Pradhumna Lal Shrestha

Assurance of Energy Efficiency and Data Security for ECG Transmission in BASNs

With the technological advancement in body area sensor networks (BASNs), low cost high quality electrocardiographic (ECG) diagnosis systems have become important equipment for healthcare service providers. However, energy consumption and data security with ECG systems in BASNs are still two major challenges to tackle. In this study, we investigate the properties of compressed ECG data for energy saving as an effort to devise a selective encryption mechanism and a two-rate unequal error protection (UEP) scheme. The proposed selective encryption mechanism provides a simple and yet effective security solution for an ECG sensor-based communication platform, where only one percent of data is encrypted without compromising ECG data security. This part of the encrypted data is essential to ECG data quality due to its unequally important contribution to distortion reduction. The two-rate UEP scheme achieves a significant additional energy saving due to its unequal investment of communication energy to the outcomes of the selective encryption, and thus, it maintains a high ECG data transmission quality. Our results show the improvements in communication energy saving of about 40%, and demonstrate a higher transmission quality and security measured in terms of wavelet-based weighted percent root-mean-squared difference.

A study on energy efficient multi-tier multi-hop wireless sensor networks for freight-train monitoring

The North American freight railroad industry is trying to leverage wireless sensor networks (WSN) onboard railcars for advanced monitoring and alerting. In railroad environments, freight train WSNs exhibit a linear chain-like topology of significant length. Thus, existing wireless technologies such as the IEEE 802.15.4 communication protocol, based on a star topology, are unable to provide reliable service. The end-to-end communication between nodes generally relies on individual nodes communicating with their respective neighbors to carry the information over multiple hops and deliver it to the preferred destination. The routing performance and reliability significantly degrades with increasing number of hops. We proposed a multitier multi-hop network which is designed to overcome these issues in large-scale multi-hop WSNs in railroad environments. This approach has significant advantages, such as more data bandwidth, higher reliability, and lower energy consumption. Our analytical results show that the proposed multi-tier communication approach spends energy more efficiently and utilizes less resource than the traditional chain topology onboard freight trains.

A Support Vector Machine-Based Framework for Detection of Covert Timing Channels

Covert channels exploit side channels within existing network resources to transmit secret messages. They are integrated into the elements of network resources that were not even designed for the purpose of communication. This means that traditional security features like firewalls cannot detect them. Their ability to evade detection makes covert channels a grave security concern. Hence, it is imperative to detect and disrupt them. However, a generic mechanism that can be used to detect a large variety of covert channels is missing. In this paper, we propose a support vector machine (SVM)-based framework for reliable detection of covert communications. The machine learning framework utilizes the fingerprints derived from the traffic under investigation to classify the traffic as covert or overt. We trained our classifier using the fingerprints from four popular and diverse covert timing channel algorithms and tested each of them independently. We have shown that the machine learning framework has great potential to blindly detect covert channels, even when the covert message size is reduced.

Performance Analysis for Direction of Arrival Estimating Algorithms

Smart antennas have emerged as one of the most promising directions in supporting maximum communication link throughput. In this paper, we have investigated the impact of smart antennas on a complex mobile network such as a railroad wireless communications system. The objective is to analyze the selection of a Direction-Of-Arrival (DOA) estimation algorithm which provides the maximum efficiency when deployed in our railroad testbeds for wireless vehicular communication. Our findings are discussed to provide an in-depth understanding of how different algorithms should be selected to support efficient network operations.

Achieving robustness and capacity gains in covert timing channels

In this paper, we introduce a covert timing channel (CTC) algorithm and compare it to one of the most prevailing CTC algorithms, originally proposed by Cabuk et al. CTC is a form of covert channels - methods that exploit network activities to transmit secret data over packet-based networks - by modifying packet timing. This algorithm is a seminal work, one of the most widely cited CTCs, and the foundation for many CTC research activities. In order to overcome some of the disadvantages of this algorithm we introduce a covert timing channel technique that leverages timeout thresholds. The proposed algorithm is compared to the original algorithm in terms of channel capacity, impact on overt traffic, bit error rates, and latency. Based on our simulation results the proposed algorithm outperforms the work from Cabuk et al., especially in terms of its higher covert data transmission rate with lower latency and fewer bit errors. In our work we also address the desynchronization problem found in Cabuk et al.s algorithm in our simulation results and show that even in the case of the synchronization-corrected Cabuk et al. algorithm our proposed method provides better results in terms of capacity and latency.

Leveraging Statistical Feature Points for Generalized Detection of Covert Timing Channels

Covert channels exploit network resources never intended for the purpose of communication in order to transfer messages undetectable by conventional security measures like intrusion detection systems and firewalls. Since covert communication provides a means to secretly transfer messages they pose a grave cyber security threat. Most research in detecting covert timing channels are focused on detecting a specific type of covert channel implementation and cannot be generalized to detect all covert channels. The most notable work in universal detection was published by Gianvecchio et al. In 2011. They evaluated the corrected conditional entropy (CCE) of the interpacket arrival time and then built a classifier based on those measurements. However, we show in this paper that the CCE fails to detect covert communications when the size of the covert message is short. Furthermore, we also show that it is not possible to train the classifier using these short covert messages, as the CCE is a parameter based on the statistical distribution of traffic, and smaller traffic samples may not adequately reflect the properties of the whole population. We also show that the variance of the CCE remains as a potential parameter for detecting covert traffic. Furthermore, we introduce the autocorrelation function of the traffic channel as an additional statistical parameter for detecting covert channels. Finally, we propose building an SVM (Support Vector Machine) classifier system using these parameters as the feature points for reliable and generalized detection of covert channels, which we show to have superior performance.

Multi-tier multi-hop routing in large-scale wireless sensor networks for real-time monitoring

Our research team at the University of Nebraskas Advanced Telecommunications Engineering Lab (TEL) is working closely with the North American freight railroad industry to leverage wireless sensor networks (WSN) onboard railcars for advanced monitoring and alerting. Because freight train WSNs exhibit a linear chain-like topology of significant length, the existing IEEE 802.15.4 communication protocol, based on a star topology, is unable to provide acceptable service. The routing performance and reliability degrades significantly with increasing number of hops. The proposed multi-tier multi-hop network is a practical solution for large-scale multi-hop WSNs in railroad environments. The idea is to have longer-distance communication overlaid on the top of the shorter-distance communication of WSNs to reduce the number of hops needed for route discovery and data forwarding. This approach has significant advantages, such as more data bandwidth and higher reliability. Our simulation results show that our proposed multi-tier communication approach is a feasible and reliable solution for implementing chain-topology WSNs with a high number of hops onboard freight trains.

Detecting covert timing channels using non-parametric statistical approaches

Extensive availability and development of Internet applications and services open up the opportunity for abusing network and Internet resources to distribute malicious data and leak sensitive information. One of the prevalent information-hiding approaches suitable for such activities is known as Covert Timing Channel (CTC), which utilizes the modulation of Inter-Packet Delays (IPDs) to embed secret data and transfers that to designated receivers. In this paper, we propose two different non-parametric statistical tests that can be employed to detect this type of covert communication activities over a network. The new detection metrics are evaluated and verified against four different and highly recognized CTC algorithms. The experimental results show that the proposed detection metrics can reliably and effectively distinguish between the covert and overt traffic flows, thus significantly supporting our research toward an accurate blind and comprehensive CTC detection. This is a capability vital to cyber security in todays information society.

A quality-preserving hidden information removal approach for digital images

In this paper, we introduce a novel algorithm to destroy the steganographic information embedded in an image without changing the quality of the image and with no prior knowledge of the used steganography scheme. We propose the new Neighbor Class Displacement (NCD) algorithm, which arranges the pixels of an image into a given number of classes. The elements of two specific classes are substituted with each other based on different conditions related to the content of the class elements. For evaluating the effectiveness of our attack, we apply NCD to different steganographically modified images to remove the embedded hidden information. Our results show that over 40% of the steganography bits are toggled in natural images by our proposed attack algorithm, which means the hidden information is removed effectively. Additionally, the visual quality of the images does not change and the PSNR of the original and attacked images is above 32 dB. We compare our attack to other signal processing and geometrical attacks and show that our NCD scheme outperforms other steganography attack algorithms while maintaining the quality of the host image.

Low-complexity image coder/decoder with an approaching-entropy quad-tree search code for embedded computing platforms

In this paper, we propose a fast, simple and efficient image codec applicable for embedded processing systems. Among the existing image coding methods, wavelet quad-tree is a foundation leading to an efficient structure to encode images. By searching significant coefficients along quadtrees, an embedded efficient code can be obtained. In this work, we exploit hierarchical relations of the quad-tree structure in terms of searching entropy and present a quadtree searching model that is very close to the searching entropy. By applying this model, our codec surpasses SPIHT [1] by 0.2–0.4 db over wide code rates, and its performance is comparable to SPIHT with arithmetic coding and JPEG2000 [2]. With no additional overhead of arithmetic coding, our code is much faster and simpler than SPIHT with adaptive arithmetic coding and the more complicated JPEG2000 algorithms. This is a critical factor sought in embedded processing in communication systems where energy consumption and speed are priority concerns. Our simulation results demonstrate that the proposed codec is about twice as fast with very low computational overheads and comparable coding performances than existing algorithms.