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Srisopha, Kamonphop, Phonsom, Chukiat, Lin, Keng, Boehm, Barry.  2019.  Same App, Different Countries: A Preliminary User Reviews Study on Most Downloaded iOS Apps. 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). :76—80.
Prior work on mobile app reviews has demonstrated that user reviews contain a wealth of information and are seen as a potential source of requirements. However, most of the studies done in this area mainly focused on mining and analyzing user reviews from the US App Store, leaving reviews of users from other countries unexplored. In this paper, we seek to understand if the perception of the same apps between users from other countries and that from the US differs through analyzing user reviews. We retrieve 300,643 user reviews of the 15 most downloaded iOS apps of 2018, published directly by Apple, from nine English-speaking countries over the course of 5 months. We manually classify 3,358 reviews into several software quality and improvement factors. We leverage a random forest based algorithm to identify factors that can be used to differentiate reviews between the US and other countries. Our preliminary results show that all countries have some factors that are proportionally inconsistent with the US.
Tran, D. T., Waris, M. A., Gabbouj, M., Iosifidis, A..  2017.  Sample-Based Regularization for Support Vector Machine Classification. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). :1–6.

In this paper, we propose a new regularization scheme for the well-known Support Vector Machine (SVM) classifier that operates on the training sample level. The proposed approach is motivated by the fact that Maximum Margin-based classification defines decision functions as a linear combination of the selected training data and, thus, the variations on training sample selection directly affect generalization performance. We show that the exploitation of the proposed regularization scheme is well motivated and intuitive. Experimental results show that the proposed regularization scheme outperforms standard SVM in human action recognition tasks as well as classical recognition problems.

Haah, Jeongwan, Harrow, Aram W., Ji, Zhengfeng, Wu, Xiaodi, Yu, Nengkun.  2016.  Sample-optimal Tomography of Quantum States. Proceedings of the Forty-eighth Annual ACM Symposium on Theory of Computing. :913–925.

It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. This is the quantum analogue of the problem of estimating a probability distribution given some number of samples. Previously, it was known only that estimating states to error є in trace distance required O(dr2/є2) copies for a d-dimensional density matrix of rank r. Here, we give a measurement scheme (POVM) that uses O( (dr/ δ ) ln(d/δ) ) copies to estimate ρ to error δ in infidelity. This implies O( (dr / є2)· ln(d/є) ) copies suffice to achieve error є in trace distance. For fixed d, our measurement can be implemented on a quantum computer in time polynomial in n. We also use the Holevo bound from quantum information theory to prove a lower bound of Ω(dr/є2)/ log(d/rє) copies needed to achieve error є in trace distance. This implies a lower bound Ω(dr/δ)/log(d/rδ) for the estimation error δ in infidelity. These match our upper bounds up to log factors. Our techniques can also show an Ω(r2d/δ) lower bound for measurement strategies in which each copy is measured individually and then the outcomes are classically post-processed to produce an estimate. This matches the known achievability results and proves for the first time that such “product” measurements have asymptotically suboptimal scaling with d and r.

Haah, Jeongwan, Harrow, Aram W., Ji, Zhengfeng, Wu, Xiaodi, Yu, Nengkun.  2016.  Sample-optimal Tomography of Quantum States. Proceedings of the Forty-eighth Annual ACM Symposium on Theory of Computing. :913–925.

It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. This is the quantum analogue of the problem of estimating a probability distribution given some number of samples. Previously, it was known only that estimating states to error є in trace distance required O(dr2/є2) copies for a d-dimensional density matrix of rank r. Here, we give a measurement scheme (POVM) that uses O( (dr/ δ ) ln(d/δ) ) copies to estimate ρ to error δ in infidelity. This implies O( (dr / є2)· ln(d/є) ) copies suffice to achieve error є in trace distance. For fixed d, our measurement can be implemented on a quantum computer in time polynomial in n. We also use the Holevo bound from quantum information theory to prove a lower bound of Ω(dr/є2)/ log(d/rє) copies needed to achieve error є in trace distance. This implies a lower bound Ω(dr/δ)/log(d/rδ) for the estimation error δ in infidelity. These match our upper bounds up to log factors. Our techniques can also show an Ω(r2d/δ) lower bound for measurement strategies in which each copy is measured individually and then the outcomes are classically post-processed to produce an estimate. This matches the known achievability results and proves for the first time that such “product” measurements have asymptotically suboptimal scaling with d and r.

Shen, Shen, Tedrake, Russ.  2020.  Sampling Quotient-Ring Sum-of-Squares Programs for Scalable Verification of Nonlinear Systems. 2020 59th IEEE Conference on Decision and Control (CDC). :2535–2542.
This paper presents a novel method, combining new formulations and sampling, to improve the scalability of sum-of-squares (SOS) programming-based system verification. Region-of-attraction approximation problems are considered for polynomial, polynomial with generalized Lur'e uncertainty, and rational trigonometric multi-rigid-body systems. Our method starts by identifying that Lagrange multipliers, traditionally heavily used for S-procedures, are a major culprit of creating bloated SOS programs. In light of this, we exploit inherent system properties-continuity, convexity, and implicit algebraic structure-and reformulate the problems as quotient-ring SOS programs, thereby eliminating all the multipliers. These new programs are smaller, sparser, less constrained, yet less conservative. Their computation is further improved by leveraging a recent result on sampling algebraic varieties. Remarkably, solution correctness is guaranteed with just a finite (in practice, very small) number of samples. Altogether, the proposed method can verify systems well beyond the reach of existing SOS-based approaches (32 states); on smaller problems where a baseline is available, it computes tighter solution 2-3 orders of magnitude faster.
Wang, Hui, Yan, Qiurong, Li, Bing, Yuan, Chenglong, Wang, Yuhao.  2019.  Sampling Time Adaptive Single-Photon Compressive Imaging. IEEE Photonics Journal. 11:1–10.
We propose a time-adaptive sampling method and demonstrate a sampling-time-adaptive single-photon compressive imaging system. In order to achieve self-adapting adjustment of sampling time, the theory of threshold of light intensity estimation accuracy is deduced. According to this threshold, a sampling control module, based on field-programmable gate array, is developed. Finally, the advantage of the time-adaptive sampling method is proved experimentally. Imaging performance experiments show that the time-adaptive sampling method can automatically adjust the sampling time for the change of light intensity of image object to obtain an image with better quality and avoid speculative selection of sampling time.
Ambrosin, Moreno, Conti, Mauro, Ibrahim, Ahmad, Neven, Gregory, Sadeghi, Ahmad-Reza, Schunter, Matthias.  2016.  SANA: Secure and Scalable Aggregate Network Attestation. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :731–742.

Large numbers of smart connected devices, also named as the Internet of Things (IoT), are permeating our environments (homes, factories, cars, and also our body - with wearable devices) to collect data and act on the insight derived. Ensuring software integrity (including OS, apps, and configurations) on such smart devices is then essential to guarantee both privacy and safety. A key mechanism to protect the software integrity of these devices is remote attestation: A process that allows a remote verifier to validate the integrity of the software of a device. This process usually makes use of a signed hash value of the actual device's software, generated by dedicated hardware. While individual device attestation is a well-established technique, to date integrity verification of a very large number of devices remains an open problem, due to scalability issues. In this paper, we present SANA, the first secure and scalable protocol for efficient attestation of large sets of devices that works under realistic assumptions. SANA relies on a novel signature scheme to allow anyone to publicly verify a collective attestation in constant time and space, for virtually an unlimited number of devices. We substantially improve existing swarm attestation schemes by supporting a realistic trust model where: (1) only the targeted devices are required to implement attestation; (2) compromising any device does not harm others; and (3) all aggregators can be untrusted. We implemented SANA and demonstrated its efficiency on tiny sensor devices. Furthermore, we simulated SANA at large scale, to assess its scalability. Our results show that SANA can provide efficient attestation of networks of 1,000,000 devices, in only 2.5 seconds.

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

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

Hassan, Galal, Rashwan, Abdulmonem M., Hassanein, Hossam S..  2019.  SandBoxer: A Self-Contained Sensor Architecture for Sandboxing the Industrial Internet of Things. 2019 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
The Industrial Internet-of-Things (IIoT) has gained significant interest from both the research and industry communities. Such interest came with a vision towards enabling automation and intelligence for futuristic versions of our day to day devices. However, such a vision demands the need for accelerated research and development of IIoT systems, in which sensor integration, due to their diversity, impose a significant roadblock. Such roadblocks are embodied in both the cost and time to develop an IIoT platform, imposing limits on the innovation of sensor manufacturers, as a result of the demand to maintain interface compatibility for seamless integration and low development costs. In this paper, we propose an IIoT system architecture (SandBoxer) tailored for sensor integration, that utilizes a collaborative set of efforts from various technologies and research fields. The paper introduces the concept of ”development-sandboxing” as a viable choice towards building the foundation for enabling true-plug-and-play IIoT. We start by outlining the key characteristics desired to create an architecture that catalyzes IIoT research and development. We then present our vision of the architecture through the use of a sensor-hosted EEPROM and scripting to ”sandbox” the sensors, which in turn accelerates sensor integration for developers and creates a broader innovation path for sensor manufacturers. We also discuss multiple design alternative, challenges, and use cases in both the research and industry.
Chi, Po-Wen, Wang, Ming-Hung, Zheng, Yu.  2020.  SandboxNet: An Online Malicious SDN Application Detection Framework for SDN Networking. 2020 International Computer Symposium (ICS). :397—402.

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

Lamowski, Benjamin, Weinhold, Carsten, Lackorzynski, Adam, Härtig, Hermann.  2017.  Sandcrust: Automatic Sandboxing of Unsafe Components in Rust. Proceedings of the 9th Workshop on Programming Languages and Operating Systems. :51–57.

System-level development has been dominated by traditional programming languages such as C and C++ for decades. These languages are inherently unsafe regarding memory management. Even experienced developers make mistakes that open up security holes or compromise the safety properties of software. The Rust programming language is targeted at the systems domain and aims to eliminate memory-related programming errors by enforcing a strict memory model at the language and compiler level. Unfortunately, these compile-time guarantees no longer hold when a Rust program is linked against a library written in unsafe C, which is commonly required for functionality where an implementation in Rust is not yet available. In this paper, we present Sandcrust, an easy-to-use sand-boxing solution for isolating code and data of a C library in a separate process. This isolation protects the Rust-based main program from any memory corruption caused by bugs in the unsafe library, which would otherwise invalidate the memory safety guarantees of Rust. Sandcrust is based on the Rust macro system and requires no modification to the compiler or runtime, but only straightforward annotation of functions that call the library's API.

Osman, Amr, Bruckner, Pascal, Salah, Hani, Fitzek, Frank H. P., Strufe, Thorsten, Fischer, Mathias.  2019.  Sandnet: Towards High Quality of Deception in Container-Based Microservice Architectures. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–7.
Responding to network security incidents requires interference with ongoing attacks to restore the security of services running on production systems. This approach prevents damage, but drastically impedes the collection of threat intelligence and the analysis of vulnerabilities, exploits, and attack strategies. We propose the live confinement of suspicious microservices into a sandbox network that allows to monitor and analyze ongoing attacks under quarantine and that retains an image of the vulnerable and open production network. A successful sandboxing requires that it happens completely transparent to and cannot be detected by an attacker. Therefore, we introduce a novel metric to measure the Quality of Deception (QoD) and use it to evaluate three proposed network deception mechanisms. Our evaluation results indicate that in our evaluation scenario in best case, an optimal QoD is achieved. In worst case, only a small downtime of approx. 3s per microservice (MS) occurs and thus a momentary drop in QoD to 70.26% before it converges back to optimum as the quarantined services are restored.
Deshotels, Luke, Deaconescu, Razvan, Chiroiu, Mihai, Davi, Lucas, Enck, William, Sadeghi, Ahmad-Reza.  2016.  SandScout: Automatic Detection of Flaws in iOS Sandbox Profiles. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :704–716.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

Razeen, Ali, Lebeck, Alvin R., Liu, David H., Meijer, Alexander, Pistol, Valentin, Cox, Landon P..  2018.  SandTrap: Tracking Information Flows On Demand with Parallel Permissions. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. :230-242.

The most promising way to improve the performance of dynamic information-flow tracking (DIFT) for machine code is to only track instructions when they process tainted data. Unfortunately, prior approaches to on-demand DIFT are a poor match for modern mobile platforms that rely heavily on parallelism to provide good interactivity in the face of computationally intensive tasks like image processing. The main shortcoming of these prior efforts is that they cannot support an arbitrary mix of parallel threads due to the limitations of page protections. In this paper, we identify parallel permissions as a key requirement for multithreaded, on-demand native DIFT, and we describe the design and implementation of a system called SandTrap that embodies this approach. Using our prototype implementation, we demonstrate that SandTrap's native DIFT overhead is proportional to the amount of tainted data that native code processes. For example, in the photo-sharing app Instagram, SandTrap's performance is close to baseline (1x) when the app does not access tainted data. When it does, SandTrap imposes a slowdown comparable to prior DIFT systems (\textasciitilde8x).

Chen, Bo, Jia, Shijie, Xia, Luning, Liu, Peng.  2016.  Sanitizing Data is Not Enough!: Towards Sanitizing Structural Artifacts in Flash Media. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :496–507.

Conventional overwriting-based and encryption-based secure deletion schemes can only sanitize data. However, the past existence of the deleted data may leave artifacts in the layout at all layers of a computing system. These structural artifacts may be utilized by the adversary to infer sensitive information about the deleted data or even to fully recover them. The conventional secure deletion solutions unfortunately cannot sanitize them. In this work, we introduce truly secure deletion, a novel security notion that is much stronger than the conventional secure deletion. Truly secure deletion requires sanitizing both the obsolete data as well as the corresponding structural artifacts, so that the resulting storage layout after a delete operation is indistinguishable from that the deleted data never appeared. We propose TedFlash, a Truly secure deletion scheme for Flash-based block devices. TedFlash can successfully sanitize both the data and the structural artifacts, while satisfying the design constraints imposed for flash memory. Security analysis and experimental evaluation show that TedFlash can achieve the truly secure deletion guarantee with a small additional overhead compared to conventional secure deletion solutions.

Balouchestani, Arian, Mahdavi, Mojtaba, Hallaj, Yeganeh, Javdani, Delaram.  2019.  SANUB: A new method for Sharing and Analyzing News Using Blockchain. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :139–143.
Millions of news are being exchanged daily among people. With the appearance of the Internet, the way of broadcasting news has changed and become faster, however it caused many problems. For instance, the increase in the speed of broadcasting news leads to an increase in the speed of fake news creation. Fake news can have a huge impression on societies. Additionally, the existence of a central entity, such as news agencies, could lead to fraud in the news broadcasting process, e.g. generating fake news and publishing them for their benefits. Since Blockchain technology provides a reliable decentralized network, it can be used to publish news. In addition, Blockchain with the help of decentralized applications and smart contracts can provide a platform in which fake news can be detected through public participation. In this paper, we proposed a new method for sharing and analyzing news to detect fake news using Blockchain, called SANUB. SANUB provides features such as publishing news anonymously, news evaluation, reporter validation, fake news detection and proof of news ownership. The results of our analysis show that SANUB outperformed the existing methods.
Dhende, S., Musale, S., Shirbahadurkar, S., Najan, A..  2017.  SAODV: Black hole and gray hole attack detection protocol in MANETs. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). :2391–2394.

A MANET is a group of wireless mobile nodes which cooperate in forwarding packets over a wireless links. Due to the lack of an infrastructure and open nature of MANET, security has become an essential and challenging issue. The mobile nature and selfishness of malicious node is a critical issue in causing the security problem. The MANETs are more defenseless to the security attacks; some of them are black hole and gray hole attacks. One of its key challenges is to find black hole attack. In this paper, researchers propose a secure AODV protocol (SAODV) for detection and removal of black hole and gray hole attacks in MANTEs. The proposed method is simulated using NS-2 and it seems that the proposed methodology is more secure than the existing one.

Gu, Feng, Zhang, Hong, Wang, Chao, Wu, Fan.  2019.  SAR Image Super-Resolution Based on Noise-Free Generative Adversarial Network. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. :2575—2578.

Deep learning has been successfully applied to the ordinary image super-resolution (SR). However, since the synthetic aperture radar (SAR) images are often disturbed by multiplicative noise known as speckle and more blurry than ordinary images, there are few deep learning methods for the SAR image SR. In this paper, a deep generative adversarial network (DGAN) is proposed to reconstruct the pseudo high-resolution (HR) SAR images. First, a generator network is constructed to remove the noise of low-resolution SAR image and generate HR SAR image. Second, a discriminator network is used to differentiate between the pseudo super-resolution images and the realistic HR images. The adversarial objective function is introduced to make the pseudo HR SAR images closer to real SAR images. The experimental results show that our method can maintain the SAR image content with high-level noise suppression. The performance evaluation based on peak signal-to-noise-ratio and structural similarity index shows the superiority of the proposed method to the conventional CNN baselines.

Monteuuis, Jean-Philippe, Boudguiga, Aymen, Zhang, Jun, Labiod, Houda, Servel, Alain, Urien, Pascal.  2018.  SARA: Security Automotive Risk Analysis Method. Proceedings of the 4th ACM Workshop on Cyber-Physical System Security. :3-14.

Connected and automated vehicles aim to improve the comfort and the safety of the driver and passengers. To this end, car manufacturers continually improve actual standardized methods to ensure their customers safety, privacy, and vehicles security. However, these methods do not support fully autonomous vehicles, linkability and confusion threats. To address such gaps, we propose a systematic threat analysis and risk assessment framework, SARA, which comprises an improved threat model, a new attack method/asset map, the involvement of the attacker in the attack tree, and a new driving system observation metric. Finally, we demonstrate its feasibility in assessing risk with two use cases: Vehicle Tracking and Comfortable Emergency Brake Failure.

Niu, Yingjiao, Lei, Lingguang, Wang, Yuewu, Chang, Jiang, Jia, Shijie, Kou, Chunjing.  2020.  SASAK: Shrinking the Attack Surface for Android Kernel with Stricter “seccomp” Restrictions. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :387–394.
The increasing vulnerabilities in Android kernel make it an attractive target to the attackers. Most kernel-targeted attacks are initiated through system calls. For security purpose, Google has introduced a Linux kernel security mechanism named “seccomp” since Android O to constrain the system calls accessible to the Android apps. Unfortunately, existing Android seccomp mechanism provides a fairly coarse-grained restriction by enforcing a unified seccomp policy containing more than 250 system calls for Android apps, which greatly reduces the effectiveness of seccomp. Also, it lacks an approach to profile the unnecessary system calls for a given Android app. In this paper we present a two-level control scheme named SASAK, which can shrink the attack surface of Android kernel by strictly constraining the system calls available to the Android apps with seccomp mechanism. First, instead of leveraging a unified seccomp policy for all Android apps, SASAK introduces an architecture- dedicated system call constraining by enforcing two separate and refined seccomp policies for the 32-bit Android apps and 64-bit Android apps, respectively. Second, we provide a tool to profile the necessary system calls for a given Android app and enforce an app-dedicated seccomp policy to further reduce the allowed system calls for the apps selected by the users. The app-dedicated control could dynamically change the seccomp policy for an app according to its actual needs. We implement a prototype of SASAK and the experiment results show that the architecture-dedicated constraining reduces 39.6% system calls for the 64-bit apps and 42.5% system calls for the 32-bit apps. 33% of the removed system calls for the 64-bit apps are vulnerable, and the number for the 32-bit apps is 18.8%. The app-dedicated restriction reduces about 66.9% and 62.5% system calls on average for the 64-bit apps and 32-bit apps, respectively. In addition, SASAK introduces negligible performance overhead.
Wan, Shengye, Sun, Jianhua, Sun, Kun, Zhang, Ning, Li, Qi.  2019.  SATIN: A Secure and Trustworthy Asynchronous Introspection on Multi-Core ARM Processors. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :289–301.

On ARM processors with TrustZone security extension, asynchronous introspection mechanisms have been developed in the secure world to detect security policy violations in the normal world. These mechanisms provide security protection via passively checking the normal world snapshot. However, since previous secure world checking solutions require to suspend the entire rich OS, asynchronous introspection has not been widely adopted in the real world. Given a multi-core ARM system that can execute the two worlds simultaneously on different cores, secure world introspection can check the rich OS without suspension. However, we identify a new normal-world evasion attack that can defeat the asynchronous introspection by removing the attacking traces in parallel from one core when the security checking is performing on another core. We perform a systematic study on this attack and present its efficiency against existing asynchronous introspection mechanisms. As the countermeasure, we propose a secure and trustworthy asynchronous introspection mechanism called SATIN, which can efficiently detect the evasion attacks by increasing the attackers' evasion time cost and decreasing the defender's execution time under a safe limit. We implement a prototype on an ARM development board and the experimental results show that SATIN can effectively prevent evasion attacks on multi-core systems with a minor system overhead.

Zainuddin, Muhammad Agus, Dedu, Eugen, Bourgeois, Julien.  2016.  SBN: Simple Block Nanocode for Nanocommunications. Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication. :4:1–4:7.

Nanonetworks consist of nanomachines that perform simple tasks (sensing, data processing and communication) at molecular scale. Nanonetworks promise novel solutions in various fields, such as biomedical, industrial and military. Reliable nanocommunications require error control. ARQ and complex Forward Error Correction (FEC) are not appropriate in nano-devices due to the peculiarities of Terahertz band, limited computation complexity and energy capacity. In this paper we propose Simple Block Nanocode (SBN) to provide reliable data transmission in electromagnetic nanocommunications. We compare it with the two reliable transmission codes in nanonetworks in the literature, minimum energy channel (MEC) and Low Weight Channel (LWC) codes. The results show that SBN outperforms MEC and LWC in terms of reliability and image quality at receiver. The results also show that a nano-device (with nano-camera) with harvesting module has enough energy to support perpetual image transmission.

Nivethan, Jeyasingam, Papa, Mauricio.  2016.  A SCADA Intrusion Detection Framework That Incorporates Process Semantics. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :6:1–6:5.

SCADA security is an increasingly important research area as these systems, used for process control and automation, are being exposed to the Internet due to their use of TCP/IP protocols as a transport mechanism for control messages. Most of the existing research work on SCADA systems has focused on addressing SCADA security by monitoring attacks or anomalies at the network level. The main issue affecting these systems today is that by focusing our attention on network-level monitoring needs, security practitioners may remain unaware of process level constraints. The proposed framework helps ensure that a mechanism is in place to help map process level constraints, as described by process engineers, to network level monitoring needs. Existing solutions have tried to address this problem but have not been able to fully bridge the gap between the process and the network. The goal of this research is to provide a solution that (i) leverages the knowledge process engineers have about the system (to help strengthen cyber security) and that has the ability to (ii) seamlessly monitors process constraints at the network level using standard network security tools. A prototype system for the Modbus TCP protocol and the Bro IDS has been built to validate the approach.

Almehmadi, Abdulaziz.  2018.  SCADA Networks Anomaly-based Intrusion Detection System. Proceedings of the 11th International Conference on Security of Information and Networks. :18:1–18:4.
Intentional attacks1 that cause country wide blackouts, gas and water systems malfunction are actions that can be carried out by a nation to impact on another nation in a mean of war. Supervisory control and data acquisition (SCADA) networks that allow for communication for the utilities companies were designed with no security in mind causing the systems that a nation relies on to fall vulnerable to exploitation. Since SCADA networks are static in nature with pre-defined signatures of network traffic, we propose to design an anomaly-based intrusion detection system to detect abnormality in SCADA network traffic and protocols. We gather normal SCADA network traffic via tapping on the network for 30 days and then attack the network using Denial of Service (DoS) attack, message spoofing attack and man-in-the middle attack. We then train a classifier with two classes, normal and abnormal and report the classifier accuracy in detecting abnormal SCADA network traffic.
Ahmed, Irfan, Roussev, Vassil, Johnson, William, Senthivel, Saranyan, Sudhakaran, Sneha.  2016.  A SCADA System Testbed for Cybersecurity and Forensic Research and Pedagogy. Proceedings of the 2Nd Annual Industrial Control System Security Workshop. :1–9.

This paper presents a supervisory control and data acquisition (SCADA) testbed recently built at the University of New Orleans. The testbed consists of models of three industrial physical processes: a gas pipeline, a power transmission and distribution system, and a wastewater treatment plant–these systems are fully-functional and implemented at small-scale. It utilizes real-world industrial equipment such as transformers, programmable logic controllers (PLC), aerators, etc., bringing it closer to modeling real-world SCADA systems. Sensors, actuators, and PLCs are deployed at each physical process system for local control and monitoring, and the PLCs are also connected to a computer running human-machine interface (HMI) software for monitoring the status of the physical processes. The testbed is a useful resource for cybersecurity research, forensic research, and education on different aspects of SCADA systems such as PLC programming, protocol analysis, and demonstration of cyber attacks.