A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR

Classifying Tor traffic encrypted payload using machine learning

Tor, a network offering Internet anonymity, presented both positive and potentially malicious applications, leading to the need for efficient Tor traffic monitoring. While most current traffic classification methods rely on flow-based features, these can be unreliable due to factors like asymmetric routing, and the use of multiple packets for feature computation can lead to processing delays. Recognising the multi-layered encryption of Tor compared to nonTor encrypted payloads, our study explored distinct patterns in their encrypted data. We introduced a novel method using Deep Packet Inspection and machine learning to differentiate between Tor and nonTor traffic based solely on encrypted payload. In the first strand of our research, we investigated hex character analysis of the Tor and nonTor encrypted payloads through statistical testing across 8 groups of application types. Remarkably, our investigation revealed a significant differentiation rate of 94.53% between Tor and nonTor traffic. In the second strand of our research, we aimed to distinguish Tor and nonTor traffic using machine learning, based on encrypted payload features. This proposed feature-based approach proved effective, as evidenced by our classification performance, which attained an average accuracy rate of 95.65% across these 8 groups of applications. Thereby, this study contributes to the efficient classification of Tor and nonTor traffic through features derived solely from a single encrypted payload packet, independent of its position in the traffic flow

Classifying tor traffic using character analysis

Tor is a privacy-preserving network that enables users to browse the Internet anonymously. Although the prospect of such anonymity is welcomed in many quarters, Tor can also be used for malicious purposes, prompting the need to monitor Tor network connections. Most traffic classification methods depend on flow-based features, due to traffic encryption. However, these features can be less reliable due to issues like asymmetric routing, and processing multiple packets can be time-intensive. In light of Tor’s sophisticated multilayered payload encryption compared with nonTor encryption, our research explored patterns in the encrypted data of both networks, challenging conventional encryption theory which assumes that ciphertexts should not be distinguishable from random strings of equal length. Our novel approach leverages machine learning to differentiate Tor from nonTor traffic using only the encrypted payload. We focused on extracting statistical hex character-based features from their encrypted data. For consistent findings, we drew from two datasets: a public one, which was divided into eight application types for more granular insight and a private one. Both datasets covered Tor and nonTor traffic. We developed a custom Python script called Charcount to extract relevant data and features accurately. To verify our results’ robustness, we utilized both Weka and scikit-learn for classification. In our first line of research, we conducted hex character analysis on the encrypted payloads of both Tor and nonTor traffic using statistical testing. Our investigation revealed a significant differentiation rate between Tor and nonTor traffic of 95.42% for the public dataset and 100% for the private dataset. The second phase of our study aimed to distinguish between Tor and nonTor traffic using machine learning, focusing on encrypted payload features that are independent of length. In our evaluations, the public dataset yielded an average accuracy of 93.56% when classified with the Decision Tree (DT) algorithm in scikit-learn, and 95.65% with the j48 algorithm in Weka. For the private dataset, the accuracies were 95.23% and 97.12%, respectively. Additionally, we found that the combination of WrapperSubsetEval+BestFirst with the J48 classifier both enhanced accuracy and optimized processing efficiency. In conclusion, our study contributes to both demonstrating the distinction between Tor and nonTor traffic and achieving efficient classification of both types of traffic using features derived exclusively from a single encrypted payload packet. This work holds significant implications for cybersecurity and points towards further advancements in the field.Tor is a privacy-preserving network that enables users to browse the Internet anonymously. Although the prospect of such anonymity is welcomed in many quarters, Tor can also be used for malicious purposes, prompting the need to monitor Tor network connections. Most traffic classification methods depend on flow-based features, due to traffic encryption. However, these features can be less reliable due to issues like asymmetric routing, and processing multiple packets can be time-intensive. In light of Tor’s sophisticated multilayered payload encryption compared with nonTor encryption, our research explored patterns in the encrypted data of both networks, challenging conventional encryption theory which assumes that ciphertexts should not be distinguishable from random strings of equal length. Our novel approach leverages machine learning to differentiate Tor from nonTor traffic using only the encrypted payload. We focused on extracting statistical hex character-based features from their encrypted data. For consistent findings, we drew from two datasets: a public one, which was divided into eight application types for more granular insight and a private one. Both datasets covered Tor and nonTor traffic. We developed a custom Python script called Charcount to extract relevant data and features accurately. To verify our results’ robustness, we utilized both Weka and scikit-learn for classification. In our first line of research, we conducted hex character analysis on the encrypted payloads of both Tor and nonTor traffic using statistical testing. Our investigation revealed a significant differentiation rate between Tor and nonTor traffic of 95.42% for the public dataset and 100% for the private dataset. The second phase of our study aimed to distinguish between Tor and nonTor traffic using machine learning, focusing on encrypted payload features that are independent of length. In our evaluations, the public dataset yielded an average accuracy of 93.56% when classified with the Decision Tree (DT) algorithm in scikit-learn, and 95.65% with the j48 algorithm in Weka. For the private dataset, the accuracies were 95.23% and 97.12%, respectively. Additionally, we found that the combination of WrapperSubsetEval+BestFirst with the J48 classifier both enhanced accuracy and optimized processing efficiency. In conclusion, our study contributes to both demonstrating the distinction between Tor and nonTor traffic and achieving efficient classification of both types of traffic using features derived exclusively from a single encrypted payload packet. This work holds significant implications for cybersecurity and points towards further advancements in the field

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Machine learning approach for detection of nonTor traffic

Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset