Visible to the public Biblio

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Zheng, H., Zhang, X..  2017.  Optimizing Task Assignment with Minimum Cost on Heterogeneous Embedded Multicore Systems Considering Time Constraint. 2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids). :225–230.
Time and cost are the most critical performance metrics for computer systems including embedded system, especially for the battery-based embedded systems, such as PC, mainframe computer, and smart phone. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures this paper proposes an optimal energy efficiency and system performance model and the OTHAP (Optimizing Task Heterogeneous Assignment with Probability) algorithm to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with areal-time embedded systems guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task and The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).
Wang, M., Qu, Z., He, X., Li, T., Jin, X., Gao, Z., Zhou, Z., Jiang, F., Li, J..  2017.  Real time fault monitoring and diagnosis method for power grid monitoring and its application. 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). :1–6.
In Energy Internet mode, a large number of alarm information is generated when equipment exception and multiple faults in large power grid, which seriously affects the information collection, fault analysis and delays the accident treatment for the monitors. To this point, this paper proposed a method for power grid monitoring to monitor and diagnose fault in real time, constructed the equipment fault logical model based on five section alarm information, built the standard fault information set, realized fault information optimization, fault equipment location, fault type diagnosis, false-report message and missing-report message analysis using matching algorithm. The validity and practicality of the proposed method by an actual case was verified, which can shorten the time of obtaining and analyzing fault information, accelerate the progress of accident treatment, ensure the safe and stable operation of power grid.
Kuzuno, H., Karam, C..  2017.  Blockchain explorer: An analytical process and investigation environment for bitcoin. 2017 APWG Symposium on Electronic Crime Research (eCrime). :9–16.

Bitcoin is the most famous cryptocurrency currently operating with a total marketcap of almost 7 billion USD. This innovation stands strong on the feature of pseudo anonymity and strives on its innovative de-centralized architecture based on the Blockchain. The Blockchain is a distributed ledger that keeps a public record of all the transactions processed on the bitcoin protocol network in full transparency without revealing the identity of the sender and the receiver. Over the course of 2016, cryptocurrencies have shown some instances of abuse by criminals in their activities due to its interesting nature. Darknet marketplaces are increasing the volume of their businesses in illicit and illegal trades but also cryptocurrencies have been used in cases of extortion, ransom and as part of sophisticated malware modus operandi. We tackle these challenges by developing an analytical capability that allows us to map relationships on the blockchain and filter crime instances in order to investigate the abuse in law enforcement local environment. We propose a practical bitcoin analytical process and an analyzing system that stands alone and manages all data on the blockchain in real-time with tracing and visualizing techniques rendering transactions decipherable and useful for law enforcement investigation and training. Our system adopts combination of analyzing methods that provides statistics of address, graphical transaction relation, discovery of paths and clustering of already known addresses. We evaluated our system in the three criminal cases includes marketplace, ransomware and DDoS extortion. These are practical training in law enforcement, then we determined whether our system could help investigation process and training.

Chakraborty, N., Kalaimannan, E..  2017.  Minimum Cost Security Measurements for Attack Tree Based Threat Models in Smart Grid. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). :614–618.

In this paper, we focus on the security issues and challenges in smart grid. Smart grid security features must address not only the expected deliberate attacks, but also inadvertent compromises of the information infrastructure due to user errors, equipment failures, and natural disasters. An important component of smart grid is the advanced metering infrastructure which is critical to support two-way communication of real time information for better electricity generation, distribution and consumption. These reasons makes security a prominent factor of importance to AMI. In recent times, attacks on smart grid have been modelled using attack tree. Attack tree has been extensively used as an efficient and effective tool to model security threats and vulnerabilities in systems where the ultimate goal of an attacker can be divided into a set of multiple concrete or atomic sub-goals. The sub-goals are related to each other as either AND-siblings or OR-siblings, which essentially depicts whether some or all of the sub-goals must be attained for the attacker to reach the goal. On the other hand, as a security professional one needs to find out the most effective way to address the security issues in the system under consideration. It is imperative to assume that each attack prevention strategy incurs some cost and the utility company would always look to minimize the same. We present a cost-effective mechanism to identify minimum number of potential atomic attacks in an attack tree.

Marciani, G., Porretta, M., Nardelli, M., Italiano, G. F..  2017.  A Data Streaming Approach to Link Mining in Criminal Networks. 2017 5th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). :138–143.

The ability to discover patterns of interest in criminal networks can support and ease the investigation tasks by security and law enforcement agencies. By considering criminal networks as a special case of social networks, we can properly reuse most of the state-of-the-art techniques to discover patterns of interests, i.e., hidden and potential links. Nevertheless, in time-sensible scenarios, like the one involving criminal actions, the ability to discover patterns in a (near) real-time manner can be of primary importance.In this paper, we investigate the identification of patterns for link detection and prediction on an evolving criminal network. To extract valuable information as soon as data is generated, we exploit a stream processing approach. To this end, we also propose three new similarity social network metrics, specifically tailored for criminal link detection and prediction. Then, we develop a flexible data stream processing application relying on the Apache Flink framework; this solution allows us to deploy and evaluate the newly proposed metrics as well as the ones existing in literature. The experimental results show that the new metrics we propose can reach up to 83% accuracy in detection and 82% accuracy in prediction, resulting competitive with the state of the art metrics.

Wang, Y., Rawal, B., Duan, Q..  2017.  Securing Big Data in the Cloud with Integrated Auditing. 2017 IEEE International Conference on Smart Cloud (SmartCloud). :126–131.

In this paper, we review big data characteristics and security challenges in the cloud and visit different cloud domains and security regulations. We propose using integrated auditing for secure data storage and transaction logs, real-time compliance and security monitoring, regulatory compliance, data environment, identity and access management, infrastructure auditing, availability, privacy, legality, cyber threats, and granular auditing to achieve big data security. We apply a stochastic process model to conduct security analyses in availability and mean time to security failure. Potential future works are also discussed.

Guan, Z., Si, G., Du, X., Liu, P., Zhang, Z., Zhou, Z..  2017.  Protecting User Privacy Based on Secret Sharing with Fault Tolerance for Big Data in Smart Grid. 2017 IEEE International Conference on Communications (ICC). :1–6.

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications is usually sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with fault tolerance in smart grid, which ensures that control center gets the integrated data without revealing user's privacy. Meanwhile, we also consider fault tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.

Anderson, E. C., Okafor, K. C., Nkwachukwu, O., Dike, D. O..  2017.  Real time car parking system: A novel taxonomy for integrated vehicular computing. 2017 International Conference on Computing Networking and Informatics (ICCNI). :1–9.
Automation of real time car parking system (RTCPS) using mobile cloud computing (MCC) and vehicular networking (VN) has given rise to a novel concept of integrated communication-computing platforms (ICCP). The aim of ICCP is to evolve an effective means of addressing challenges such as improper parking management scheme, traffic congestion in parking lots, insecurity of vehicles (safety applications), and other Infrastructure-to-Vehicle (I2V) services for providing data dissemination and content delivery services to connected Vehicular Clients (VCs). Edge (parking lot based) Fog computing (EFC) through road side sensor based monitoring is proposed to achieve ICCP. A real-time cloud to vehicular clients (VCs) in the context of smart car parking system (SCPS) which satisfies deterministic and non-deterministic constraints is introduced. Vehicular cloud computing (VCC) and intra-Edge-Fog node architecture is presented for ICCP. This is targeted at distributed mini-sized self-energized Fog nodes/data centers, placed between distributed remote cloud and VCs. The architecture processes data-disseminated real-time services to the connected VCs. The work built a prototype testbed comprising a black box PSU, Arduino IoT Duo, GH-311RT ultrasonic distance sensor and SHARP 2Y0A21 passive infrared sensor for vehicle detection; LinkSprite 2MP UART JPEG camera module, SD card module, RFID card reader, RDS3115 metal gear servo motors, FPM384 fingerprint scanner, GSM Module and a VCC web portal. The testbed functions at the edge of the vehicular network and is connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. This research seeks to facilitate urban renewal strategies and highlight the significance of ICCP prototype testbed. Open challenges and future research directions are discussed for an efficient VCC model which runs on networked fog centers (NetFCs).
Arifeen, F. U., Ali, M., Ashraf, S..  2016.  QoS and security in VOIP networks through admission control mechanism. 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST). :373–380.

With the developing understanding of Information Security and digital assets, IT technology has put on tremendous importance of network admission control (NAC). In NAC architecture, admission decisions and resource reservations are taken at edge devices, rather than resources or individual routers within the network. The NAC architecture enables resilient resource reservation, maintaining reservations even after failures and intra-domain rerouting. Admission Control Networks destiny is based on IP networks through its Security and Quality of Service (QoS) demands for real time multimedia application via advance resource reservation techniques. To achieve Security & QoS demands, in real time performance networks, admission control algorithm decides whether the new traffic flow can be admitted to the network or not. Secure allocation of Peer for multimedia traffic flows with required performance is a great challenge in resource reservation schemes. In this paper, we have proposed our model for VoIP networks in order to achieve security services along with QoS, where admission control decisions are taken place at edge routers. We have analyzed and argued that the measurement based admission control should be done at edge routers which employs on-demand probing parallel from both edge routers to secure the source and destination nodes respectively. In order to achieve Security and QoS for a new call, we choose various probe packet sizes for voice and video calls respectively. Similarly a technique is adopted to attain a security allocation approach for selecting an admission control threshold by proposing our admission control algorithm. All results are tested on NS2 based simulation to evalualate the network performance of edge router based upon network admission control in VoIP traffic.

Ashok, A., Sridhar, S., McKinnon, A. D., Wang, P., Govindarasu, M..  2016.  Testbed-based performance evaluation of Attack Resilient Control for AGC. 2016 Resilience Week (RWS). :125–129.

The modern electric power grid is a complex cyber-physical system whose reliable operation is enabled by a wide-area monitoring and control infrastructure. Recent events have shown that vulnerabilities in this infrastructure may be exploited to manipulate the data being exchanged. Such a scenario could cause the associated control applications to mis-operate, potentially causing system-wide instabilities. There is a growing emphasis on looking beyond traditional cybersecurity solutions to mitigate such threats. In this paper we perform a testbed-based validation of one such solution - Attack Resilient Control (ARC) - on Iowa State University's PowerCyber testbed. ARC is a cyber-physical security solution that combines domain-specific anomaly detection and model-based mitigation to detect stealthy attacks on Automatic Generation Control (AGC). In this paper, we first describe the implementation architecture of the experiment on the testbed. Next, we demonstrate the capability of stealthy attack templates to cause forced under-frequency load shedding in a 3-area test system. We then validate the performance of ARC by measuring its ability to detect and mitigate these attacks. Our results reveal that ARC is efficient in detecting stealthy attacks and enables AGC to maintain system operating frequency close to its nominal value during an attack. Our studies also highlight the importance of testbed-based experimentation for evaluating the performance of cyber-physical security and control applications.

AbuAli, N. A., Taha, A. E. M..  2017.  A dynamic scalable scheme for managing mixed crowds. 2017 IEEE International Conference on Communications (ICC). :1–5.

Crowd management in urban settings has mostly relied on either classical, non-automated mechanisms or spontaneous notifications/alerts through social networks. Such management techniques are heavily marred by lack of comprehensive control, especially in terms of averting risks in a manner that ensures crowd safety and enables prompt emergency response. In this paper, we propose a Markov Decision Process Scheme MDP to realize a smart infrastructure that is directly aimed at crowd management. A key emphasis of the scheme is a robust and reliable scalability that provides sufficient flexibility to manage a mixed crowd (i.e., pedestrian, cyclers, manned vehicles and unmanned vehicles). The infrastructure also spans various population settings (e.g., roads, buildings, game arenas, etc.). To realize a reliable and scalable crowd management scheme, the classical MDP is decomposed into Local MDPs with smaller action-state spaces. Preliminarily results show that the MDP decomposition can reduce the system global cost and facilitate fast convergence to local near-optimal solution for each L-MDP.

Maheshwari, B. C., Burns, J., Blott, M., Gambardella, G..  2017.  Implementation of a scalable real time canny edge detector on programmable SOC. 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). :1–5.

In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmable logic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.

Falk, E., Repcek, S., Fiz, B., Hommes, S., State, R., Sasnauskas, R..  2017.  VSOC - A Virtual Security Operating Center. GLOBECOM 2017 - 2017 IEEE Global Communications Conference. :1–6.

Security in virtualised environments is becoming increasingly important for institutions, not only for a firm's own on-site servers and network but also for data and sites that are hosted in the cloud. Today, security is either handled globally by the cloud provider, or each customer needs to invest in its own security infrastructure. This paper proposes a Virtual Security Operation Center (VSOC) that allows to collect, analyse and visualize security related data from multiple sources. For instance, a user can forward log data from its firewalls, applications and routers in order to check for anomalies and other suspicious activities. The security analytics provided by the VSOC are comparable to those of commercial security incident and event management (SIEM) solutions, but are deployed as a cloud-based solution with the additional benefit of using big data processing tools to handle large volumes of data. This allows us to detect more complex attacks that cannot be detected with todays signature-based (i.e. rules) SIEM solutions.

Sharma, V..  2017.  Multi-agent based intrusion prevention and mitigation architecture for software defined networks. 2017 International Conference on Information and Communication Technology Convergence (ICTC). :686–692.

Software Defined Networking (SDN) has proved to be a promising approach for creating next generation software based network ecosystems. It has provided us with a centralized network provision, a holistic management plane and a well-defined level of abstraction. But, at the same time brings forth new security and management challenges. Research in the field of SDN is primarily focused on reconfiguration, forwarding and network management issues. However in recent times the interest has moved to tackling security and maintenance issues. This work is based on providing a means to mitigate security challenges in an SDN environment from a DDoS attack based point of view. This paper introduces a Multi-Agent based intrusion prevention and mitigation architecture for SDN. Thus allowing networks to govern their behavior and take appropriate measures when the network is under attack. The architecture is evaluated against filter based intrusion prevention architectures to measure efficiency and resilience against DDoS attacks and false policy based attacks.

Ahmed, M. E., Kim, H..  2017.  DDoS Attack Mitigation in Internet of Things Using Software Defined Networking. 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService). :271–276.

Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.

Gangadhar, S., Sterbenz, J. P. G..  2017.  Machine learning aided traffic tolerance to improve resilience for software defined networks. 2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM). :1–7.

Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.

Kotel, S., Sbiaa, F., Zeghid, M., Machhout, M., Baganne, A., Tourki, R..  2016.  Efficient Hybrid Encryption System Based on Block Cipher and Chaos Generator. 2016 IEEE International Conference on Computer and Information Technology (CIT). :375–382.

In recent years, more and more multimedia data are generated and transmitted in various fields. So, many encryption methods for multimedia content have been put forward to satisfy various applications. However, there are still some open issues. Each encryption method has its advantages and drawbacks. Our main goal is expected to provide a solution for multimedia encryption which satisfies the target application constraints and performs metrics of the encryption algorithm. The Advanced Encryption Standard (AES) is the most popular algorithm used in symmetric key cryptography. Furthermore, chaotic encryption is a new research direction of cryptography which is characterized by high initial-value sensitivity and good randomness. In this paper we propose a hybrid video cryptosystem which combines two encryption techniques. The proposed cryptosystem realizes the video encryption through the chaos and AES in CTR mode. Experimental results and security analysis demonstrate that this cryptosystem is highly efficient and a robust system for video encryption.

Pritchard, S. W., Hancke, G. P., Abu-Mahfouz, A. M..  2017.  Security in software-defined wireless sensor networks: Threats, challenges and potential solutions. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). :168–173.
A Software-Defined Wireless Sensor Network (SD-WSN) is a recently developed model which is expected to play a large role not only in the development of the Internet of Things (IoT) paradigm but also as a platform for other applications such as smart water management. This model makes use of a Software-Defined Networking (SDN) approach to manage a Wireless Sensor Network (WSN) in order to solve most of the inherent issues surrounding WSNs. One of the most important aspects of any network, is security. This is an area that has received little attention within the development of SDWSNs, as most research addresses security concerns within SDN and WSNs independently. There is a need for research into the security of SDWSN. Some concepts from both SDN and WSN security can be adjusted to suit the SDWSN model while others cannot. Further research is needed into consolidating SDN and WSN security measures to consider security in SDWSN. Threats, challenges and potential solutions to securing SDWSN are presented by considering both the WSN and SDN paradigms.
Rubin, S. H., Grefe, W. K., Bouabana-Tebibel, T., Chen, S. C., Shyu, M. L., Simonsen, K. S..  2017.  Cyber-Secure UAV Communications Using Heuristically Inferred Stochastic Grammars and Hard Real-Time Adaptive Waveform Synthesis and Evolution. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :9–15.
Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar
Shao, S., Tunc, C., Satam, P., Hariri, S..  2017.  Real-Time IRC Threat Detection Framework. 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). :318–323.

Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.

Paramathma, M. K., Devaraj, D., Reddy, B. S..  2016.  Artificial neural network based static security assessment module using PMU measurements for smart grid application. 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS). :1–5.

Power system security is one of the key issues in the operation of smart grid system. Evaluation of power system security is a big challenge considering all the contingencies, due to huge computational efforts involved. Phasor measurement unit plays a vital role in real time power system monitoring and control. This paper presents static security assessment scheme for large scale inter connected power system with Phasor measurement unit using Artificial Neural Network. Voltage magnitude and phase angle are used as input variables of the ANN. The optimal location of PMU under base case and critical contingency cases are determined using Genetic algorithm. The performance of the proposed optimization model was tested with standard IEEE 30 bus system incorporating zero injection buses and successful results have been obtained.

Yap, B. L., Baskaran, V. M..  2016.  Active surveillance using depth sensing technology \#8212; Part I: Intrusion detection. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). :1–2.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Shahrak, M. Z., Ye, M., Swaminathan, V., Wei, S..  2016.  Two-way real time multimedia stream authentication using physical unclonable functions. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). :1–4.

Multimedia authentication is an integral part of multimedia signal processing in many real-time and security sensitive applications, such as video surveillance. In such applications, a full-fledged video digital rights management (DRM) mechanism is not applicable due to the real time requirement and the difficulties in incorporating complicated license/key management strategies. This paper investigates the potential of multimedia authentication from a brand new angle by employing hardware-based security primitives, such as physical unclonable functions (PUFs). We show that the hardware security approach is not only capable of accomplishing the authentication for both the hardware device and the multimedia stream but, more importantly, introduce minimum performance, resource, and power overhead. We justify our approach using a prototype PUF implementation on Xilinx FPGA boards. Our experimental results on the real hardware demonstrate the high security and low overhead in multimedia authentication obtained by using hardware security approaches.

Yu, F., Chen, L., Zhang, H..  2016.  Virtual TPM Dynamic Trust Extension Suitable for Frequent Migrations. 2016 IEEE Trustcom/BigDataSE/ISPA. :57–65.

This paper has presented an approach of vTPM (virtual Trusted Platform Module) Dynamic Trust Extension (DTE) to satisfy the requirements of frequent migrations. With DTE, vTPM is a delegation of the capability of signing attestation data from the underlying pTPM (physical TPM), with one valid time token issued by an Authentication Server (AS). DTE maintains a strong association between vTPM and its underlying pTPM, and has clear distinguishability between vTPM and pTPM because of the different security strength of the two types of TPM. In DTE, there is no need for vTPM to re-acquire Identity Key (IK) certificate(s) after migration, and pTPM can have a trust revocation in real time. Furthermore, DTE can provide forward security. Seen from the performance measurements of its prototype, DTE is feasible.

Tan, B., Biglari-Abhari, M., Salcic, Z..  2016.  A system-level security approach for heterogeneous MPSoCs. 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP). :74–81.

Embedded systems are becoming increasingly complex as designers integrate different functionalities into a single application for execution on heterogeneous hardware platforms. In this work we propose a system-level security approach in order to provide isolation of tasks without the need to trust a central authority at run-time. We discuss security requirements that can be found in complex embedded systems that use heterogeneous execution platforms, and by regulating memory access we create mechanisms that allow safe use of shared IP with direct memory access, as well as shared libraries. We also present a prototype Isolation Unit that checks memory transactions and allows for dynamic configuration of permissions.