Visible to the public Biblio

Found 12055 results

Conference Paper
Fan, Renshi, Du, Gaoming, Xu, Pengfei, Li, Zhenmin, Song, Yukun, Zhang, Duoli.  2019.  An Adaptive Routing Scheme Based on Q-learning and Real-time Traffic Monitoring for Network-on-Chip. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :244—248.
In the Network on Chip (NoC), performance optimization has always been a research focus. Compared with the static routing scheme, dynamical routing schemes can better reduce the data of packet transmission latency under network congestion. In this paper, we propose a dynamical Q-learning routing approach with real-time monitoring of NoC. Firstly, we design a real-time monitoring scheme and the corresponding circuits to record the status of traffic congestion for NoC. Secondly, we propose a novel method of Q-learning. This method finds an optimal path based on the lowest traffic congestion. Finally, we dynamically redistribute network tasks to increase the packet transmission speed and balance the traffic load. Compared with the C-XY routing and DyXY routing, our method achieved improvement in terms of 25.6%-49.5% and 22.9%-43.8%.
Salehie, Mazeiar, Pasquale, Liliana, Omoronyia, Inah, Nuseibeh, Bashar.  2012.  Adaptive Security and Privacy in Smart Grids: A Software Engineering Vision. 2012 First International Workshop on Software Engineering Challenges for the Smart Grid (SE-SmartGrids). :46–49.

Despite the benefits offered by smart grids, energy producers, distributors and consumers are increasingly concerned about possible security and privacy threats. These threats typically manifest themselves at runtime as new usage scenarios arise and vulnerabilities are discovered. Adaptive security and privacy promise to address these threats by increasing awareness and automating prevention, detection and recovery from security and privacy requirements' failures at runtime by re-configuring system controls and perhaps even changing requirements. This paper discusses the need for adaptive security and privacy in smart grids by presenting some motivating scenarios. We then outline some research issues that arise in engineering adaptive security. We particularly scrutinize published reports by NIST on smart grid security and privacy as the basis for our discussions.

Li, F., Jiang, M., Zhang, Z..  2017.  An adaptive sparse representation model by block dictionary and swarm intelligence. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA). :200–203.

The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients and also to improve the sparsity, we propose a new SR model called adaptive sparse representation classifier(ASRC). In ASRC, a sparse coefficient strengthened item is added in the objective function. The model is solved by the artificial bee colony (ABC) algorithm with variable step to speed up the convergence. Also, a partition strategy for large scale dictionary is adopted to lighten bee's load and removes the irrelevant groups. Through different data sets, we empirically demonstrate the property of the new model and its recognition performance.

Ollesch, Julius.  2016.  Adaptive Steering of Cyber-physical Systems with Atomic Complex Event Processing Services: Doctoral Symposium. Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems. :402–405.
Given the advent of cyber-physical systems (CPS), event-based control paradigms such as complex event processing (CEP) are vital enablers for adaptive analytical control mechanisms. CPS are becoming a high-profile research topic as they are key to disruptive digital innovations such as autonomous driving, industrial internet, smart grid and ambient assisted living. However, organizational and technological scalability of today's CEP approaches is limited by their monolithic architectures. This leads to the research idea for atomic CEP entities and the hypothesis that a network of small event-based control services is better suited for CPS development and operation than current centralised approaches. In addition, the paper summarizes preliminary results of the presented doctoral work and outlines questions for future research as well as an evaluation plan.
Kin-Cleaves, Christy, Ker, Andrew D..  2018.  Adaptive Steganography in the Noisy Channel with Dual-Syndrome Trellis Codes. 2018 IEEE International Workshop on Information Forensics and Security (WIFS). :1–7.
Adaptive steganography aims to reduce distortion in the embedding process, typically using Syndrome Trellis Codes (STCs). However, in the case of non-adversarial noise, these are a bad choice: syndrome codes are fragile by design, amplifying the channel error rate into unacceptably-high payload error rates. In this paper we examine the fragility of STCs in the noisy channel, and consider how this can be mitigated if their use cannot be avoided altogether. We also propose an extension called Dual-Syndrome Trellis Codes, that combines error correction and embedding in the same Viterbi process, which slightly outperforms a straight-forward combination of standard forward error correction and STCs.
Nasser, B., Rabani, A., Freiling, D., Gan, C..  2018.  An Adaptive Telerobotics Control for Advanced Manufacturing. 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS). :82—89.
This paper explores an innovative approach to the telerobotics reasoning architecture and networking, which offer a reliable and adaptable operational process for complex tasks. There are many operational challenges in the remote control for manufacturing that can be introduced by the network communications and Iatency. A new protocol, named compact Reliable UDP (compact-RUDP), has been developed to combine both data channelling and media streaming for robot teleoperation. The original approach ensures connection reliability by implementing a TCP-like sliding window with UDP packets. The protocol provides multiple features including data security, link status monitoring, bandwidth control, asynchronous file transfer and prioritizing transfer of data packets. Experiments were conducted on a 5DOF robotic arm where a cutting tool was mounted at its distal end. A light sensor was used to guide the robot movements, and a camera device to provide a video stream of the operation. The data communication reliability is evaluated using Round-Trip Time (RTT), and advanced robot path planning for distributed decision making between endpoints. The results show 88% correlation between the remotely and locally operated robots. The file transfers and video streaming were performed with no data loss or corruption on the control commands and data feedback packets.
Jialing Mo, Qiang He, Weiping Hu.  2014.  An adaptive threshold de-noising method based on EEMD. Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on. :209-214.

In view of the difficulty in selecting wavelet base and decomposition level for wavelet-based de-noising method, this paper proposes an adaptive de-noising method based on Ensemble Empirical Mode Decomposition (EEMD). The autocorrelation, cross-correlation method is used to adaptively find the signal-to-noise boundary layer of the EEMD in this method. Then the noise dominant layer is filtered directly and the signal dominant layer is threshold de-noised. Finally, the de-noising signal is reconstructed by each layer component which is de-noised. This method solves the problem of mode mixing in Empirical Mode Decomposition (EMD) by using EEMD and combines the advantage of wavelet threshold. In this paper, we focus on the analysis and verification of the correctness of the adaptive determination of the noise dominant layer. The simulation experiment results prove that this de-noising method is efficient and has good adaptability.

Chae, Younghun, Katenka, Natallia, DiPippo, Lisa.  2019.  An Adaptive Threshold Method for Anomaly-based Intrusion Detection Systems. 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA). :1–4.
Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme.
Yuan, Yaofeng, When, JieChang.  2019.  Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel. 2019 15th International Conference on Computational Intelligence and Security (CIS). :87–91.
In the deep learning tasks, we can design different network models to address different tasks (classification, detection, segmentation). But traditional deep learning networks simply increase the depth and breadth of the network. This leads to a higher complexity of the model. We propose Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel(SKENet). SKENet extract features from different kernels, then mixed those features by elementwise, lastly do sigmoid operator on channel features to get adaptive weightings. We did a simple classification test on the CIFAR10 amd CIFAR100 dataset. The results show that SKENet can achieve a better result in a shorter time. After that, we did an object detection experiment on the VOC dataset. The experimental results show that SKENet is far ahead of the SKNet[20] in terms of speed and accuracy.
Corneci, Vlad-Mihai, Carabas, Costin, Deaconescu, Razvan, Tapus, Nicolae.  2019.  Adding Custom Sandbox Profiles to iOS Apps. 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
The massive adoption of mobile devices by both individuals and companies is raising many security concerns. The fact that such devices are handling sensitive data makes them a target for attackers. Many attack prevention mechanisms are deployed with a last line of defense that focuses on the containment principle. Currently, iOS treats each 3rd party application alike which may lead to security flaws. We propose a framework in which each application has a custom sandboxed environment. We investigated the current confinement architecture used by Apple and built a solution on top of it.
Hayward, Jake, Tomlinson, Andrew, Bryans, Jeremy.  2019.  Adding Cyberattacks To An Industry-Leading CAN Simulator. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :9–16.
Recent years have seen an increase in the data usage in cars, particularly as they become more autonomous and connected. With the rise in data use have come concerns about automotive cyber-security. An in-vehicle network shown to be particularly vulnerable is the Controller Area Network (CAN), which is the communication bus used by the car's safety critical and performance critical components. Cyber attacks on the CAN have been demonstrated, leading to research to develop attack detection and attack prevention systems. Such research requires representative attack demonstrations and data for testing. Obtaining this data is problematical due to the expense, danger and impracticality of using real cars on roads or tracks for example attacks. Whilst CAN simulators are available, these tend to be configured for testing conformance and functionality, rather than analysing security and cyber vulnerability. We therefore adapt a leading, industry-standard, CAN simulator to incorporate a core set of cyber attacks that are representative of those proposed by other researchers. Our adaptation allows the user to configure the attacks, and can be added easily to the free version of the simulator. Here we describe the simulator and, after reviewing the attacks that have been demonstrated and discussing their commonalities, we outline the attacks that we have incorporated into the simulator.
Tojiboev, R., Lee, W., Lee, C. C..  2020.  Adding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :432—434.

Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they tend to protect by reducing data depends on a feature of each method. When a strong privacy protection is required, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment.

Giaretta, Alberto, De Donno, Michele, Dragoni, Nicola.  2018.  Adding Salt to Pepper: A Structured Security Assessment over a Humanoid Robot. Proceedings of the 13th International Conference on Availability, Reliability and Security. :22:1–22:8.
The rise of connectivity, digitalization, robotics, and artificial intelligence (AI) is rapidly changing our society and shaping its future development. During this technological and societal revolution, security has been persistently neglected, yet a hacked robot can act as an insider threat in organizations, industries, public spaces, and private homes. In this paper, we perform a structured security assessment of Pepper, a commercial humanoid robot. Our analysis, composed by an automated and a manual part, points out a relevant number of security flaws that can be used to take over and command the robot. Furthermore, we suggest how these issues could be fixed, thus, avoided in the future. The very final aim of this work is to push the rise of the security level of IoT products before they are sold on the public market.
Miller, J. A., Peng, H., Cotterell, M. E..  2017.  Adding Support for Theory in Open Science Big Data. 2017 IEEE World Congress on Services (SERVICES). :71–75.

Open Science Big Data is emerging as an important area of research and software development. Although there are several high quality frameworks for Big Data, additional capabilities are needed for Open Science Big Data. These include data provenance, citable reusable data, data sources providing links to research literature, relationships to other data and theories, transparent analysis/reproducibility, data privacy, new optimizations/advanced algorithms, data curation, data storage and transfer. An important part of science is explanation of results, ideally leading to theory formation. In this paper, we examine means for supporting the use of theory in big data analytics as well as using big data to assist in theory formation. One approach is to fit data in a way that is compatible with some theory, existing or new. Functional Data Analysis allows precise fitting of data as well as penalties for lack of smoothness or even departure from theoretical expectations. This paper discusses principal differential analysis and related techniques for fitting data where, for example, a time-based process is governed by an ordinary differential equation. Automation in theory formation is also considered. Case studies in the fields of computational economics and finance are considered.

Chen, Xi, Oliveira, Igor C., Servedio, Rocco A..  2017.  Addition is Exponentially Harder Than Counting for Shallow Monotone Circuits. Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. :1232–1245.
Let Addk,N denote the Boolean function which takes as input k strings of N bits each, representing k numbers a(1),…,a(k) in \0,1,…,2N−1\, and outputs 1 if and only if a(1) + ⋯ + a(k) ≥ 2N. Let MAJt,n denote a monotone unweighted threshold gate, i.e., the Boolean function which takes as input a single string x ∈ \0,1\n and outputs 1 if and only if x1 + ⋯ + xn ≥ t. The function Addk,N may be viewed as a monotone function that performs addition, and MAJt,n may be viewed as a monotone gate that performs counting. We refer to circuits that are composed of MAJ gates as monotone majority circuits. The main result of this paper is an exponential lower bound on the size of bounded-depth monotone majority circuits that compute Addk,N. More precisely, we show that for any constant d ≥ 2, any depth-d monotone majority circuit that computes Addd,N must have size 2Ω(N1/d). As Addk,N can be computed by a single monotone weighted threshold gate (that uses exponentially large weights), our lower bound implies that constant-depth monotone majority circuits require exponential size to simulate monotone weighted threshold gates. This answers a question posed by Goldmann and Karpinski (STOC’93) and recently restated by Håstad (2010, 2014). We also show that our lower bound is essentially best possible, by constructing a depth-d, size 2O(N1/d) monotone majority circuit for Addd,N. As a corollary of our lower bound, we significantly strengthen a classical theorem in circuit complexity due to Ajtai and Gurevich (JACM’87). They exhibited a monotone function that is in AC0 but requires super-polynomial size for any constant-depth monotone circuit composed of unbounded fan-in AND and OR gates. We describe a monotone function that is in depth-3 AC0 but requires exponential size monotone circuits of any constant depth, even if the circuits are composed of MAJ gates.
Huang, K., Yang, T..  2020.  Additive and Subtractive Cuckoo Filters. 2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS). :1–10.
Bloom filters (BFs) are fast and space-efficient data structures used for set membership queries in many applications. BFs are required to satisfy three key requirements: low space cost, high-speed lookups, and fast updates. Prior works do not satisfy these requirements at the same time. The standard BF does not support deletions of items and the variants that support deletions need additional space or performance overhead. The state-of-the-art cuckoo filters (CF) has high performance with seemingly low space cost. However, the CF suffers a critical issue of varying space cost per item. This is because the exclusive-OR (XOR) operation used by the CF requires the total number of buckets to be a power of two, leading to the space inflation. To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.
Zhang, Xiaoqiang, Wang, Xuesong, Wang, Qingming.  2018.  Additive Spread Spectrum Image Hiding Algorithm Based on Host Signal. Proceedings of the 2018 7th International Conference on Software and Computer Applications. :164-168.

Image hiding is the important tools to protect the ownership rights of digital multimedia contents. To reduce the interference effect of the host signal in the popular Spread Spectrum (SS) image hiding algorithm, this paper proposes an Improved Additive Spread Spectrum (IASS) image hiding algorithm. The proposed IASS image hiding algorithm maintains the simple decoder of the Additive Spread Spectrum (ASS) image hiding algorithm. This paper makes the comparative experiments with the ASS image hiding algorithm and Correlation-and-bit-Aware Spread Spectrum (CASS) image hiding algorithm. For the noise-free scenario, the proposed IASS image hiding algorithm could yield error-free decoding performance in theory. For the noise scenario, the experimental results show that the proposed IASS image hiding algorithm could significantly reduce the host effect in data hiding and improve the watermark decoding performance remarkably.

Hong, Tang, Ju, Tailiang, Li, Yao.  2020.  Address Collision Attacks on ECSM Protected by ADPA. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :235—239.

Using the physical characteristics of the encryption device, an attacker can more easily obtain the key, which is called side-channel attack. Common side-channel attacks, such as simple power analysis (SPA) and differential power analysis (DPA), mainly focus on the statistical analysis of the data involved in the encryption algorithm, while there are relatively few studies on the Hamming weight of the addresses. Therefore, a new method of address-based Hamming weight analysis, address collision attack, is proposed in this research. The collision attack method (CA) and support vector machines algorithm (SVM) are used for analysis, meanwhile, the scalar multiplication implemented by protected address-bit DPA (ADPA) can be attack on the ChipWhisperer-Pro CW1200.

Apruzzese, G., Colajanni, M., Ferretti, L., Marchetti, M..  2019.  Addressing Adversarial Attacks Against Security Systems Based on Machine Learning. 2019 11th International Conference on Cyber Conflict (CyCon). 900:1—18.

Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.

Vaughn, Jr., Rayford B., Morris, Tommy.  2016.  Addressing Critical Industrial Control System Cyber Security Concerns via High Fidelity Simulation. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :12:1–12:4.

This paper outlines a set of 10 cyber security concerns associated with Industrial Control Systems (ICS). The concerns address software and hardware development, implementation, and maintenance practices, supply chain assurance, the need for cyber forensics in ICS, a lack of awareness and training, and finally, a need for test beds which can be used to address the first 9 cited concerns. The concerns documented in this paper were developed based on the authors' combined experience conducting research in this field for the US Department of Homeland Security, the National Science Foundation, and the Department of Defense. The second half of this paper documents a virtual test bed platform which is offered as a tool to address the concerns listed in the first half of the paper. The paper discusses various types of test beds proposed in literature for ICS research, provides an overview of the virtual test bed platform developed by the authors, and lists future works required to extend the existing test beds to serve as a development platform.

Peterson, Brad, Humphrey, Alan, Schmidt, John, Berzins, Martin.  2017.  Addressing Global Data Dependencies in Heterogeneous Asynchronous Runtime Systems on GPUs. Proceedings of the Third International Workshop on Extreme Scale Programming Models and Middleware. :1:1–1:8.
Large-scale parallel applications with complex global data dependencies beyond those of reductions pose significant scalability challenges in an asynchronous runtime system. Internodal challenges include identifying the all-to-all communication of data dependencies among the nodes. Intranodal challenges include gathering together these data dependencies into usable data objects while avoiding data duplication. This paper addresses these challenges within the context of a large-scale, industrial coal boiler simulation using the Uintah asynchronous many-task runtime system on GPU architectures. We show significant reduction in time spent analyzing data dependencies through refinements in our dependency search algorithm. Multiple task graphs are used to eliminate subsequent analysis when task graphs change in predictable and repeatable ways. Using a combined data store and task scheduler redesign reduces data dependency duplication ensuring that problems fit within host and GPU memory. These modifications did not require any changes to application code or sweeping changes to the Uintah runtime system. We report results running on the DOE Titan system on 119K CPU cores and 7.5K GPUs simultaneously. Our solutions can be generalized to other task dependency problems with global dependencies among thousands of nodes which must be processed efficiently at large scale.
Yilmaz, I., Masum, R., Siraj, A..  2020.  Addressing Imbalanced Data Problem with Generative Adversarial Network For Intrusion Detection. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :25–30.

Machine learning techniques help to understand underlying patterns in datasets to develop defense mechanisms against cyber attacks. Multilayer Perceptron (MLP) technique is a machine learning technique used in detecting attack vs. benign data. However, it is difficult to construct any effective model when there are imbalances in the dataset that prevent proper classification of attack samples in data. In this research, we use UGR'16 dataset to conduct data wrangling initially. This technique helps to prepare a test set from the original dataset to train the neural network model effectively. We experimented with a series of inputs of varying sizes (i.e. 10000, 50000, 1 million) to observe the performance of the MLP neural network model with distribution of features over accuracy. Later, we use Generative Adversarial Network (GAN) model that produces samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan) for balancing the dataset. These samples are generated based on data from the UGR'16 dataset. Further experiments with MLP neural network model shows that a balanced attack sample dataset, made possible with GAN, produces more accurate results than an imbalanced one.

Langfinger, M., Schneider, M., Stricker, D., Schotten, H. D..  2017.  Addressing Security Challenges in Industrial Augmented Reality Systems. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). :299–304.

In context of Industry 4.0 Augmented Reality (AR) is frequently mentioned as the upcoming interface technology for human-machine communication and collaboration. Many prototypes have already arisen in both the consumer market and in the industrial sector. According to numerous experts it will take only few years until AR will reach the maturity level to be deployed in productive applications. Especially for industrial usage it is required to assess security risks and challenges this new technology implicates. Thereby we focus on plant operators, Original Equipment Manufacturers (OEMs) and component vendors as stakeholders. Starting from several industrial AR use cases and the structure of contemporary AR applications, in this paper we identify security assets worthy of protection and derive the corresponding security goals. Afterwards we elaborate the threats industrial AR applications are exposed to and develop an edge computing architecture for future AR applications which encompasses various measures to reduce security risks for our stakeholders.

Laranjeiro, Nuno, Gomez, Camilo, Schiavone, Enrico, Montecchi, Leonardo, Carvalho, Manoel J. M., Lollini, Paolo, Micskei, Zoltán.  2019.  Addressing Verification and Validation Challenges in Future Cyber-Physical Systems. 2019 9th Latin-American Symposium on Dependable Computing (LADC). :1–2.
Cyber-physical systems are characterized by strong interactions between their physical and computation parts. The increasing complexity of such systems, now used in numerous application domains (e.g., aeronautics, healthcare), in conjunction with hard to predict surrounding environments or the use of non-traditional middleware and with the presence of non-deterministic or non-explainable software outputs, tend to make traditional Verification and Validation (V&V) techniques ineffective. This paper presents the H2020 ADVANCE project, which aims precisely at addressing the Verification and Validation challenges that the next-generation of cyber-physical systems bring, by exploring techniques, methods and tools for achieving the technical objective of improving the overall efficiency and effectiveness of the V&V process. From a strategic perspective, the goal of the project is to create an international network of expertise on the topic of V&V of cyber-physical systems.
Hänel, T., Bothe, A., Helmke, R., Gericke, C., Aschenbruck, N..  2017.  Adjustable security for RFID-equipped IoT devices. 2017 IEEE International Conference on RFID Technology Application (RFID-TA). :208–213.

Over the last years, the number of rather simple interconnected devices in nonindustrial scenarios (e.g., for home automation) has steadily increased. For ease of use, the overall system security is often neglected. Before the Internet of Things (IoT) reaches the same distribution rate and impact in industrial applications, where security is crucial for success, solutions that combine usability, scalability, and security are required. We develop such a security system, mainly targeting sensor modules equipped with Radio Frequency IDentification (RFID) tags which we leverage to increase the security level. More specifically, we consider a network based on Message Queue Telemetry Transport (MQTT) which is a widely adopted protocol for the IoT.