The increase of connected devices and the constantly evolving methods and techniques by attackers pose a challenge for network intrusion detection systems from conception to operation. As a result, we see a constant adoption of machine learning algorithms for network intrusion detection systems. However, the dataset used by these studies has become obsolete regarding both background and attack traffic. This work describes the AB-TRAP framework that enables the use of updated network traffic and considers operational concerns to enable the complete deployment of the solution. AB-TRAP is a fivestep framework consisting of (i) the generation of the attack dataset, (ii) the bonafide dataset, (iii) training of machine learning models, (iv) realization (implementation) of the models, and (v) the performance evaluation of the realized model after deployment. We exercised the AB-TRAP for local (LAN) and global (internet) environments to detect TCP port scanning attacks. The LAN study case presented an f1-score of 0.96, and an area under the ROC curve of 0.99 using a decision tree with minimal CPU and RAM usage on kernel-space. For the internet case with eight machine learning algorithms with an average f1-score of 0.95, an average area under the ROC curve of 0.98, and an average overhead of 1.4% CPU and 3.6% RAM on user-space in a single-board computer. This framework has the following paramount characteristics: it is reproducible, uses the most up-to-date network traffic, attacks, and addresses the concerns to the model's realization and deployment. INDEX TERMS cybersecurity, datasets, intrusion detection system, machine learning, network security, supervised learning NOMENCLATURE AU C Area Under the Curve. B5G 5G and beyond. CoAP Constrained Application Protocol. CP S Cyber-Physical System. CSV Comma-separated values. DDoS Distributed Denial of Service. DL Deep Learning. DN P 3 Distributed Network Protocol 3. DoS Denial of Service.
Abstract-The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semi-supervised learning problems. Three actions govern the particles' dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model evolution consists of sets of edges arranged by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove there exists a deterministic version with largely reduced computational complexity; specifically, with linear growth. Furthermore, the edge domination process corresponds to an unfolding map in such way that edges "stretch" and "shrink" according to the vertexedge dynamics. Consequently, the unfolding effect summarizes the relevant relationships between vertices and the uncovered data classes. The proposed model captures important details of connectivity patterns over the vertex-edge dynamics evolution, in contrast to previous approaches which focused on only vertex or only edge dynamics. Computer simulations reveal that the new model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and overlapping structures of data.
Drones can play a game-changing role in reducing both cost and time in the context of last-mile deliveries. This paper addresses the last-mile delivery problem from a complex system viewpoint, where the collective performance of the drones is investigated. We consider a last-mile delivery system with a tradable permit model (TPM) for airspace use. Typically, in other research works regarding lastmile delivery drones, a fully cooperative centralized scenario is contemplated. In our approach, due to the TPM, the agents (i.e. drones) need to compete for airspace permits in a distributed manner. We simulate the system and evaluate how different parameters, such as the arrival rate and airspace dimensions, impact the system behavior in terms of the cost and time needed by the drones to acquire flight permits, and the airspace utilization. We use a simplified simulation model, where the agents' strategies are naïve, and the drones' flight dynamics are not accounted for. Nevertheless, the simulation's level of detail is adequate for capturing interesting properties from the agents' collective behavior, as our results support. The obtained results show that the system's performance is satisfactory, even with naïve agents and under high traffic conditions. Moreover, a real-world implementation of our competitive decentralized approach would lead to advantages, such as fast permit transactions, simple computational infrastructures, and error resilience.
This article presents Maximum Visibility Approach (MVA), a new time series forecasting method based on the Complex Network theory. MVA initially maps time series data into a complex network using the visibility graph method. Then, based on the similarity measures between the nodes in the network, MVA calculates the one-step-ahead forecasts. MVA does not use all past terms in the forecasting process, but only the most significant observations, which are indicated as a result of the autocorrelation function. This method was applied to five different groups of data, most of them showing trend characteristics, seasonal variations and/or non-stationary behavior. We calculated error measures to evaluate the performance of MVA. The results of statistical tests and error measures revealed that MVA has a good performance compared to the accuracy obtained by the benchmarks considered in this work. In all cases, MVA surpassed other forecasting methods in Literature, which confirms that this work will contribute to the field of time series forecasting not only in the theoretical aspect, but also in practice.
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