Automated Web Browsing tools, such as Selenium and headless browsers, are used to collect traffic traces from networked applications, with which statistical models describing the traffic are obtained. However, we show that traces from Selenium and headless browsers have markedly different traffic characteristics than human generated traces, with potential impact on the quality of the obtained models. To overcome this limitation, we propose RLBrowse, an automated web automation framework that imitates human browsing habits by separating web automation from the browser using reinforcement learning. By separating the browser and automation tool, RLBrowse improves on 9 out of the 13 traffic trace features tested. The distribution of packet sizes in a trace improves the most, with a nearly 400 % improvement. We test RLBrowse by collecting a corpus of network packet traces on a set of human-navigated website browsing sessions, and by RLBrowse and Selenium. In the subsequent analysis, we identify key differences in the resulting packet traces.

RLBrowse: Generating Realistic Packet Traces with Reinforcement Learning

With the rise of the internet, there has been an increasing focus on user anonymity. Anonymous communication networks (ACNs) aim to protect the identity privacy of users in the network. As a typical ACN, Tor achieves user anonymity by relaying user data through a series of relay nodes. However, this results in higher latency due to the transmission of network traffic between multiple nodes. This paper proposes a port-based anonymous communication network (PBACN) to address this issue. First, we propose a path construction algorithm. This algorithm describes constructing paths by partitioning the communication path information, which can reduce the probability of being discovered by adversaries. Secondly, we design a port-based source routing addressing method. During data transmission from the source to the destination, each node can directly forward the data by resolving the address into the port of each node. This method eliminates the need for table lookups, reducing the complexity of routing. Lastly, we propose an entropy-based metric to measure the anonymity of different ACNs. In terms of experimental evaluation, we quantitatively analyze the anonymity and end-to-end delay of various ACNs. The experimental results show that our proposed method reduces end-to-end delay by approximately 25% compared to Tor. When the adversary fraction is 20%, PBACN can improve the anonymity degree by approximately 4%.

Port-Based Anonymous Communication Network: An Efficient and Secure Anonymous Communication Network

Automated Web Browsing tools, such as Selenium and headless browsers, are used to collect traffic traces from networked applications, with which statistical models describing the traffic are obtained. However, we show that traces from Selenium and headless browsers have markedly different traffic characteristics than human generated traces, with potential impact on the quality of the obtained models. To overcome this limitation, we propose RLBrowse, an automated web automation framework that imitates human browsing habits by separating web automation from the browser using reinforcement learning. By separating the browser and automation tool, RLBrowse improves on 9 out of the 13 traffic trace features tested. The distribution of packet sizes in a trace improves the most, with a nearly 400 % improvement. We test RLBrowse by collecting a corpus of network packet traces on a set of human-navigated website browsing sessions, and by RLBrowse and Selenium. In the subsequent analysis, we identify key differences in the resulting packet traces. 

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

We proposed a Traffic Analysis Resistant Network (TARN) that randomizes IP addresses in a fashion similar to Frequency Hop Spread Spectrum (FHSS), allowing users to blend into background traffic. IP hopping alone is not enough. TARN may still be susceptible to side-channel analysis. To remove the vulnerabilities, we introduce a SDX-based solution. In this work, we describe the design and implementation of TARN and experimental environment used to test TARN.

/pdf/traffic-analysis-resistant-network-tarn-anonymity-analysis-302zbarjdu.pdf

Traffic Analysis Resistant Network (TARN) Anonymity Analysis

Current Internet Protocol routing provides minimal privacy, which enables multiple exploits. The main issue is that the source and destination addresses of all packets appear in plain text. This enables numerous attacks, including surveillance, man-in-the-middle (MITM), and denial of service (DoS). The talk explains how these attacks work in the current network. Endpoints often believe that use of Network Address Translation (NAT), and Dynamic Host Configuration Protocol (DHCP) can minimize the loss of privacy.We will explain how the regularity of human behavior can be used to overcome these countermeasures. Once packets leave the local autonomous system (AS), they are routed through the network by the Border Gateway Protocol (BGP). The talk will discuss the unreliability of BGP and current attacks on the routing protocol. This will include an introduction to BGP injects and the PEERING testbed for BGP experimentation. One experiment we have performed uses statistical methods (CUSUM and F-test) to detect BGP injection events. We describe work we performed that applies BGP injects to Internet Protocol (IP) address randomization to replace fixed IP addresses in headers with randomized addresses. We explain the similarities and differences of this approach with virtual private networks (VPNs). Analysis of this work shows that BGP reliance on autonomous system (AS) numbers removes privacy from the concept, even though it would disable the current generation of MITM and DoS attacks. We end by presenting a compromise approach that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts. We contrast this approach with the Tor overlay network and provide some performance data.

Global Internet Traffic Routing and Privacy

This paper investigates side-channel vulnerabilities of a wireless communication application in vehicular environments (DSRC/WAVE) protocol implementation of a traffic intersection application. A prototype roadside unit (RSU) was implemented using real DSRC devices. The functionality of the WAVE short message (Wsm)-channel is extended to include an implementation of WAVE short message protocol (WSMP) for broadcasting GPS data and RSU instructions in vehicular communications. In the example used, DSRC is used to replace an intersection stoplight. Denial of service attacks are executed that leverage DSRC RSU timing and packet size side-channels to selectively disable the stoplight. Simulations are implemented to determine our ability to stealthily drop packets so as to force two vehicles to collide. Hidden Markov models (HMM) and Support Vector Machines (SVM) are constructed from sniffed side-channel information. We use inter-packet delay time and packet size side-channel information to design our attackes. In operational networks, packets should be encrypted in order to hide the contents of the packet payloads, but packet sizes and timing are not affected by encryption. HMMs were inferred using only side-channel information. The inferred HMMs track the protocol status over time. The SVM classifier was inferred using both side-channel data and packet payloads. At run-time, though, the SVM only had access to side-channel information. Simulation experiments were implemented to test HMM and SVM ability to identify packets used to signal vehicles to stop and yield right-of-way. Timing HMM side-channel attack caused collision with 2.5% false positive rate (FPR), while the packet size one resulted 9.5% FPR.

Side-Channel Security Analysis of Connected Vehicle Communications Using Hidden Markov Models

Access control enforces security policies for controlling critical resources. For V2X (Vehicle to Everything) autonomous military vehicle fleets, network middleware systems such as ROS (Robotic Operating System) expose system resources through networked publisher/subscriber and client/server paradigms. Without proper access control, these systems are vulnerable to attacks from compromised network nodes, which may perform data poisoning attacks, flood packets on a network, or attempt to gain lateral control of other resources. Access control for robotic middleware systems has been investigated in both ROS1 and ROS2. Still, these implementations do not have mechanisms for evaluating a policy's consistency and completeness or writing expressive policies for distributed fleets. We explore an RBAC (Role-Based Access Control) mechanism layered onto ROS environments that uses local permission caches with precomputed truth tables for fast policy evaluation. To demonstrate the features, we will compare policy outputs against SROS (Secure ROS) policies and test our approach against simulated malicious adversaries with penetration testing and fuzzing techniques.

Access Control Requirements for Autonomous Robotic Fleets

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Papers

Traffic Analysis Resistant Network (TARN) Anonymity Analysis

Global Internet Traffic Routing and Privacy

Side-Channel Security Analysis of Connected Vehicle Communications Using Hidden Markov Models

Access Control Requirements for Autonomous Robotic Fleets