Traffic Analysis Attacks on Skype VoIP Calls

Skype is one of the most popular voice-over-IP (VoIP) service providers. One of the main reasons for the popularity of Skype VoIP services is its unique set of features to protect privacy of VoIP calls such as strong encryption, proprietary protocols, unknown codecs, dynamic path selection, and the constant packet rate. In this paper, we propose a class of passive traffic analysis attacks to compromise privacy of Skype VoIP calls. The proposed attacks are based on application-level features extracted from VoIP call traces. The proposed attacks are evaluated by extensive experiments over different types of networks including commercialized anonymity networks and our campus network. The experiment results show that the proposed traffic analysis attacks can greatly compromise the privacy of Skype calls. Possible countermeasure to mitigate the proposed traffic analysis attacks are analyzed in this paper

Traffic Analysis Attacks on Skype VoIP Calls

Skype is one of the most popular voice-over-IP (VoIP) service providers. One of the main reasons for the popularity of Skype VoIP services is its unique set of features to protect privacy of VoIP calls such as strong encryption, proprietary protocols, unknown codecs, dynamic path selection, and the constant packet rate. In this paper, we propose a class of passive traffic analysis attacks to compromise privacy of Skype VoIP calls. The proposed attacks are based on application-level features extracted from VoIP call traces. The proposed attacks are evaluated by extensive experiments over different types of networks including commercialized anonymity networks and our campus network. The experiment results show that the proposed traffic analysis attacks can greatly compromise the privacy of Skype calls. Possible countermeasure to mitigate the proposed traffic analysis attacks are analyzed in this paper

A New Class of Attacks on Time Series Data Mining

Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining are not effective in preserving time-domain privacy. We present the data flow separation attack on privacy in time series data mining, which is based on blind source separation techniques from statistical signal processing. Our experiments with real data show that this attack is effective. By combining the data flow separation method and the frequency matching method, an attacker can identify data sources and compromise time-domain privacy. We propose possible countermeasures to the data flow separation attack in the paper

A New Class of Attacks on Time Series Data Mining

Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining are not effective in preserving time-domain privacy. We present the data flow separation attack on privacy in time series data mining, which is based on blind source separation techniques from statistical signal processing. Our experiments with real data show that this attack is effective. By combining the data flow separation method and the frequency matching method, an attacker can identify data sources and compromise time-domain privacy. We propose possible countermeasures to the data flow separation attack in the paper

On Topology of Sensor Networks Deployed for Multi-Target Tracking

In this paper, we study topologies of sensor networks deployed for tracking multiple targets. Tracking multiple moving targets is a challenging problem. Most of the previously proposed tracking algorithms simplify the problem by assuming access to the signal from an individual target for tracking. Recently, tracking algorithms based on blind source separation (BSS), a statistical signal-processing technique widely used to recover individual signals from mixtures of signals, have been proposed. BSS-based tracking algorithms are proven to be effective in tracking multiple indistinguishable targets. The topology of a wireless sensor network deployed for tracking with BSS-based algorithms is critical to tracking performance because the topology affects separation performance, and the topology determines accuracy and precision of estimation on the paths taken by targets. We propose cluster topologies for BSS-based tracking algorithms. Guidelines on parameter selection for proposed topologies are given in this paper. We evaluate the proposed cluster topologies with extensive experiments. Our experiments show that the proposed topologies can significantly improve both the accuracy and the precision of BSS-based tracking algorithms

Energy Prediction Based Intrusion Detection In Wireless Sensor Networks

A challenge in designing wireless sensor networks is to maximize the lifetime of the network with respect to limited resources and energy. These limitations make the network particularly vulnerable to attacks from adversaries. Denial of Service (DOS) is considered a severely damaging attack in monitoring applications when intruders attack the network and force it to lose its power and die early. There are intrusion detection approaches, but they require communications and calculations which waste the network’s limited resources. In this paper, we propose a new intrusion detection model that is suitable for defending against DOS attacks. We use the idea of energy prediction to anticipate the energy consumption of the network in order to detect intruders based on the each individual node’s excessive usage of power. Our approach does not require a lot of communications or calculations between the nodes and the cluster head. It is energy efficient and accurate in detecting intruders. Simulations show that our energy aware intrusion detection approach can effectively detect intruders based on energy consumption rate

Energy Prediction Based Intrusion Detection In Wireless Sensor Networks

A challenge in designing wireless sensor networks is to maximize the lifetime of the network with respect to limited resources and energy. These limitations make the network particularly vulnerable to attacks from adversaries. Denial of Service (DOS) is considered a severely damaging attack in monitoring applications when intruders attack the network and force it to lose its power and die early. There are intrusion detection approaches, but they require communications and calculations which waste the network’s limited resources. In this paper, we propose a new intrusion detection model that is suitable for defending against DOS attacks. We use the idea of energy prediction to anticipate the energy consumption of the network in order to detect intruders based on the each individual node’s excessive usage of power. Our approach does not require a lot of communications or calculations between the nodes and the cluster head. It is energy efficient and accurate in detecting intruders. Simulations show that our energy aware intrusion detection approach can effectively detect intruders based on energy consumption rate

On Non-Cooperative Multiple-Target Tracking with Wireless Sensor Networks

In this paper, we propose an approach to track multiple non-cooperative targets with wireless sensor networks. Most existing tracking algorithms can not be directly applied to non-cooperative target tracking because they assume the access to signals from individual targets for tracking by assuming that: 1) there is only one target in a field; 2) signals from different co-operative targets can be differentiated; or 3) interference caused by signals from other targets is negligible because of attenuation. We propose a general approach for tracking non-cooperative targets. The tracking algorithm first separates the aggregate signals from multiple indistinguishable targets via the blind source separation (BSS) algorithms. Through the analysis on both the temporal and spatial correlation of the separated individual signals, the tracking algorithm determines the location of a target and its moving track. A voting scheme based on the spatial information is designed to better estimate the moving track. Furthermore, we analyze and discuss the influence of signal attenuation and the tracking resolution of the proposed tracking approach. Our experiments show that the proposed approach can both accurately and precisely track multiple indistinguishable moving targets

Evaluating Throughput and Delay in 3G and 4G Mobile Architectures

The third generation (3G) system was officially completed in 1997 by the International Telecommunications Union Radio communication Sector (ITU-R). This technology was a leader for more than a decade in the cellular network architecture. When 4G was introduced as an upgrade of the existing architecture, it was driven by the increasing demand for mobile broadband services with higher data rates and Quality of Service (QoS) [1]. The 4G infrastructure market in 2014 is predicted to reach $11.4 billion. According to AT & T the number of 4G subscribers will reach 440 millio