A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Multiple classifier systems for network security from data collection to attack detection

Since the Internet started developing, hosts and provided services have always been targeted with attacks trying to disrupt them. Trends show that, throughout the years, the number of hosts, as well as the degree of dependency of the whole society on the services provided through the Internet, increased dramatically, whereas the skills and knowledge required to interfere with normal network operation, and eventually to abruptly interrupt it, decreased accordingly. This considerations urge the requirement for effective tools, aimed at granting security to Internet users. The need for systems capable of detecting attacks, and reacting in order to prevent them from occurring again, is nowadays undeniable. In this thesis we propose methods based on multiple classifier systems for intrusion detection. We use such systems for automated data collection, also taking privacy issues into account. Some approaches to traffic classification are presented too, together with a proposal for the practical deployment of multiple classifiers in a real network environment

Automatically building datasets of labeled IP traffic traces: A self-training approach

Many approaches have been proposed so far to tackle computer network security. Among them, several systems exploit Machine Learning and Pattern Recognition techniques, by regarding malicious behavior detection as a classification problem. Supervised and unsupervised algorithms have been used in this context, each one with its own benefits and shortcomings. When using supervised techniques, a representative training set is required, which reliably indicates what a human expert wants the system to learn and recognize, by means of suitably labeled samples. In real environments there is a significant difficulty in collecting a representative dataset of correctly labeled traffic traces. In adversarial environments such a task is made even harder by malicious attackers, trying to make their actions’ evidences stealthy. In order to overcome this problem, a self-training system is presented in this paper, building a dataset of labeled network traffic based on raw tcpdump traces and no prior knowledge on data. Results on both emulated and real traffic traces have shown that intrusion detection systems trained on such a dataset perform as well as the same systems trained on correctly hand-labeled data

Integrating a Network IDS into an Open Source Cloud Computing Environment

Abstract—The success of the Cloud Computing paradigm may be jeopardized by concerns about the risk of misuse of this model aimed at conducting illegal activities. In this paper we address the issue of detecting Denial of Service attacks performed by means of resources acquired on-demand on a Cloud Computing platform. To this purpose, we propose to investigate the consequences of the use of a distributed strategy to detect and block attacks, or other malicious activities, originated by misbehaving customers of a Cloud Computing provider. In order to check the viability of our approach, we also evaluate the impact on performance of our proposed solution. This paper presents the installation and deployment experience of a distributed defence strategy and illustrates the preliminary results of the performance evaluation