the core, changes the system from static protection to dynamic protection, and provides the basis for the rapid response of the system. A large number of empirical studies have been conducted to verify the cache consistency. The development of object oriented language is pure object oriented language, and the other is mixed object oriented language, that is, adding class, inheritance and other elements in process language and other languages. This paper studies a new object-oriented language application, namely GUT for a write-back cache, which is based on the study of simulation algorithm to solve all these challenges in the field of low energy independence complexity theory.

Liu, Yingying, Wang, Yiwei.  2019.  A Robust Malware Detection System Using Deep Learning on API Calls. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1456–1460.
With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.
Goli, Mehran, Drechsler, Rolf.  2019.  Scalable Simulation-Based Verification of SystemC-Based Virtual Prototypes. 2019 22nd Euromicro Conference on Digital System Design (DSD). :522–529.
Virtual Prototypes (VPs) at the Electronic System Level (ESL) written in SystemC language using its Transaction Level Modeling (TLM) framework are increasingly adopted by the semiconductor industry. The main reason is that VPs are much earlier available, and their simulation is orders of magnitude faster in comparison to the hardware models implemented at lower levels of abstraction (e.g. RTL). This leads designers to use VPs as reference models for an early design verification. Hence, the correctness assurance of these reference models (VPs) is critical as undetected faults may propagate to less abstract levels in the design process, increasing the fixing cost and effort. In this paper, we propose a novel simulation-based verification approach to automatically validate the simulation behavior of a given SystemC VP against both the TLM-2.0 rules and its specifications (i.e. functional and timing behavior of communications in the VP). The scalability and the efficiency of the proposed approach are demonstrated using an extensive set of experiments including a real-word VP.
Tun, Hein, Lupin, Sergey, Than, Ba Hla, Nay Zaw Linn, Kyaw, Khaing, Min Thu.  2019.  Estimation of Information System Security Using Hybrid Simulation in AnyLogic. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1829–1834.
Nowadays the role of Information systems in our life has greatly increased, which has become one of the biggest challenges for citizens, organizations and governments. Every single day we are becoming more and more dependent on information and communication technology (ICT). A major goal of information security is to find the best ways to mitigate the risks. The context-role and perimeter protection approaches can reduce and prevent an unauthorized penetration to protected zones and information systems inside the zones. The result of this work can be useful for the security system analysis and optimization of their organizations.
Pahl, Marc-Oliver, Liebald, Stefan.  2019.  Information-Centric IoT Middleware Overlay: VSL. 2019 International Conference on Networked Systems (NetSys). :1–8.
The heart of the Internet of Things (IoT) is data. IoT services processes data from sensors that interface their physical surroundings, and from other software such as Internet weather databases. They produce data to control physical environments via actuators, and offer data to other services. More recently, service-centric designs for managing the IoT have been proposed. Data-centric or name-based communication architectures complement these developments very well. Especially for edge-based or site-local installations, data-centric Internet architectures can be implemented already today, as they do not require any changes at the core. We present the Virtual State Layer (VSL), a site-local data-centric architecture for the IoT. Special features of our solution are full separation of logic and data in IoT services, offering the data-centric VSL interface directly to developers, which significantly reduces the overall system complexity, explicit data modeling, a semantically-rich data item lookup, stream connections between services, and security-by-design. We evaluate our solution regarding usability, performance, scalability, resilience, energy efficiency, and security.
Kulik, T., Tran-Jørgensen, P. W. V., Boudjadar, J., Schultz, C..  2018.  A Framework for Threat-Driven Cyber Security Verification of IoT Systems. 2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). :89-97.

Industrial control systems are changing from monolithic to distributed and interconnected architectures, entering the era of industrial IoT. One fundamental issue is that security properties of such distributed control systems are typically only verified empirically, during development and after system deployment. We propose a novel modelling framework for the security verification of distributed industrial control systems, with the goal of moving towards early design stage formal verification. In our framework we model industrial IoT infrastructures, attack patterns, and mitigation strategies for countering attacks. We conduct model checking-based formal analysis of system security through scenario execution, where the analysed system is exposed to attacks and implement mitigation strategies. We study the applicability of our framework for large systems using a scalability analysis.

Volz, V., Majchrzak, K., Preuss, M..  2018.  A Social Science-based Approach to Explanations for (Game) AI. 2018 IEEE Conference on Computational Intelligence and Games (CIG). :1–2.

The current AI revolution provides us with many new, but often very complex algorithmic systems. This complexity does not only limit understanding, but also acceptance of e.g. deep learning methods. In recent years, explainable AI (XAI) has been proposed as a remedy. However, this research is rarely supported by publications on explanations from social sciences. We suggest a bottom-up approach to explanations for (game) AI, by starting from a baseline definition of understandability informed by the concept of limited human working memory. We detail our approach and demonstrate its application to two games from the GVGAI framework. Finally, we discuss our vision of how additional concepts from social sciences can be integrated into our proposed approach and how the results can be generalised.

Lokananta, F., Hartono, D., Tang, C. M..  2017.  A Scalable and Reconfigurable Verification and Benchmark Environment for Network on Chip Architecture. 2017 4th International Conference on New Media Studies (CONMEDIA). :6–10.

To reduce the complex communication problem that arise as the number of on-chip component increases, the use of Network-on-Chip (NoC) as interconnection architectures have become more promising to solve complex on-chip communication problems. However, providing a suitable test base to measure and verify functionality of any NoC is a compulsory. Universal Verification Methodology (UVM) is introduced as a standardized and reusable methodology for verifying integrated circuit design. In this research, a scalable and reconfigurable verification and benchmark environment for NoC is proposed.

MüUller, W., Kuwertz, A., Mühlenberg, D., Sander, J..  2017.  Semantic Information Fusion to Enhance Situational Awareness in Surveillance Scenarios. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). :397–402.

In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

Birnstill, P., Haas, C., Hassler, D., Beyerer, J..  2017.  Introducing Remote Attestation and Hardware-Based Cryptography to OPC UA. 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). :1–8.

In this paper we investigate whether and how hardware-based roots of trust, namely Trusted Platform Modules (TPMs) can improve the security of the communication protocol OPC UA (Open Platform Communications Unified Architecture) under reasonable assumptions, i.e. the Dolev-Yao attacker model. Our analysis shows that TPMs may serve for generating (RNG) and securely storing cryptographic keys, as cryptocoprocessors for weak systems, as well as for remote attestation. We propose to include these TPM functions into OPC UA via so-called ConformanceUnits, which can serve as building blocks of profiles that are used by clients and servers for negotiating the parameters of a session. Eventually, we present first results regarding the performance of a client-server communication including an additional OPC UA server providing remote attestation of other OPC UA servers.

Roberts, C. J., Morgenstern, M., Israel, D. J., Borky, J. M., Bradley, T. H..  2017.  Preliminary results from a model-driven architecture methodology for development of an event-driven space communications service concept. 2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE). :122–127.

NASA's next-generation space communications network will involve dynamic and autonomous services analogous to services provided by current terrestrial wireless networks. This architecture concept, known as the Space Mobile Network (SMN), is enabled by several technologies now in development. A pillar of the SMN architecture is the establishment and utilization of a continuous bidirectional control plane space link channel and a new User Initiated Service (UIS) protocol to enable more dynamic and autonomous mission operations concepts, reduced user space communications planning burden, and more efficient and effective provider network resource utilization. This paper provides preliminary results from the application of model-driven architecture methodology to develop UIS. Such an approach is necessary to ensure systematic investigation of several open questions concerning the efficiency, robustness, interoperability, scalability and security of the control plane space link and UIS protocol.

Boucher, A., Badri, M..  2017.  Predicting Fault-Prone Classes in Object-Oriented Software: An Adaptation of an Unsupervised Hybrid SOM Algorithm. 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). :306–317.

Many fault-proneness prediction models have been proposed in literature to identify fault-prone code in software systems. Most of the approaches use fault data history and supervised learning algorithms to build these models. However, since fault data history is not always available, some approaches also suggest using semi-supervised or unsupervised fault-proneness prediction models. The HySOM model, proposed in literature, uses function-level source code metrics to predict fault-prone functions in software systems, without using any fault data. In this paper, we adapt the HySOM approach for object-oriented software systems to predict fault-prone code at class-level granularity using object-oriented source code metrics. This adaptation makes it easier to prioritize the efforts of the testing team as unit tests are often written for classes in object-oriented software systems, and not for methods. Our adaptation also generalizes one main element of the HySOM model, which is the calculation of the source code metrics threshold values. We conducted an empirical study using 12 public datasets. Results show that the adaptation of the HySOM model for class-level fault-proneness prediction improves the consistency and the performance of the model. We additionally compared the performance of the adapted model to supervised approaches based on the Naive Bayes Network, ANN and Random Forest algorithms.

Sultana, K. Z., Williams, B. J..  2017.  Evaluating micro patterns and software metrics in vulnerability prediction. 2017 6th International Workshop on Software Mining (SoftwareMining). :40–47.

Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level. The goal of this study is to help developers evaluate software security using class-level traceable patterns called micro patterns to reduce security risks. The concept of micro patterns is similar to design patterns, but they can be automatically recognized and mined from source code. If micro patterns can better predict vulnerable classes compared to traditional software metrics, they can be used in developing a vulnerability prediction model. This study explores the performance of class-level patterns in vulnerability prediction and compares them with traditional class-level software metrics. We studied security vulnerabilities as reported for one major release of Apache Tomcat, Apache Camel and three stand-alone Java web applications. We used machine learning techniques for predicting vulnerabilities using micro patterns and class-level metrics as features. We found that micro patterns have higher recall in detecting vulnerable classes than the software metrics.

L. Rivière, J. Bringer, T. H. Le, H. Chabanne.  2015.  "A novel simulation approach for fault injection resistance evaluation on smart cards". 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). :1-8.

Physical perturbations are performed against embedded systems that can contain valuable data. Such devices and in particular smart cards are targeted because potential attackers hold them. The embedded system security must hold against intentional hardware failures that can result in software errors. In a malicious purpose, an attacker could exploit such errors to find out secret data or disrupt a transaction. Simulation techniques help to point out fault injection vulnerabilities and come at an early stage in the development process. This paper proposes a generic fault injection simulation tool that has the particularity to embed the injection mechanism into the smart card source code. By its embedded nature, the Embedded Fault Simulator (EFS) allows us to perform fault injection simulations and side-channel analyses simultaneously. It makes it possible to achieve combined attacks, multiple fault attacks and to perform backward analyses. We appraise our approach on real, modern and complex smart card systems under data and control flow fault models. We illustrate the EFS capacities by performing a practical combined attack on an Advanced Encryption Standard (AES) implementation.

Xianguo Zhang, Tiejun Huang, Yonghong Tian, Wen Gao.  2014.  Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding. Image Processing, IEEE Transactions on. 23:769-784.

The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.