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Sandberg, Henrik.  2018.  Control Theory for Practical Cyber-Physical Security: Extended Abstract. Proceedings of the 4th ACM Workshop on Cyber-Physical System Security. :25–26.

In this talk, we discuss how control theory can contribute to the analysis and design of secure cyber-physical systems. We start by reviewing conditions for undetectable false-data injection attacks on feedback control systems. In particular, we highlight how a physical understanding of the controlled process can guide us in the allocation of protective measures. We show that protecting only a few carefully selected actuators or sensors can give indirect protection to many more components. We then illustrate how such analysis is exploited in the design of a resilient control scheme for a microgrid energy management system.

Sanders, Matthew W., Yue, Chuan.  2018.  Minimizing Privilege Assignment Errors in Cloud Services. Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy. :2–12.

The Principle of Least Privilege is a security objective of granting users only those accesses they need to perform their duties. Creating least privilege policies in the cloud environment with many diverse services, each with unique privilege sets, is significantly more challenging than policy creation previously studied in other environments. Such security policies are always imperfect and must balance between the security risk of granting over-privilege and the effort to correct for under-privilege. In this paper, we formally define the problem of balancing between over-privilege and under-privilege as the Privilege Error Minimization Problem (PEMP) and present a method for quantitatively scoring security policies. We design and compare three algorithms for automatically generating policies: a naive algorithm, an unsupervised learning algorithm, and a supervised learning algorithm. We present the results of evaluating these three policy generation algorithms on a real-world dataset consisting of 5.2 million Amazon Web Service (AWS) audit log entries. The application of these methods can help create policies that balance between an organization's acceptable level of risk and effort to correct under-privilege.

Sandic-Stankovic, D., Kukolj, D., Callet, P. Le.  2015.  DIBR synthesized image quality assessment based on morphological pyramids. 2015 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON). :1–4.

Most Depth Image Based Rendering (DIBR) techniques produce synthesized images which contain non-uniform geometric distortions affecting edges coherency. This type of distortions are challenging for common image quality metrics. Morphological filters maintain important geometric information such as edges across different resolution levels. There is inherent congruence between the morphological pyramid decomposition scheme and human visual perception. In this paper, multi-scale measure, morphological pyramid peak signal-to-noise ratio MP-PSNR, based on morphological pyramid decomposition is proposed for the evaluation of DIBR synthesized images. It is shown that MPPSNR achieves much higher correlation with human judgment compared to the state-of-the-art image quality measures in this context.

Sandic-Stankovic, D., Kukolj, D., Callet, P. Le.  2015.  DIBR synthesized image quality assessment based on morphological wavelets. 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX). :1–6.

Most of the Depth Image Based Rendering (DIBR) techniques produce synthesized images which contain nonuniform geometric distortions affecting edges coherency. This type of distortions are challenging for common image quality metrics. Morphological filters maintain important geometric information such as edges across different resolution levels. In this paper, morphological wavelet peak signal-to-noise ratio measure, MW-PSNR, based on morphological wavelet decomposition is proposed to tackle the evaluation of DIBR synthesized images. It is shown that MW-PSNR achieves much higher correlation with human judgment compared to the state-of-the-art image quality measures in this context.

Sandifort, Maguell L. T. L., Liu, Jianquan, Nishimura, Shoji, Hürst, Wolfgang.  2018.  An Entropy Model for Loiterer Retrieval Across Multiple Surveillance Cameras. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. :309–317.

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates.

Sandifort, Maguell L.T.L., Liu, Jianquan, Nishimura, Shoji, Hürst, Wolfgang.  2018.  VisLoiter+: An Entropy Model-Based Loiterer Retrieval System with User-Friendly Interfaces. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. :505–508.

It is very difficult to fully automate the detection of loitering behavior in video surveillance, therefore humans are often required for monitoring. Alternatively, we could provide a list of potential loiterer candidates for a final yes/no judgment of a human operator. Our system, VisLoiter+, realizes this idea with a unique, user-friendly interface and by employing an entropy model for improved loitering analysis. Rather than using only frequency of appearance, we expand the loiter analysis with new methods measuring the amount of person movements across multiple camera views. The interface gives an overview of loiterer candidates to show their behavior at a glance, complemented by a lightweight video playback for further details about why a candidate was selected. We demonstrate that our system outperforms state-of-the-art solutions using real-life data sets.

Sándor, H., Genge, B., Szántó, Z..  2017.  Sensor data validation and abnormal behavior detection in the Internet of Things. 2017 16th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
Internet of Things (IoT) and its various application domains are radically changing the lives of people, providing smart services which will ultimately constitute integral components of the living environment. The services of IoT operate based on the data flows collected from the different sensors and actuators. In this respect, the correctness and security of the sensor data transported over the IoT system is a crucial factor in ensuring the correct functioning of the IoT services. In this work, we present a method that can detect abnormal sensor events based on “apriori” knowledge of the behavior of the monitored process. The main advantage of the proposed methodology is that it builds on well-established theoretical works, while delivering a practical technique with low computational requirements. As a result, the developed technique can be hosted on various components of an IoT system. The developed approach is evaluated through real-world use-cases.
Sandor, Hunor, Genge, Bela, Haller, Piroska, Bica, Andrei.  2019.  A Security-Enhanced Interoperability Middleware for the Internet of Things. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1–6.
This paper documents an Internet of Things (IoT) middleware specially tailored to address the security, and operational requirements expected from an effective IoT platform. In essence, the middleware exposes a diverse palette of features, including authentication, authorization, auditing, confidentiality and integrity of data. Besides these aspects, the middleware encapsulates an IoT object abstraction layer that builds a generic object model that is independent from the device type (i.e., hardware, software, vendor). Furthermore, it builds on standards and specifications to accomplish a highly resilient and scalable solution. The approach is tested on several hardware platforms. A use case scenario is presented to demonstrate its main features. The middleware represents a key component in the context of the “GHOST - Safe-Guarding Home IoT Environments with Personalised Real-time Risk Control” project.
Sandosh, S., Govindasamy, V., Akila, G., Deepasangavy, K., FemidhaBegam, S., Sowmiya, B..  2019.  A Progressive Intrusion Detection System through Event Processing: Challenges and Motivation. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). :1–7.
In this contemporary world, working on internet is a crucial task owing to the security threats in the network like intrusions, injections etc. To recognize and reduce these system attacks, analysts and academicians have introduced Intrusion Detection Systems (IDSs) with the various standards and applications. There are different types of Intrusion Detection Systems (IDS) arise to solve the attacks in various environments. Though IDS is more powerful, it produces the results on the abnormal behaviours said to be attacks with false positive and false negative rates which leads to inaccurate detection rate. The other problem is that, there are more number of attacks arising simultaneously with different behaviour being detected by the IDS with high false positive rates which spoils the strength and lifetime of the system, system's efficiency and fault tolerance. Complex Event Processing (CEP) plays a vital role in handling the alerts as events in real time environment which mainly helps to recognize and reduce the redundant alerts.CEP identifies and analyses relationships between events in real time, allowing the system to proactively take efficient actions to respond to specific alerts.In this study, the tendency of Complex Event Processing (CEP) over Intrusion Detection System (IDS) which offers effective handling of the alerts received from IDS in real time and the promotion of the better detection of the attacks are discussed. The merits and challenges of CEP over IDS described in this paper helps to understand and educate the IDS systems to focus on how to tackle the dynamic attacks and its alerts in real time.
Sandoval, S., Thulasiraman, P..  2019.  Cyber Security Assessment of the Robot Operating System 2 for Aerial Networks. 2019 IEEE International Systems Conference (SysCon). :1—8.

The Robot Operating System (ROS) is a widely adopted standard robotic middleware. However, its preliminary design is devoid of any network security features. Military grade unmanned systems must be guarded against network threats. ROS 2 is built upon the Data Distribution Service (DDS) standard and is designed to provide solutions to identified ROS 1 security vulnerabilities by incorporating authentication, encryption, and process profile features, which rely on public key infrastructure. The Department of Defense is looking to use ROS 2 for its military-centric robotics platform. This paper seeks to demonstrate that ROS 2 and its DDS security architecture can serve as a functional platform for use in military grade unmanned systems, particularly in unmanned Naval aerial swarms. In this paper, we focus on the viability of ROS 2 to safeguard communications between swarms and a ground control station (GCS). We test ROS 2's ability to mitigate and withstand certain cyber threats, specifically that of rogue nodes injecting unauthorized data and accessing services that will disable parts of the UAV swarm. We use the Gazebo robotics simulator to target individual UAVs to ascertain the effectiveness of our attack vectors under specific conditions. We demonstrate the effectiveness of ROS 2 in mitigating the chosen attack vectors but observed a measurable operational delay within our simulations.

Sang, Dinh Viet, Cuong, Dang Manh, Cuong, Le Tran Bao.  2018.  An Effective Ensemble Deep Learning Framework for Malware Detection. Proceedings of the Ninth International Symposium on Information and Communication Technology. :192–199.
Malware (or malicious software) is any program or file that brings harm to a computer system. Malware includes computer viruses, worms, trojan horses, rootkit, adware, ransomware and spyware. Due to the explosive growth in number and variety of malware, the demand of improving automatic malware detection has increased. Machine learning approaches are a natural choice to deal with this problem since they can automatically discover hidden patterns in large-scale datasets to distinguish malware from benign. In this paper, we propose different deep neural network architectures from simple to advanced ones. We then fuse hand-crafted and deep features, and combine all models together to make an overall effective ensemble framework for malware detection. The experiment results demonstrate the efficiency of our proposed method, which is capable to detect malware with accuracy of 96.24% on our large real-life dataset.
Sangeetha, V., Kumar, S. S..  2018.  Detection of malicious node in mobile ad-hoc network. 2018 International Conference on Power, Signals, Control and Computation (EPSCICON). :1–3.

In recent years, the area of Mobile Ad-hoc Net-work(MANET) has received considerable attention among the research community owing to the advantages in its networking features as well as solving the unsolved issues in it. One field which needs more security is the mobile ad hoc network. Mobile Ad-hoc Network is a temporary network composed of mobile nodes, connected by wireless links, without fixed infrastructure. Network security plays a crucial role in this MANET and the traditional way of protecting the networks through firewalls and encryption software is no longer effective and sufficient. In order to provide additional security to the MANET, intrusion detection mechanisms should be added. In this paper, selective acknowledgment is used for detecting malicious nodes in the Mobile ad-hoc network is proposed. In this paper we propose a novel mechanism called selective acknowledgment for solving problems that airse with Adaptive ACKnowledgment (AACK). This mechanism is an enhancement to the AACK scheme where its Packet delivery ration and detection overhead is reduced. NS2 is used to simulate and evaluate the proposed scheme and compare it against the AACK. The obtained results show that the selective acknowledgment scheme outperforms AACK in terms of network packet delivery ratio and routing overhead.

Sanger, J., Richthammer, C., Hassan, S., Pernul, G..  2014.  Trust and Big Data: A Roadmap for Research. Database and Expert Systems Applications (DEXA), 2014 25th International Workshop on. :278-282.

We are currently living in the age of Big Data coming along with the challenge to grasp the golden opportunities at hand. This mixed blessing also dominates the relation between Big Data and trust. On the one side, large amounts of trust-related data can be utilized to establish innovative data-driven approaches for reputation-based trust management. On the other side, this is intrinsically tied to the trust we can put in the origins and quality of the underlying data. In this paper, we address both sides of trust and Big Data by structuring the problem domain and presenting current research directions and inter-dependencies. Based on this, we define focal issues which serve as future research directions for the track to our vision of Next Generation Online Trust within the FORSEC project.
 

Sänger, Johannes, Hänsch, Norman, Glass, Brian, Benenson, Zinaida, Landwirth, Robert, Sasse, M. Angela.  2016.  Look Before You Leap: Improving the Users' Ability to Detect Fraud in Electronic Marketplaces. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :3870–3882.

Reputation systems in current electronic marketplaces can easily be manipulated by malicious sellers in order to appear more reputable than appropriate. We conducted a controlled experiment with 40 UK and 41 German participants on their ability to detect malicious behavior by means of an eBay-like feedback profile versus a novel interface involving an interactive visualization of reputation data. The results show that participants using the new interface could better detect and understand malicious behavior in three out of four attacks (the overall detection accuracy 77% in the new vs. 56% in the old interface). Moreover, with the new interface, only 7% of the users decided to buy from the malicious seller (the options being to buy from one of the available sellers or to abstain from buying), as opposed to 30% in the old interface condition.

Sani, Abubakar Sadiq, Yuan, Dong, Bao, Wei, Yeoh, Phee Lep, Dong, Zhao Yang, Vucetic, Branka, Bertino, Elisa.  2019.  Xyreum: A High-Performance and Scalable Blockchain for IIoT Security and Privacy. 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). :1920–1930.
As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.
Sani, Abubakar Sadiq, Yuan, Dong, Bao, Wei, Dong, Zhao Yang, Vucetic, Branka, Bertino, Elisa.  2019.  Universally Composable Key Bootstrapping and Secure Communication Protocols for the Energy Internet. IEEE Transactions on Information Forensics and Security. 14:2113–2127.
The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.
Sanila, A., Mahapatra, B., Turuk, A. K..  2020.  Performance Evaluation of RPL protocol in a 6LoWPAN based Smart Home Environment. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). :1–6.
The advancement in technologies like IoT, device-to-device communication lead to concepts like smart home and smart cities, etc. In smart home architecture, different devices such as home appliances, personal computers, surveillance cameras, etc. are connected to the Internet and enable the user to monitor and control irrespective of time and location. IPv6-enabled 6LoWPAN is a low-power, low-range communication protocol designed and developed for the short-range IoT applications. 6LoWPAN is based on IEEE 802.15.4 protocol and IPv6 network protocol for low range wireless applications. Although 6LoWPAN supports different routing protocols, RPL is the widely used routing protocol for low power and lossy networks. In this work, we have taken an IoT enabled smart home environment, in which 6LoWPAN is used as a communication and RPL as a routing protocol. The performance of this proposed network model is analyzed based on the different performance metrics such as latency, PDR, and throughput. The proposed model is simulated using Cooja simulator running over the Contiki OS. Along with the Cooja simulator, the network analyzer tool Wireshark is used to analyze the network behaviors.
Sanjaroon, Vahideh, Motahari, Abolfazl S., Farhadi, Alireza, Khalaj, Babak. H..  2019.  Tight Bound on the Stability of Control Systems over Parallel Gaussian Channels Using a New Joint Source Channel Coding. 2019 Iran Workshop on Communication and Information Theory (IWCIT). :1–6.
In this paper, we address the stability problem of a noiseless linear time invariant control system over parallel Gaussian channels with feedback. It is shown that the eigenvalues-rate condition which has been proved as a necessary condition, is also sufficient for stability over parallel Gaussian channels. In fact, it is proved that for stabilizing a control system over the parallel Gaussian channels, it suffices that the Shannon channel capacity obtained by the water filling technique is greater than the sum of the logarithm of the unstable eigenvalues magnitude. In order to prove this sufficient condition, we propose a new nonlinear joint source channel coding for parallel Gaussian channels by which the initial state is transmitted through communication steps. This coding scheme with a linear control policy results in the stability of the system under the eigenvalues-rate condition. Hence, the proposed encoder, decoder and controller are efficient for this problem.
Sanjay, K. N., Shaila, K., Venugopal, K. R..  2020.  LA-ANA based Architecture for Bluetooth Environment. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :222—226.
Wireless Personal Area Network is widely used in day to day life. It might be a static or dynamic environment. As the density of the nodes increases it becomes difficult to handle the situation. The need of multiple sensor node technology in a desired environment without congestion is required. The use of autonomic network provides one such solution. The autonomicity combines the local automate and address agnostic features that controls the congestion resulting in improved throughput, fault tolerance and also with unicast and multicast packets delivery. The algorithm LA based ANA in a Bluetooth based dynamic environment provide 20% increase in throughput compared with LACAS based Wireless Sensor Network. The LA based ANA leads with 10% lesser fault tolerance levels and extended unicast and multi-cast packet delivery.
Sanjeetha, R., Benoor, Pallavi, Kanavalli, Anita.  2019.  Mitigation of DDoS Attacks in Software Defined Networks at Application Level. 2019 PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). :1–3.

Software-Defined Network's (SDN) core working depends on the centralized controller which implements the control plane. With the help of this controller, security threats like Distributed Denial of Service (DDoS) attacks can be identified easily. A DDoS attack is usually instigated on servers by sending a huge amount of unwanted traffic that exhausts its resources, denying their services to genuine users. Earlier research work has been carried out to mitigate DDoS attacks at the switch and the host level. Mitigation at switch level involves identifying the switch which sends a lot of unwanted traffic in the network and blocking it from the network. But this solution is not feasible as it will also block genuine hosts connected to that switch. Later mitigation at the host level was introduced wherein the compromised hosts were identified and blocked thereby allowing genuine hosts to send their traffic in the network. Though this solution is feasible, it will block the traffic from the genuine applications of the compromised host as well. In this paper, we propose a new way to identify and mitigate the DDoS attack at the application level so that only the application generating the DDoS traffic is blocked and other genuine applications are allowed to send traffic in the network normally.

Sankalpa, I., Dhanushka, T., Amarasinghe, N., Alawathugoda, J., Ragel, R..  2016.  On implementing a client-server setting to prevent the Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH) attacks. 2016 Manufacturing Industrial Engineering Symposium (MIES). :1–5.

Compression is desirable for network applications as it saves bandwidth. Differently, when data is compressed before being encrypted, the amount of compression leaks information about the amount of redundancy in the plaintext. This side channel has led to the “Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH)” attack on web traffic protected by the TLS protocol. The general guidance to prevent this attack is to disable HTTP compression, preserving confidentiality but sacrificing bandwidth. As a more sophisticated countermeasure, fixed-dictionary compression was introduced in 2015 enabling compression while protecting high-value secrets, such as cookies, from attacks. The fixed-dictionary compression method is a cryptographically sound countermeasure against the BREACH attack, since it is proven secure in a suitable security model. In this project, we integrate the fixed-dictionary compression method as a countermeasure for BREACH attack, for real-world client-server setting. Further, we measure the performance of the fixed-dictionary compression algorithm against the DEFLATE compression algorithm. The results evident that, it is possible to save some amount of bandwidth, with reasonable compression/decompression time compared to DEFLATE operations. The countermeasure is easy to implement and deploy, hence, this would be a possible direction to mitigate the BREACH attack efficiently, rather than stripping off the HTTP compression entirely.

Sannon, Shruti, Stoll, Brett, DiFranzo, Dominic, Jung, Malte, Bazarova, Natalya N..  2018.  How Personification and Interactivity Influence Stress-Related Disclosures to Conversational Agents. Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. :285–288.
In this exploratory study, we examine how personification and interactivity may influence people's disclosures around sensitive topics, such as psychological stressors. Participants (N=441) shared a recent stressful experience with one of three agent interfaces: 1) a non-interactive, non-personified survey, 2) an interactive, non-personified chatbot, and 3) an interactive, personified chatbot. We coded these responses to examine how agent type influenced the nature of the stressor disclosed, and the intimacy and amount of disclosure. Participants discussed fewer homelife related stressors, but more finance-related stressors and more chronic stressors overall with the personified chatbot than the other two agents. The personified chatbot was also twice as likely as the other agents to receive disclosures that contained very little detail. We discuss the role played by personification and interactivity in interactions with conversational agents, and implications for design.
Santhi, G..  2016.  An Efficient Intrusion Detection System Based on Adaptive Acknowledgement with Digital Signature Scheme in MANETs. Proceedings of the International Conference on Informatics and Analytics. :103:1–103:5.

Mobile Ad hoc NETworks (MANETs) is a collection of mobile nodes and they can communicate with each other over the wireless medium without any fixed infrastructure. In MANETs any node can join and leave the network at any time and this makes MANETs vulnerable to a malicious attackers. Hence, it is necessary to develop an efficient intrusion-detection system to safeguard the MANET from attacks. In this paper, an Enhanced Adaptive Acknowledgement with Digital Signature Algorithm namely (EAACK-DSA) has been proposed which can detect and isolate the malicious nodes. This algorithm is based on the acknowledgement packet and hence all acknowledgement packets are digitally signed before transmission. The proposed algorithm can be integrated with any source routing protocol and EAACK-DSA gives a better malicious-behavior-detection than the conventional approaches.

Santhosh Prabhu, University of Illinois at Urbana-Champaign.  2016.  Oreo: Transparent Optimization to Enable Flexible Policy Enforcement in Softward Defined Networks.

Commercial networks today have diverse security policies, defined by factors such as the type of traffic they carry, nature of applications they support, access control objectives, organizational principles etc. Ideally, the wide diversity in SDN controller frameworks should prove helpful in correctly and efficiently enforcing these policies. However, this has not been the case so far. By requiring the administrators to implement both security as well as performance objectives in the SDN controller, these frameworks have made the task of security policy enforcement in SDNs a challenging one. We observe that by separating security policy enforcement from performance optimization, we can facilitate the use of SDN for flexible policy management. To this end, we propose Oreo, a transparent performance enhancement layer for SDNs. Oreo allows SDN controllers to focus entirely on a correct security policy enforcement, and transparently optimizes the dataplane thus defined, reducing path stretch, switch memory consumption etc. Optimizations are performed while guaranteeing that end-to-end reachability characteristics are preserved – meaning that the security policies defined by the controller are not violated. Oreo performs these optimizations by first constructing a network-wide model describing the behavior of all traffic, and then optimizing the paths observed in the model by solving a multi-objective optimization problem. Initial experiments suggest that the techniques used by Oreo is effective, fast, and can scale to commercial-sized networks.

Santhosh Prabhu, University of Illinois at Urbana-Champaign, Ali Kheradmand, University of Illinois at Urbana-Champaign, Brighten Godfrey, University of Illinois at Urbana-Champaign, Matthew Caesar, University of Illinois at Urbana-Champaign.  2017.  Predicting Network Futures with Plankton. 1st Asia-Pacific Workshop on Networking (APNet).

Recent years have seen significant advancement in the field of formal network verification. Tools have been proposed for offline data plane verification, real-time data plane verification and configuration verification under arbitrary, but static sets of failures. However, due to the fundamental limitation of not treating the network as an evolving system, current verification platforms have significant constraints in terms of scope. In real-world networks, correctness policies may be violated only through a particular combination of environment events and protocol actions, possibly in a non-deterministic sequence. Moreover, correctness specifications themselves may often correlate multiple data plane states, particularly when dynamic data plane elements are present. Tools in existence today are not capable of reasoning about all the possible network events, and all the subsequent execution paths that are enabled by those events. We propose Plankton, a verification platform for identifying undesirable evolutions of networks. By combining symbolic modeling of data plane and control plane with explicit state exploration, Plankton
performs a goal-directed search on a finite-state transition system that captures the behavior of the network as well as the various events that can influence it. In this way, Plankton can automatically find policy violations that can occur due to a sequence of network events, starting from the current state. Initial experiments have successfully predicted scenarios like BGP Wedgies.