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.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Cuba’s Role in Venezuela’s Control of the Internet and Online Social Networks

The arrival of Cuba’s Information Technology (IT) and Communications Minister Ramiro Valdés to Venezuela in the Spring of 2010 to serve as a ‘consultant’ to the Venezuelan government awakened a new reality in that country. Rampant with deep economic troubles, escalating crime, a murder rate that has doubled since Chávez took over in 1999, and an opposition movement led by university students and other activists who use the Internet as their primary weapon, Venezuela has resorted to Cuba for help. In a country where in large part traditional media outlets have been censored or are government-controlled, the Internet and its online social networks have become the place to obtain, as well as disseminate, unfiltered information. As such, Internet growth and use of its social networks has skyrocketed in Venezuela, making it one of Latin America’s highest Web users. Because of its increased use to spark political debate among Venezuelans and publish information that differs with the official government line, Chávez has embarked on an initiative to bring the Internet to the poor and others who would otherwise not have access, by establishing government-sponsored Internet Info Centers throughout the country, to disseminate information to his followers. With the help of Cuban advisors, who for years have been a part of Venezuela’s defense, education, and health care initiatives, Chávez has apparently taken to adapting Cuba’s methodology for the control of information. He has begun to take special steps toward also controlling the type of information flowing through the country’s online social networks, considering the implementation of a government-controlled single Internet access point in Venezuela. Simultaneously, in adapting to Venezuela’s Internet reality, Chávez has engaged online by creating his own Twitter account in an attempt to influence public opinion, primarily of those who browse the Web. With a rapidly growing following that may soon reach one million subscribers, Chávez claims to have set up his own online trench to wage cyber space battle.

Political strategy, leadership, and policy entrepreneurship in Japanese defense policy and politics: A comparison of three prime ministerships

Since the end of the Cold War, Japan's defense policy and politics has gone through significant changes. Throughout the post cold war period, US-Japan alliance managers, politicians with differing visions and preferences, scholars, think tanks, and the actions of foreign governments have all played significant roles in influencing these changes. Along with these actors, the Japanese prime minister has played an important, if sometimes subtle, role in the realm of defense policy and politics. Japanese prime ministers, though significantly weaker than many heads of state, nevertheless play an important role in policy by empowering different actors (bureaucratic actors, independent commissions, or civil actors), through personal diplomacy, through agenda-setting, and through symbolic acts of state. The power of the prime minister to influence policy processes, however, has frequently varied by prime minister. My dissertation investigates how different political strategies and entrepreneurial insights by the prime minister have influenced defense policy and politics since the end of the Cold War. In addition, it seeks to explain how the quality of political strategy and entrepreneurial insight employed by different prime ministers was important in the success of different approaches to defense. My dissertation employs a comparative case study approach to examine how different prime ministerial strategies have mattered in the realm of Japanese defense policy and politics. Three prime ministers have been chosen: Prime Minister Hashimoto Ryutaro (1996-1998); Prime Minister Koizumi Junichiro (2001-2006); and Prime Minister Hatoyama Yukio (2009-2010). These prime ministers have been chosen to provide maximum contrast on issues of policy preference, cabinet management, choice of partners, and overall strategy. As my dissertation finds, the quality of political strategy has been an important aspect of Japan's defense transformation. Successful strategies have frequently used the knowledge and accumulated personal networks of bureaucrats, supplemented bureaucratic initiatives with top-down personal diplomacy, and used a revitalized US-Japan strategic relationship as a political resource for a stronger prime ministership. Though alternative approaches, such as those that have looked to displace the influence of bureaucrats and the US in defense policy, have been less successful, this dissertation also finds theoretical evidence that alternatives may exist.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

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.

Political Strategy, Leadership, and Policy Entrepreneurship in Japanese Defense Policy and Politics: A Comparison of Three Prime Ministerships

Since the end of the Cold War, Japan’s defense policy and politics has gone through significant changes. Throughout the post cold war period, US-Japan alliance managers, politicians with differing visions and preferences, scholars, think tanks, and the actions of foreign governments have all played significant roles in influencing these changes. Along with these actors, the Japanese prime minister has played an important, if sometimes subtle, role in the realm of defense policy and politics. Japanese prime ministers, though significantly weaker than many heads of state, nevertheless play an important role in policy by empowering different actors (bureaucratic actors, independent commissions, or civil actors), through personal diplomacy, through agenda-setting, and through symbolic acts of state. The power of the prime minister to influence policy processes, however, has frequently varied by prime minister. My dissertation investigates how different political strategies and entrepreneurial insights by the prime minister have influenced defense policy and politics since the end of the Cold War. In addition, it seeks to explain how the quality of political strategy and entrepreneurial insight employed by different prime ministers was important in the success of different approaches to defense. My dissertation employs a comparative case study approach to examine how different prime ministerial strategies have mattered in the realm of Japanese defense policy and politics. Three prime ministers have been chosen: Prime Minister Hashimoto Ryutaro (1996-1998); Prime Minister Koizumi Junichiro (2001-2006); and Prime Minister Hatoyama Yukio (2009-2010). These prime ministers have been chosen to provide maximum contrast on issues of policy preference, cabinet management, choice of partners, and overall strategy. As my dissertation finds, the quality of political strategy has been an important aspect of Japan’s defense transformation. Successful strategies have frequently used the knowledge and accumulated personal networks of bureaucrats, supplemented bureaucratic initiatives with top-down personal diplomacy, and used a revitalized US-Japan strategic relationship as a political resource for a stronger prime ministership. Though alternative approaches, such as those that have looked to displace the influence of bureaucrats and the US in defense policy, have been less successful, this dissertation also finds theoretical evidence that alternatives may exist.