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Zhang, L., Shen, X., Zhang, F., Ren, M., Ge, B., Li, B..  2019.  Anomaly Detection for Power Grid Based on Time Series Model. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :188—192.

In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.

Huang, Y., Wang, Y..  2019.  Multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band variance. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :243—251.

In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.

Hassan, S. U., Khan, M. Zeeshan, Khan, M. U. Ghani, Saleem, S..  2019.  Robust Sound Classification for Surveillance using Time Frequency Audio Features. 2019 International Conference on Communication Technologies (ComTech). :13—18.

Over the years, technology has reformed the perception of the world related to security concerns. To tackle security problems, we proposed a system capable of detecting security alerts. System encompass audio events that occur as an outlier against background of unusual activity. This ambiguous behaviour can be handled by auditory classification. In this paper, we have discussed two techniques of extracting features from sound data including: time-based and signal based features. In first technique, we preserve time-series nature of sound, while in other signal characteristics are focused. Convolution neural network is applied for categorization of sound. Major aim of research is security challenges, so we have generated data related to surveillance in addition to available datasets such as UrbanSound 8k and ESC-50 datasets. We have achieved 94.6% accuracy for proposed methodology based on self-generated dataset. Improved accuracy on locally prepared dataset demonstrates novelty in research.

Han, Y., Zhang, W., Wei, J., Liu, X., Ye, S..  2019.  The Study and Application of Security Control Plan Incorporating Frequency Stability (SCPIFS) in CPS-Featured Interconnected Asynchronous Grids. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :349—354.

The CPS-featured modern asynchronous grids interconnected with HVDC tie-lines facing the hazards from bulk power imbalance shock. With the aid of cyber layer, the SCPIFS incorporates the frequency stability constrains is put forwarded. When there is bulk power imbalance caused by HVDC tie-lines block incident or unplanned loads increasing, the proposed SCPIFS ensures the safety and frequency stability of both grids at two terminals of the HVDC tie-line, also keeps the grids operate economically. To keep frequency stability, the controllable variables in security control strategy include loads, generators outputs and the power transferred in HVDC tie-lines. McCormick envelope method and ADMM are introduced to solve the proposed SCPIFS optimization model. Case studies of two-area benchmark system verify the safety and economical benefits of the SCPFS. HVDC tie-line transferred power can take the advantage of low cost generator resource of both sides utmost and avoid the load shedding via tuning the power transferred through the operating tie-lines, thus the operation of both connected asynchronous grids is within the limit of frequency stability domain.

Geng, J., Yu, B., Shen, C., Zhang, H., Liu, Z., Wan, P., Chen, Z..  2019.  Modeling Digital Low-Dropout Regulator with a Multiple Sampling Frequency Circuit Technology. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :207—210.

The digital low dropout regulators are widely used because it can operate at low supply voltage. In the digital low drop-out regulators, the high sampling frequency circuit has a short setup time, but it will produce overshoot, and then the output can be stabilized; although the low sampling frequency circuit output can be directly stabilized, the setup time is too long. This paper proposes a two sampling frequency circuit model, which aims to include the high and low sampling frequencies in the same circuit. By controlling the sampling frequency of the circuit under different conditions, this allows the circuit to combine the advantages of the circuit operating at different sampling frequencies. This shortens the circuit setup time and the stabilization time at the same time.

Fujiwara, N., Shimasaki, K., Jiang, M., Takaki, T., Ishii, I..  2019.  A Real-time Drone Surveillance System Using Pixel-level Short-time Fourier Transform. 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). :303—308.

In this study we propose a novel method for drone surveillance that can simultaneously analyze time-frequency responses in all pixels of a high-frame-rate video. The propellers of flying drones rotate at hundreds of Hz and their principal vibration frequency components are much higher than those of their background objects. To separate the pixels around a drone's propellers from its background, we utilize these time-series features for vibration source localization with pixel-level short-time Fourier transform (STFT). We verify the relationship between the number of taps in the STFT computation and the performance of our algorithm, including the execution time and the localization accuracy, by conducting experiments under various conditions, such as degraded appearance, weather, and defocused blur. The robustness of the proposed algorithm is also verified by localizing a flying multi-copter in real-time in an outdoor scenario.

Kousri, M. R., Deniau, V., Gransart, C., Villain, J..  2019.  Optimized Time-Frequency Processing Dedicated to the Detection of Jamming Attacks on Wi-Fi Communications. 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC). :1—4.

Attacks by Jamming on wireless communication network can provoke Denial of Services. According to the communication system which is affected, the consequences can be more or less critical. In this paper, we propose to develop an algorithm which could be implemented at the reception stage of a communication terminal in order to detect the presence of jamming signals. The work is performed on Wi-Fi communication signals and demonstrates the necessity to have a specific signal processing at the reception stage to be able to detect the presence of jamming signals.

Li, J., Liu, H., Wu, J., Zhu, J., Huifeng, Y., Rui, X..  2019.  Research on Nonlinear Frequency Hopping Communication Under Big Data. 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). :349—354.

Aiming at the problems of poor stability and low accuracy of current communication data informatization processing methods, this paper proposes a research on nonlinear frequency hopping communication data informatization under the framework of big data security evaluation. By adding a frequency hopping mediation module to the frequency hopping communication safety evaluation framework, the communication interference information is discretely processed, and the data parameters of the nonlinear frequency hopping communication data are corrected and converted by combining a fast clustering analysis algorithm, so that the informatization processing of the nonlinear frequency hopping communication data under the big data safety evaluation framework is completed. Finally, experiments prove that the research on data informatization of nonlinear frequency hopping communication under the framework of big data security evaluation could effectively improve the accuracy and stability.

Ma, X., Sun, X., Cheng, L., Guo, X., Liu, X., Wang, Z..  2019.  Parameter Setting of New Energy Sources Generator Rapid Frequency Response in Northwest Power Grid Based on Multi-Frequency Regulation Resources Coordinated Controlling. 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP). :218—222.
Since 2016, the northwest power grid has organized new energy sources to participate in the rapid frequency regulation research and carried out pilot test work at the sending end large power grid. The experimental results show that new energy generator has the ability to participate in the grid's rapid frequency regulation, and its performance is better than that of conventional power supply units. This paper analyses the requirements for fast frequency control of the sending end large power grid in northwest China, and proposes the segmented participation indexes of photovoltaic and wind power in the frequency regulation of power grids. In accordance with the idea of "clear responsibilities, various types of unit coordination", the parameter setting of new energy sources rapid frequency regulation is completed based on the coordinated control based on multi-frequency regulation resources in northwest power grid. The new energy fast frequency regulation model was established, through the PSASP power grid stability simulation program and the large-scale power grid stability simulation analysis was completed. The simulation results show that the wind power and photovoltaic adopting differential rapid frequency regulation parameters can better utilize the rapid frequency regulation capability of various types of power sources, realize the coordinated rapid frequency regulation of all types of units, and effectively improve the frequency security prevention and control level of the sending end large power grid.
Abratkiewicz, K., Gromek, D., Samczynski, P..  2019.  Chirp Rate Estimation and micro-Doppler Signatures for Pedestrian Security Radar Systems. 2019 Signal Processing Symposium (SPSympo). :212—215.

A new approach to micro-Doppler signal analysis is presented in this article. Novel chirp rate estimators in the time-frequency domain were used for this purpose, which provided the chirp rate of micro-Doppler signatures, allowing the classification of objects in the urban environment. As an example verifying the method, a signal from a high-resolution radar with a linear frequency modulated continuous wave (FMCW) recording an echo reflected from a pedestrian was used to validate the proposed algorithms for chirp rate estimation. The obtained results are plotted on saturated accelerograms, giving an additional parameter dedicated for target classification in security systems utilizing radar sensors for target detection.

Kostyria, O., Storozhenko, V., Naumenko, V., Romanov, Y..  2018.  Mathematical Models of Blocks for Compensation Multipath Distortion in Spatially Separated Passive Time-Frequency Synchronization Radio System. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S T). :104-108.

Multipath propagation of radio waves negatively affects to the performance of telecommunications and radio navigation systems. When performing time and frequency synchronization tasks of spatially separated standards, the multipath signal propagation aggravates the probability of a correct synchronization and introduces an error. The presence of a multipath signal reduces the signal-to-noise ratio in the received signal, which in turn causes an increase in the synchronization error. If the time delay of the additional beam (s) is less than the useful signal duration, the reception of the useful signal is further complicated by the presence of a partially correlated interference, the level and correlation degree of which increases with decreasing time delay of the interference signals. The article considers with the method of multi-path interference compensation in a multi-position (telecommunication or radio navigation system) or a time and frequency synchronization system for the case if at least one of the receiving positions has no noise signal or does not exceed the permissible level. The essence of the method is that the interference-free useful signal is transmitted to other points in order to pick out the interference component from the signal / noise mix. As a result, an interference-free signal is used for further processing. The mathematical models of multipath interference suppressors in the temporal and in the frequency domain are presented in the article. Compared to time processing, processing in the frequency domain reduces computational costs. The operation of the suppressor in the time domain has been verified experimentally.

Yazicigil, R. T., Nadeau, P., Richman, D., Juvekar, C., Vaidya, K., Chandrakasan, A. P..  2018.  Ultra-Fast Bit-Level Frequency-Hopping Transmitter for Securing Low-Power Wireless Devices. 2018 IEEE Radio Frequency Integrated Circuits Symposium (RFIC). :176-179.

Current BLE transmitters are susceptible to selective jamming due to long dwell times in a channel. To mitigate these attacks, we propose physical-layer security through an ultra-fast bit-level frequency-hopping (FH) scheme by exploiting the frequency agility of bulk acoustic wave resonators (BAW). Here we demonstrate the first integrated bit-level FH transmitter (TX) that hops at 1$μ$s period and uses data-driven random dynamic channel selection to enable secure wireless communications with additional data encryption. This system consists of a time-interleaved BAW-based TX implemented in 65nm CMOS technology with 80MHz coverage in the 2.4GHz ISM band and a measured power consumption of 10.9mW from 1.1V supply.

Park, Jungmin, Xu, Xiaolin, Jin, Yier, Forte, Domenic, Tehranipoor, Mark.  2018.  Power-Based Side-Channel Instruction-Level Disassembler. Proceedings of the 55th Annual Design Automation Conference. :119:1-119:6.
Modern embedded computing devices are vulnerable against malware and software piracy due to insufficient security scrutiny and the complications of continuous patching. To detect malicious activity as well as protecting the integrity of executable software, it is necessary to monitor the operation of such devices. In this paper, we propose a disassembler based on power-based side-channel to analyze the real-time operation of embedded systems at instruction-level granularity. The proposed disassembler obtains templates from an original device (e.g., IoT home security system, smart thermostat, etc.) and utilizes machine learning algorithms to uniquely identify instructions executed on the device. The feature selection using Kullback-Leibler (KL) divergence and the dimensional reduction using PCA in the time-frequency domain are proposed to increase the identification accuracy. Moreover, a hierarchical classification framework is proposed to reduce the computational complexity associated with large instruction sets. In addition, covariate shifts caused by different environmental measurements and device-to-device variations are minimized by our covariate shift adaptation technique. We implement this disassembler on an AVR 8-bit microcontroller. Experimental results demonstrate that our proposed disassembler can recognize test instructions including register names with a success rate no lower than 99.03% with quadratic discriminant analysis (QDA).
Jourdan, Théo, Boutet, Antoine, Frindel, Carole.  2018.  Toward Privacy in IoT Mobile Devices for Activity Recognition. Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. :155-165.
Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.
Keshishzadeh, Sarineh, Fallah, Ali, Rashidi, Saeid.  2018.  Electroencephalogram Based Biometrics: A Fractional Fourier Transform Approach. Proceedings of the 2018 2Nd International Conference on Biometric Engineering and Applications. :1-5.
The non-stationary nature of the human Electroencephalogram (EEG) has caused problems in EEG based biometrics. Stationary signals analysis is done simply with Discrete Fourier Transform (DFT), while it is not possible to analyze non-stationary signals with DFT, as it does not have the ability to show the occurrence time of different frequency components. The Fractional Fourier Transform (FrFT), as a generalization of Fourier Transform (FT), has the ability to exhibit the variable frequency nature of non-stationary signals. In this paper, Discrete Fractional Fourier Transform (DFrFT) with different fractional orders is proposed as a novel feature extraction technique for EEG based human verification with different number of channels. The proposed method in its' best performance achieved 0.22% Equal Error Rate (EER) with three EEG channels of 104 subjects.
Zhang, Sheng, Tang, Adrian, Jiang, Zhewei, Sethumadhavan, Simha, Seok, Mingoo.  2018.  Blacklist Core: Machine-Learning Based Dynamic Operating-Performance-Point Blacklisting for Mitigating Power-Management Security Attacks. Proceedings of the International Symposium on Low Power Electronics and Design. :5:1-5:6.
Most modern computing devices make available fine-grained control of operating frequency and voltage for power management. These interfaces, as demonstrated by recent attacks, open up a new class of software fault injection attacks that compromise security on commodity devices. CLKSCREW, a recently-published attack that stretches the frequency of devices beyond their operational limits to induce faults, is one such attack. Statically and permanently limiting frequency and voltage modulation space, i.e., guard-banding, could mitigate such attacks but it incurs large performance degradation and long testing time. Instead, in this paper, we propose a run-time technique which dynamically blacklists unsafe operating performance points using a neural-net model. The model is first trained offline in the design time and then subsequently adjusted at run-time by inspecting a selected set of features such as power management control registers, timing-error signals, and core temperature. We designed the algorithm and hardware, titled a BlackList (BL) core, which is capable of detecting and mitigating such power management-based security attack at high accuracy. The BL core incurs a reasonably small amount of overhead in power, delay, and area.
Lakshminarayana, Subhash, Karachiwala, Jabir Shabbir, Chang, Sang-Yoon, Revadigar, Girish, Kumar, Sristi Lakshmi Sravana, Yau, David K.Y., Hu, Yih-Chun.  2018.  Signal Jamming Attacks Against Communication-Based Train Control: Attack Impact and Countermeasure. Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :160-171.
We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.
Queiroz, Diego V., Gomes, Ruan D., Benavente-Peces, Cesar, Fonseca, Iguatemi E., Alencar, Marcelo S..  2018.  Evaluation of Channels Blacklists in TSCH Networks with Star and Tree Topologies. Proceedings of the 14th ACM International Symposium on QoS and Security for Wireless and Mobile Networks. :116-123.
The Time-Slotted Channel Hopping (TSCH) mode, defined by the IEEE 802.15.4e protocol, aims to reduce the effects of narrowband interference and multipath fading on some channels through the frequency hopping method. To work satisfactorily, this method must be based on the evaluation of the channel quality through which the packets will be transmitted to avoid packet losses. In addition to the estimation, it is necessary to manage channel blacklists, which prevents the sensors from hopping to bad quality channels. The blacklists can be applied locally or globally, and this paper evaluates the use of a local blacklist through simulation of a TSCH network in a simulated harsh industrial environment. This work evaluates two approaches, and both use a developed protocol based on TSCH, called Adaptive Blacklist TSCH (AB-TSCH), that considers beacon packets and includes a link quality estimation with blacklists. The first approach uses the protocol to compare a simple version of TSCH to configurations with different sizes of blacklists in star topology. In this approach, it is possible to analyze the channel adaption method that occurs when the blacklist has 15 channels. The second approach uses the protocol to evaluate blacklists in tree topology, and discusses the inherent problems of this topology. The results show that, when the estimation is performed continuously, a larger blacklist leads to an increase of performance in star topology. In tree topology, due to the simultaneous transmissions among some nodes, the use of smaller blacklist showed better performance.
Amosov, O. S., Amosova, S. G., Muller, N. V..  2018.  Identification of Potential Risks to System Security Using Wavelet Analysis, the Time-and-Frequency Distribution Indicator of the Time Series and the Correlation Analysis of Wavelet-Spectra. 2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). :1-6.

To identify potential risks to the system security presented by time series it is offered to use wavelet analysis, the indicator of time-and-frequency distribution, the correlation analysis of wavelet-spectra for receiving rather complete range of data about the process studied. The indicator of time-and-frequency localization of time series was proposed allowing to estimate the speed of non-stationary changing. The complex approach is proposed to use the wavelet analysis, the time-and-frequency distribution of time series and the wavelet spectra correlation analysis; this approach contributes to obtaining complete information on the studied phenomenon both in numerical terms, and in the form of visualization for identifying and predicting potential system security threats.

Islam, Mohammad A., Ren, Shaolei.  2018.  Ohm's Law in Data Centers: A Voltage Side Channel for Timing Power Attacks. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :146-162.

Maliciously-injected power load, a.k.a. power attack, has recently surfaced as a new egregious attack vector for dangerously compromising the data center availability. This paper focuses on the emerging threat of power attacks in a multi-tenant colocation data center, an important type of data center where multiple tenants house their own servers and share the power distribution system. Concretely, we discover a novel physical side channel –- a voltage side channel –- which leaks the benign tenants' power usage information at runtime and helps an attacker precisely time its power attacks. The key idea we exploit is that, due to the Ohm's Law, the high-frequency switching operation (40\textasciitilde100kHz) of the power factor correction circuit universally built in today's server power supply units creates voltage ripples in the data center power lines. Importantly, without overlapping the grid voltage in the frequency domain, the voltage ripple signals can be easily sensed by the attacker to track the benign tenants' runtime power usage and precisely time its power attacks. We evaluate the timing accuracy of the voltage side channel in a real data center prototype, demonstrating that the attacker can extract benign tenants' power pattern with a great accuracy (correlation coefficient = 0.90+) and utilize 64% of all the attack opportunities without launching attacks randomly or consecutively. Finally, we highlight a few possible defense strategies and extend our study to more complex three-phase power distribution systems used in large multi-tenant data centers.

Huang, Kaiyu, Qu, Y., Zhang, Z., Chakravarthy, V., Zhang, Lin, Wu, Z..  2017.  Software Defined Radio Based Mixed Signal Detection in Spectrally Congested and Spectrally Contested Environment. 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA). :1–6.

In a spectrally congested environment or a spectrally contested environment which often occurs in cyber security applications, multiple signals are often mixed together with significant overlap in spectrum. This makes the signal detection and parameter estimation task very challenging. In our previous work, we have demonstrated the feasibility of using a second order spectrum correlation function (SCF) cyclostationary feature to perform mixed signal detection and parameter estimation. In this paper, we present our recent work on software defined radio (SDR) based implementation and demonstration of such mixed signal detection algorithms. Specifically, we have developed a software defined radio based mixed RF signal generator to generate mixed RF signals in real time. A graphical user interface (GUI) has been developed to allow users to conveniently adjust the number of mixed RF signal components, the amplitude, initial time delay, initial phase offset, carrier frequency, symbol rate, modulation type, and pulse shaping filter of each RF signal component. This SDR based mixed RF signal generator is used to transmit desirable mixed RF signals to test the effectiveness of our developed algorithms. Next, we have developed a software defined radio based mixed RF signal detector to perform the mixed RF signal detection. Similarly, a GUI has been developed to allow users to easily adjust the center frequency and bandwidth of band of interest, perform time domain analysis, frequency domain analysis, and cyclostationary domain analysis.

Nandhini, M., Priya, P..  2017.  A Hybrid Routing Algorithm for Secure Environmental Monitoring System in WSN. 2017 International Conference on Communication and Signal Processing (ICCSP). :1061–1065.

Wireless sensor networks are the most prominent set of recently made sensor nodes. They play a numerous role in many applications like environmental monitoring, agriculture, Structural and industrial monitoring, defense applications. In WSN routing is one of the absolutely requisite techniques. It enhance the network lifetime. This can be gives additional priority and system security by using bio inspired algorithm. The combination of bio inspired algorithms and routing algorithms create a way to easy data transmission and improves network lifetime. We present a new metaheuristic hybrid algorithm namely firefly algorithm with Localizability aided localization routing protocol for encircle monitoring in wireless area. This algorithm entirely covers the wireless sensor area by localization process and clumping the sensor nodes with the use of LAL (Localizability Aided Localization) users can minimize the time latency, packet drop and packet loss compared to traditional methods.

Vasile, D. C., Svasta, P., Codreanu, N., Safta, M..  2017.  Active Tamper Detection Circuit Based on the Analysis of Pulse Response in Conductive Mesh. 2017 40th International Spring Seminar on Electronics Technology (ISSE). :1–6.

Tamper detection circuits provide the first and most important defensive wall in protecting electronic modules containing security data. A widely used procedure is to cover the entire module with a foil containing fine conductive mesh, which detects intrusion attempts. Detection circuits are further classified as passive or active. Passive circuits have the advantage of low power consumption, however they are unable to detect small variations in the conductive mesh parameters. Since modern tools provide an upper leverage over the passive method, the most efficient way to protect security modules is thus to use active circuits. The active tamper detection circuits are typically probing the conductive mesh with short pulses, analyzing its response in terms of delay and shape. The method proposed in this paper generates short pulses at one end of the mesh and analyzes the response at the other end. Apart from measuring pulse delay, the analysis includes a frequency domain characterization of the system, determining whether there has been an intrusion or not, by comparing it to a reference (un-tampered with) spectrum. The novelty of this design is the combined analysis, in time and frequency domains, of the small variations in mesh characteristic parameters.

Liu, Rui, Rawassizadeh, Reza, Kotz, David.  2017.  Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables. Proceedings of the 2017 Workshop on Wearable Systems and Applications. :41–46.

Due to the user-interface limitations of wearable devices, voice-based interfaces are becoming more common; speaker recognition may then address the authentication requirements of wearable applications. Wearable devices have small form factor, limited energy budget and limited computational capacity. In this paper, we examine the challenge of computing speaker recognition on small wearable platforms, and specifically, reducing resource use (energy use, response time) by trimming the input through careful feature selections. For our experiments, we analyze four different feature-selection algorithms and three different feature sets for speaker identification and speaker verification. Our results show that Principal Component Analysis (PCA) with frequency-domain features had the highest accuracy, Pearson Correlation (PC) with time-domain features had the lowest energy use, and recursive feature elimination (RFE) with frequency-domain features had the least latency. Our results can guide developers to choose feature sets and configurations for speaker-authentication algorithms on wearable platforms.

Hawkins, William, Nguyen-Tuong, Anh, Hiser, Jason D., Co, Michele, Davidson, Jack W..  2017.  Mixr: Flexible Runtime Rerandomization for Binaries. Proceedings of the 2017 Workshop on Moving Target Defense. :27–37.

Mixr is a novel moving target defense (MTD) system that improves on the traditional address space layout randomization (ASLR) security technique by giving security architects the tools to add "runtime ASLR" to existing software programs and libraries without access to their source code or debugging information and without requiring changes to the host's linker, loader or kernel. Runtime ASLR systems rerandomize the code of a program/library throughout execution at rerandomization points and with a particular granularity. The security professional deploying the Mixr system on a program/library has the flexibility to specify the frequency of runtime rerandomization and the granularity. For example, she/he can specify that the program rerandomizes itself on 60-byte boundaries every time the write() system call is invoked. The Mixr MTD of runtime ASLR protects binary programs and software libraries that are vulnerable to information leaks and attacks based on that information. Mixr is an improvement on the state of the art in runtime ASLR systems. Mixr gives the security architect the flexibility to specify the rerandomization points and granularity and does not require access to the target program/library's source code, debugging information or other metadata. Nor does Mixr require changes to the host's linker, loader or kernel to execute the protected software. No existing runtime ASLR system offers those capabilities. The tradeoff is that applying the Mixr MTD of runtime ASLR protection requires successful disassembly of a program - something which is not always possible. Moreoever, the runtime overhead of a Mixr-protected program is non-trivial. Mixr, besides being a tool for implementing the MTD of runtime ASLR, has the potential to further improve software security in other ways. For example, Mixr could be deployed to implement noise injection into software to thwart side-channel attacks using differential power analysis.