Fahimeh Rezaei

LTE PHY performance analysis under 3GPP standards parameters

Long Term Evolution (LTE) has been introduced by 3GPP (3rd Generation Partnership Project) and is poised to dominate the 4th generation (4G) of mobile telecommunication networks. In this article, we present an in-depth analysis of the LTE physical layers characteristics and its performance. Our work is unique in providing a detailed performance study based on Release 8 of the 3GPP standard. Several works have discussed the LTE performance; however, most have been restricted to limited scenarios. Our work shows a more comprehensive investigation of the maximum data throughput under different conditions and scenarios. Our performance study includes TDD and FDD operational modes for uplink and downlink transmissions in different antenna diversity schemes, data modulation, and code rates. Our results show that LTE (3GPP-Release 8) supports downlink throughputs of up to 300Mbps and for the uplink a throughput of up to 75 Mbps.

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.

Analysis and evaluation of covert channels over LTE advanced

In this paper, we have investigated Covert Channels in 3GPP LTE-A (Long Term Evolution-Advanced) for identifying headers and extension fields where covert data can be hidden in covert communications. We also discuss the covert channel capacity for LTE-A where the specific fields in the headers of the MAC (Medium Access Control), RLC (Radio Link Control) and PDCP (Packet Data Convergence Protocol) layers are analyzed. We review different scenarios of channel coding rate, modulation schemes and channel bandwidths and present our results.

Disrupting and Preventing Late-Packet Covert Communication Using Sequence Number Tracking

Modern covert channel communication is the art of hiding secret information in legitimate network traffic in a way that cannot normally be detected by anyone other than the intended receiver. It is growing in its presence and sophistication. This type of communication enables the distribution of malicious or sensitive information and poses a significant network security problem to individuals, organizations, and governments. One popular method of covert communication in RTP streams is the transmission of one or more packets after significantly delaying them. As a result, any normal receiver will discard them as arriving late, whereas covert receivers successfully receive them to extract their payload subverted by the covert transmitter. This provides a covert channel method with significant throughput potential and thus high risk. In this paper we propose a method that can restrict this type of covert communication and prevent the distribution of secret information. Our proposed method takes advantage of buffering the sequence number of the received packets and thus detecting late packets, allowing it to discard them instead of delivering them to the receiver. Therefore, the covert receiver will not be able to intercept and observe these intentionally delayed packets, nor extracting the covert message. The in-depth analysis and our simulation results demonstrate that the proposed method is effective and capable of preventing this type of covert communication.

A comprehensive performance analysis of LTE and Mobile WiMAX

LTE (3GPP Release8) and Mobile WiMAX (IEEE 802.16e) are discussed extensively as choices for the next generation of mobile broadband technologies. In this paper, LTE and Mobile WiMAX are analyzed comprehensively from a physical layer performance point of view for different antenna diversity modes in downlink and uplink transmissions. The objective of this study is to fill the current gaps in published literature for comparative in-depth analysis of the performance of LTE and Mobile WiMAX. Our study considers maximum channel bandwidth and the least control information overhead for both protocols. Performance comparison results for different scenarios of TDD (Time Division Duplex) operation in similar configurations suggest that LTE in general outperforms Mobile WiMAX. The throughput comparison of FDD (Frequency Division Duplex) and TDD operations is also presented in this paper to provide a complete discussion of physical layer maximum throughput in LTE.

A Survey of Recent Trends in Wireless Communication Standards, Routing Protocols, and Energy Harvesting Techniques in E-Health Applications

One of the most rapidly growing technology areas is the advances in sensing, networking, and miniaturization in medical domain, which enables innovative new applications. This is especially apparent in e-Health and telemedicine. There is an enormous demand for innovation in wireless sensor networking, body area networks, network security and routing, and many other areas, attracting the attention of numerous researchers. With all the advances it can be challenging to identify trends and areas with opportunities for research engagement. In this paper, the authors therefore review the state-of-the-art in wireless communication used in telemedicine and e-Health applications-ranging from the Wide Area Networks to Body Area Networks-and discuss the studies and literature that employ these technologies for e-Health applications. Moreover, recent routing protocols and techniques that are used for Body Area Networks are investigated. One key challenge for e-Health applications, particularly for mobile or patient-worn devices, is energy consumption and supply. One possible solution is found in energy harvesting, and our survey encompasses current challenges and accomplishments in its application to e-Health and discuss various promising techniques.

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.

Towards a Reliable Detection of Covert Timing Channels over Real-Time Network Traffic

Inter-packet delays (IPD) of legitimate network traffic can be exploited for information hiding purposes and distribution of secret and sensitive data. This process is known as Covert Timing Channel (CTC), which is usually used for malicious purposes. In this paper we propose a novel approach, CTC Real-Time Detection (CTCRTD) to detect such activities based on IPD distributions of network traffic. We present and leverage three different non-parametric statistical tests that can be used to generate distinct statistical test scores for overt and covert traffic IPDs. Our new detection approach is designed around two major benefits: First, the new detection approach can detect various CTC algorithms that have similar impact on network traffic IPD distributions. Second, our detection approach reliably detects covert communication over real-time network traffic with minimal lag between the start of covert activity and the point of detection. We have evaluated and verified the reliability and effectiveness of our detection approach utilizing a large number of overt and covert traffic streams and various scenarios of the proposed detection technique. The obtained results show that the new detection approach can precisely differentiate between overt and covert network traffic and detect covert communication activities over 90 percent of time on average.

A novel automated framework for modeling and evaluating covert channel algorithms

In this paper, we introduce an automated framework that can model covert communication algorithms. Our proposed framework, Automated Covert Channel Modeling ACCM, can provide a reliable and valid implementation method for fully functional covert communication techniques that can be used to study various concepts, characteristics and features of covert communication. ACCM exploits the shared features of covert channel algorithms to define common blocks and automatically converts tasks and events of the common blocks to executable codes. In order to verify the accuracy of our proposed ACCM framework, we implement two different covert channel algorithms that are referenced repeatedly in literature. We demonstrate that the characteristics obtained from implementing covert communication using the ACCM framework match the theoretical concepts of network communication and the expected results. Copyright

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.