Defense Against Node Compromise in Sensor Network Security

Recent advances in electronic and computer technologies lead to wide-spread deployment of wireless sensor networks (WSNs). WSNs have wide range applications, including military sensing and tracking, environment monitoring, smart environments, etc. Many WSNs have mission-critical tasks, such as military applications. Thus, the security issues in WSNs are kept in the foreground among research areas. Compared with other wireless networks, such as ad hoc, and cellular networks, security in WSNs is more complicated due to the constrained capabilities of sensor nodes and the properties of the deployment, such as large scale, hostile environment, etc. Security issues mainly come from attacks. In general, the attacks in WSNs can be classified as external attacks and internal attacks. In an external attack, the attacking node is not an authorized participant of the sensor network. Cryptography and other security methods can prevent some of external attacks. However, node compromise, the major and unique problem that leads to internal attacks, will eliminate all the efforts to prevent attacks. Knowing the probability of node compromise will help systems to detect and defend against it. Although there are some approaches that can be used to detect and defend against node compromise, few of them have the ability to estimate the probability of node compromise. Hence, we develop basic uniform, basic gradient, intelligent uniform and intelligent gradient models for node compromise distribution in order to adapt to different application environments by using probability theory. These models allow systems to estimate the probability of node compromise. Applying these models in system security designs can improve system security and decrease the overheads nearly in every security area. Moreover, based on these models, we design a novel secure routing algorithm to defend against the routing security issue that comes from the nodes that have already been compromised but have not been detected by the node compromise detecting mechanism. The routing paths in our algorithm detour those nodes which have already been detected as compromised nodes or have larger probabilities of being compromised. Simulation results show that our algorithm is effective to protect routing paths from node compromise whether detected or not.

Estratégias distribuídas para a coexistência de redes de banda larga sem fio em bandas não-licensiadas

The increasing demand for Internet data traffic in wireless broadband access networks requires both the development of efficient, novel wireless broadband access technologies and the allocation of new spectrum bands for that purpose. The introduction of a great number of small cells in cellular networks allied to the complimentary adoption of Wireless Local Area Network (WLAN) technologies in unlicensed spectrum is one of the most promising concepts to attend this demand. One alternative is the aggregation of Industrial, Science and Medical (ISM) unlicensed spectrum to licensed bands, using wireless networks defined by Institute of Electrical and Electronics Engineers (IEEE) and Third Generation Partnership Project (3GPP). While IEEE 802.11 (Wi-Fi) networks are aggregated to Long Term Evolution (LTE) small cells via LTE / WLAN Aggregation (LWA), in proposals like Unlicensed LTE (LTE-U) and LWA the LTE air interface itself is used for transmission on the unlicensed band. Wi-Fi technology is widespread and operates in the same 5 GHz ISM spectrum bands as the LTE proposals, which may bring performance decrease due to the coexistence of both technologies in the same spectrum bands. Besides, there is the need to improve Wi-Fi operation to support scenarios with a large number of neighbor Overlapping Basic Subscriber Set (OBSS) networks, with a large number of Wi-Fi nodes (i.e. dense deployments). It is long known that the overall Wi-Fi performance falls sharply with the increase of Wi-Fi nodes sharing the channel, therefore there is the need for introducing mechanisms to increase its spectral efficiency. This work is dedicated to the study of coexistence between different wireless broadband access systems operating in the same unlicensed spectrum bands, and how to solve the coexistence problems via distributed coordination mechanisms. The problem of coexistence between different networks (i.e. LTE and Wi-Fi) and the problem of coexistence between different networks of the same technology (i.e. multiple Wi-Fi OBSSs) is analyzed both qualitatively and quantitatively via system-level simulations, and the main issues to be faced are identified from these results. From that, distributed coordination mechanisms are proposed and evaluated via system-level simulations, both for the inter-technology coexistence problem and intra-technology coexistence problem. Results indicate that the proposed solutions provide significant gains when compare to the situation without distributed coordination.

Beamforming and interference cancellation schemes for D2D communications

We investigate device-to-device (D2D) communication underlaying cellular networks with M-antenna base stations. We consider both beamforming (BF) and interference cancellation (IC) strategies under quantized channel state information (CSI), as well as, perfect CSI. We derive tight closed-form approximations of the ergodic achievable rate which hold for arbitrary transmit power, location of users and number of antennas. Based on these approximations, we derive insightful asymptotic expressions for three special cases namely high signal-to-noise (SNR), weak interference, and large M. In particular, we show that in the high SNR regime a ceiling effect exists which depends on the received signal-to-interference ratio and the number of antennas. Moreover, the achievable rate scales logarithmically with M. The ergodic achievable rate is shown to scale logarithmically with SNR and the antenna number in the weak interference case. When the BS is equipped with large number of antennas, we find that the ergodic achievable rate under quantized CSI reaches a saturated value, whilst it scales as log2M under perfect CSI.

Massive MIMO in sparse channels

Massive multi-user multiple-input multiple-output (MU-MIMO) systems are cellular networks where the base stations (BSs) are equipped with hundreds of antennas, N, and communicate with tens of mobile stations (MSs), K, such that, N ≫ K ≫ 1. Contrary to most prior works, in this paper, we consider the uplink of a single-cell massive MIMO system operating in sparse channels with limited scattering. This case is of particular importance in most propagation scenarios, where the prevalent Rayleigh fading assumption becomes idealistic. We derive analytical approximations for the achievable rates of maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Furthermore, we study the asymptotic behavior of the achievable rates for both MRC and ZF receivers, when N and K go to infinity under the condition that N/K → c ≥ 1. Our results indicate that the achievable rate of MRC receivers reaches an asymptotic saturation limit, whereas the achievable rate of ZF receivers grows logarithmically with the number of MSs.

Circuit-aware design of energy-efficient massive MIMO systems

Densification is a key to greater throughput in cellular networks. The full potential of coordinated multipoint (CoMP) can be realized by massive multiple-input multiple-output (MIMO) systems, where each base station (BS) has very many antennas. However, the improved throughput comes at the price of more infrastructure; hardware cost and circuit power consumption scale linearly/affinely with the number of antennas. In this paper, we show that one can make the circuit power increase with only the square root of the number of antennas by circuit-aware system design. To this end, we derive achievable user rates for a system model with hardware imperfections and show how the level of imperfections can be gradually increased while maintaining high throughput. The connection between this scaling law and the circuit power consumption is established for different circuits at the BS.

Avalição de desempenho de protocolos de autenticação para redes sem fio heterogêneas

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2016.

Single-electron tunneling depressing synapse for cellular neural networks

In this paper, a cellular neural network with depressing synapses for contrast-invariant pattern classification and synchrony detection is presented, starting from the impulse model of the single-electron tunneling junction. The results of the impulse model and the network are simulated using simulation program with integrated circuit emphasis (SPICE). It is demonstrated that depressing synapses should be an important candidate of robust systems since they exhibit a rapid depression of excitatory postsynaptic potentials for successive presynaptic spikes.

Design of associative memories using cellular neural networks

Cellular neural networks (CNNs) have locally connected neurons. This characteristic makes CNNs adequate for hardware implementation and, consequently, for their employment on a variety of applications as real-time image processing and construction of efficient associative memories. Adjustments of CNN parameters is a complex problem involved in the configuration of CNN for associative memories. This paper reviews methods of associative memory design based on CNNs, and provides comparative performance analysis of these approaches.

Foundational forensic techniques for cellular and ad hoc multi-hop networks

The Internet has become an integral part of our nation’s critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a ‘distance metric’. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

Foundational Forensic Techniques for Cellular and Ad Hoc Multi-hop Networks

The Internet has become an integral part of our nation's critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a 'distance metric'. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

Host cellular regulatory networks in dengue virus-human interactions

Dengue fever is one of the most important mosquito-borne diseases worldwide and is caused by infection with dengue virus (DENV). The disease is endemic in tropical and sub-tropical regions and has increased remarkably in the last few decades. At present, there is no antiviral or approved vaccine against the virus. Treatment of dengue patients is usually supportive, through oral or intravenous rehydration, or by blood transfusion for more severe dengue cases. Infection of DENV in humans and mosquitoes involves a complex interplay between the virus and host factors. This results in regulation of numerous intracellular processes, such as signal transduction and gene transcription which leads to progression of disease. To understand the mechanisms underlying the disease, the study of virus and host factors is therefore essential and could lead to the identification of human proteins modulating an essential step in the virus life cycle. Knowledge of these human proteins could lead to the discovery of potential new drug targets and disease control strategies in the future. Recent advances of high throughput screening technologies have provided researchers with molecular tools to carry out investigations on a large scale. Several studies have focused on determination of the host factors during DENV infection in human and mosquito cells. For instance, a genome-wide RNA interference (RNAi) screen has identified host factors that potentially play an important role in both DENV and West Nile virus replication (Krishnan et al. 2008). In the present study, a high-throughput yeast two-hybrid screen has been utilised in order to identify human factors interacting with DENV non-structural proteins. From the screen, 94 potential human interactors were identified. These include proteins involved in immune signalling regulation, potassium voltage-gated channels, transcriptional regulators, protein transporters and endoplasmic reticulum-associated proteins. Validation of fifteen of these human interactions revealed twelve of them strongly interacted with DENV proteins. Two proteins of particular interest were selected for further investigations of functional biological systems at the molecular level. These proteins, including a nuclear-associated protein BANP and a voltage-gated potassium channel Kv1.3, both have been identified through interaction with the DENV NS2A. BANP is known to be involved in NF-kB immune signalling pathway, whereas, Kv1.3 is known to play an important role in regulating passive flow of potassium ions upon changes in the cell transmembrane potential. This study also initiated a construction of an Aedes aegypti cDNA library for use with DENV proteins in Y2H screen. However, several issues were encountered during the study which made the library unsuitable for protein interaction analysis. In parallel, innate immune signalling was also optimised for downstream analysis. Overall, the work presented in this thesis, in particular the Y2H screen provides a number of human factors potentially targeted by DENV during infection. Nonetheless, more work is required to be done in order to validate these proteins and determine their functional properties, as well as testing them with infectious DENV to establish a biological significance. In the long term, data from this study will be useful for investigating potential human factors for development of antiviral strategies against dengue.

Processing of polycaprolactone and polycaprolactone-based copolymers into 3D scaffolds and their cellular responses

Synthetic polymers have attracted much attention in tissue engineering due to their ability to modulate biomechanical properties. This study investigated the feasibility of processing poly(varepsilon-caprolactone) (PCL) homopolymer, PCL-poly(ethylene glycol) (PEG) diblock, and PCL-PEG-PCL triblock copolymers into three-dimensional porous scaffolds. Properties of the various polymers were investigated by dynamic thermal analysis. The scaffolds were manufactured using the desktop robot-based rapid prototyping technique. Gross morphology and internal three-dimensional structure of scaffolds were identified by scanning electron microscopy and micro-computed tomography, which showed excellent fusion at the filament junctions, high uniformity, and complete interconnectivity of pore networks. The influences of process parameters on scaffolds' morphological and mechanical characteristics were studied. Data confirmed that the process parameters directly influenced the pore size, porosity, and, consequently, the mechanical properties of the scaffolds. The in vitro cell culture study was performed to investigate the influence of polymer nature and scaffold architecture on the adhesion of the cells onto the scaffolds using rabbit smooth muscle cells. Light, scanning electron, and confocal laser microscopy showed cell adhesion, proliferation, and extracellular matrix formation on the surface as well as inside the structure of both scaffold groups. The completely interconnected and highly regular honeycomb-like pore morphology supported bridging of the pores via cell-to-cell contact as well as production of extracellular matrix at later time points. The results indicated that the incorporation of hydrophilic PEG into hydrophobic PCL enhanced the overall hydrophilicity and cell culture performance of PCL-PEG copolymer. However, the scaffold architecture did not significantly influence the cell culture performance in this study.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.