Cyber range exercises are important to validate security tools and train personnel to enhance their testing skills. The background web traffic for cyber range exercises must emulate a real network. The currently available traffic generator tools can only replay the captured network traces, or generate simple packet streams and do not consider the representative end-users behaviour. Generating realistic and useful web traffic incorporating a varsity of user behaviour models is a challenging research task. In this work, we present a web traffic generator based on Markov model, Dirichlet distribution, and Hybrid distribution. We evaluate our models using a large dataset containing one million web sessions, and our results show that the entropy of output data and cross entropy is approximately same as the entropy of training data. Our tool has interactive GUI and supports various OS environments, and hence, can be used for various web traffic generation applications including CR.

The increased usage of smart wearables in various applications, specifically in health-care, emphasizes the need for secure communication to transmit sensitive health-data. In a practical scenario, where multiple devices are carried by a person, a common secret key is essential for secure group communication. Group key generation and sharing among wearables have received very little attention in the literature due to the underlying challenges: 1) difficulty in obtaining a good source of randomness to generate strong cryptographic keys, and 2) finding a common feature among all the devices to share the key. In this paper, we present a novel solution to generate and distribute group secret keys by exploiting on-board accelerometer sensor and the unique walking style of the user, i.e., gait. We propose a method to identify the suitable samples of accelerometer data during all routine activities of a subject to generate the keys with high entropy. In our scheme, the smartphone placed on waist employs fuzzy vault, a cryptographic construct, and utilizes the acceleration due to gait, a common characteristic extracted on all wearable devices to share the secret key. We implement our solution on commercially available off-the-shelf smart wearables, measure the system performance, and conduct experiments with multiple subjects. Our results demonstrate that the proposed solution has a bit rate of 750 b/s, low system overhead, distributes the key securely and quickly to all legitimate devices, and is suitable for practical applications.

Recent years have witnessed a remarkable growth in the number of smart wearable devices. For many of these devices, an important security issue is to establish an authenticated communication channel between legitimate devices to protect the subsequent communications. Due to the wireless nature of the communication and the extreme resource constraints of sensor devices, providing secure, efficient, and user-friendly device pairing is a challenging task. Traditional solutions for device pairing mostly depend on key predistribution, which is unsuitable for wearable devices in many ways. In this article, we design Gait-Key, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users’ walking characteristics (gait). The intuition is that the sensors on different locations on the same body experience similar accelerometer signals when the user is walking. However, one main challenge is that the accelerometer also captures motion signals produced by other body parts (e.g., swinging arms). We address this issue by using the blind source separation technique to extract the informative signal produced by the unique gait patterns. Our experimental results show that Gait-Key can generate a common 128-bit key for two legitimate devices with 98.3% probability. To demonstrate the feasibility, the proposed key generation scheme is implemented on modern smartphones. The evaluation results show that the proposed scheme can run in real time on modern mobile devices and incurs low system overhead.

In recent years, the proliferation of wireless devices has contributed to the emergence of new set of applications termed as Location Based Services (LBS). LBS provide privileges to mobile users based on their proximity to a facility. In order to gain benefits, users may lie or falsely claim their location. Hence, it is essential to verify the legitimacy of users. In this paper, we propose our novel solution for generating location proof for mobile users and verification of the location claim by application services. Our protocol exploits unique Wi-Fi signal characteristics and employs an information theoretically secure fuzzy vault scheme. We provide a detailed theoretical and experimental evaluation of our protocol. Our solution is faster by an order of magnitude, and the performance of our scheme is independent of the location tag size and distance between the mobile user and location proof provider compared to the state-of-the-art.

The existing secret key generation schemes for body-worn devices using wireless channel characteristics, e.g., received signal strength indicator (RSSI) are dependent on the node mobility and have very low bit rate. In this work, we propose a novel mobility independent RSSI based secret key generation protocol - iARC, which induces artificial randomness in the channel by employing dual antennas and dynamic frequency hopping effectively.

Exploiting unique wireless channel characteristics like signal strength for secret key generation has been recently studied by researchers. These schemes are lightweight and suitable for resource constrained wearable devices. However, a major drawback of existing schemes is that the successive channel samples with small sampling interval will have high correlation in time. This reduces the entropy and bit rate of keys. In this paper, we present dual-link based Radio Frequency fingerprinting solution - DLINK, which dynamically identifies the suitable multipath link to generate secret keys with improved entropy and bit rate in fast as well as slow fading channel conditions. We conduct an extensive set of experiments with real sensor devices mounted on subjects in multiple indoor environments. Our results show that, DLINK reduces the correlation of successive channel samples by 67%, and has 5 times higher bit rate, and improved entropy in all channel conditions compared to existing solutions.

In recent years, Location Based Services (LBS) and related applications are gaining popularity for providing access to the resources. LBS can either increase or decrease the access privileges of the users based on their location. Current mobile devices lack the intelligence to prove their location when requested. In this paper, we propose our novel solution for generating location proof for mobile users leveraging unique wireless characteristics and verification of the location claim by application services.