Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection

Intrusion detection is the essential part of network security in combating against illegal network access or malicious cyberattacks. Due to the constantly evolving nature of cyber attacks, it has been a technical challenge for an intrusion detection system (IDS) to effectively recognize unknown attacks or known attacks with inadequate training data. Therefore in this dissertation work, an innovative two-stage classifier is developed for accurately and efficiently detecting both unknown attacks and known attacks with insufficient or inaccurate training information. The novel two-stage fuzzy classification scheme is based on advanced machine learning techniques specifically for handling the ambiguity of traffic connections and network data. In the first stage of the classification, a fuzzy C-means (FCM) algorithm is employed to softly compute and optimize clustering centers of the training datasets with some degree of fuzziness counting for feature inaccuracy and ambiguity in the training data. Subsequently, a distance-weighted k-NN (k-nearest neighbors) classifier, combined with the Dempster-Shafer Theory (DST), is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes to further address the data uncertainty issue in the cyberattack data. In the second stage of the proposed classification algorithm, a subsequent classification scheme is implemented based on the obtained pignistic probabilities and their entropy functions to determine if the input data are normal, one of the known attacks or an unknown attack. Secondly, to strengthen the robustness to attacks, we form the three-layer hierarchy ensemble classifier based on the FCM weighted k-NN DST classifier to have more precise inferences than those made by a single classifier. The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks. The experimental results show that the new two-stage fuzzy KNN-DST classifier outperforms other well-known classifiers in intrusion detection and is especially effective in detecting unknown attacks

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Unsupervised tracking of time-evolving data streams and an application to short-term urban traffic flow forecasting

I am indebted to many people for their help and support I receive during my Ph.D. study and research at DIBRIS-University of Genoa. First and foremost, I would like to express my sincere thanks to my supervisors Prof.Dr. Masulli, and Prof.Dr. Rovetta for the invaluable guidance, frequent meetings, and discussions, and the encouragement and support on my way of research. I thanks all the members of the DIBRIS for their support and kindness during my 4 years Ph.D. I would like also to acknowledge the contribution of the projects Piattaforma per la mobili\ue0 Urbana con Gestione delle INformazioni da sorgenti eterogenee (PLUG-IN) and COST Action IC1406 High Performance Modelling and Simulation for Big Data Applications (cHiPSet). Last and most importantly, I wish to thanks my family: my wife Shaimaa who stays with me through the joys and pains; my daughter and son whom gives me happiness every-day; and my parents for their constant love and encouragement

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour