JTAG Security System Based on Credentials

JTAG (Joint Test Access Group) is a powerful tool for the embedded system development environments. The features of JTAG, however, can be exploited by malicious users as a backdoor for launching attacks, an approach which now constitutes a major threat in the domain of device hacking. To deny unauthenticated users access to the features of JTAG port, this paper proposes a novel JTAG security mechanism. The proposed solution uses authentication based on credentials to achieve improved security and usability over existing solutions. Our approach is easily applicable to all standard JTAG environments because its structure is designed to be independent from the application environment. Further, the approach has lower implementation cost than encryption/decryption-based solutions since only hash and XOR calculations are employed in its authentication protocol. The security of the proposed mechanism has been verified through analysis against all forms of expected attacks, and its functionality is demonstrated with a real-life implementation.     

A Cross-layer Dual Queue Approach for Improving TCP Fairness in Infrastructure WLANs

Fairness is one of the most important performance measures in IEEE 802.11 Wireless Local Area Networks (WLANs), where channel is accessed through competition. In this paper, we focus on the fairness problem between TCP uplink and downlink flows in infrastructure WLANs from the cross-layer perspective. First, we show that there exists a notable discrepancy between throughput of uplink flow and that of downlink flow, and discuss its root cause from the standpoint of different responses to TCP data packet drop and TCP ACK packet drop at the access point (AP) buffer. In order to mitigate this unfairness, we propose a dual queue scheme, which works in a cross-layer manner. It employs two separate queues at the AP, one for the data packets of downlink TCP flows and another for the ACK packets of uplink TCP flows, and selects these queues with appropriate probabilities so that TCP per-flow fairness is improved. Moreover, we analyze the behavior of the dual queue scheme and derive throughputs of uplink and downlink flows. Based on this analysis, we obtain the optimal queue selection probabilities for fairness. Extensive simulation results confirm that the proposed scheme is effective and useful in resolving the TCP unfairness problem without deteriorating overall utilization.     

Ad Hoc LTE Method for Resilient Smart Grid Communications

Wireless Personal Communications - LTE network is a good choice for delivering smart grid demand response (DR) traffic. However, LTE connectivity is not pervasively available due to smart meter...     

Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization

Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many challenging financial forecasting tasks, it is not always straightforward to employ DL-based approaches for highly volatile and non-stationary time financial series. To this end, in this paper, an adaptive input normalization layer that can learn to identify the distribution from which the input data were generated and then apply the most appropriate normalization scheme is proposed. This allows for promptly adapting the input to the subsequent DL model, which can be especially important, given recent findings that hint at the existence of critical learning periods in neural networks. Furthermore, the proposed method operates on a sliding window over the time series allowing for overcoming non-stationary issues that often arise. It is worth noting that the main difference with existing approaches is that the proposed method does not just learn to perform static normalization, e.g., using a fixed set of parameters, but instead it adaptively calculates the most appropriate normalization parameters, significantly improving the robustness of the proposed approach when distribution shifts occur. The effectiveness of the proposed formulation is verified using extensive experiments on three challenging financial time-series datasets.

     

A decision-theoretic approach for model interpretability in Bayesian framework

Machine Learning - A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure...     

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the structures in the data. Most of the current clustering algorithms produce a clustering structure even on data that do not possess such structure. In these cases, the algorithms force a structure in the data instead of discovering one. To avoid false structures in the relations of data, a novel clusterability assessment method called density-based clusterability measure is proposed in this paper. It measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningful insight to the relationships in the data. This is especially useful in time-series data since visualizing the structure in time-series data is hard. The performance of the clusterability measure is evaluated against several synthetic data sets and time-series data sets, which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.      

Damping properties of austenitic stainless steels containing strain-induced martensite

Damping properties of two austenitic stainless steel grades, EN 1.4318 and EN 1.4301, were investigated. The test materials were cold rolled to different reductions and damping capacity was measured as a function of temperature with an internal friction method. Microstructures of the test materials were studied by means of X-ray diffraction (XRD) and magnetic measurements. The results showed that damping capacity of the studied materials depended on the amounts of strain-induced ε- and α′-martensite phases. At temperatures around 0 °C, highest damping capacity was achieved with cold rolling reduction of 10 to 15 pct. This behavior is related to the existence of ε-martensite and stacking faults. Internal friction peak due to α′-martensite phase was present at the temperature of 130 °C. Strain aging heat treatment at 200 °C for 20 minutes decreased the damping capacity in the entire studied temperature range.