Anti-fragile ICT Systems

This book introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix’s media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta’s anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts

Design and Management of Collaborative Intrusion Detection Networks

In recent years network intrusions have become a severe threat to the privacy and safety of computer users. Recent cyber attacks compromise a large number of hosts to form botnets. Hackers not only aim at harvesting private data and identity information from compromised nodes, but also use the compromised nodes to launch attacks such as distributed denial-of-service (DDoS) attacks. As a counter measure, Intrusion Detection Systems (IDS) are used to identify intrusions by comparing observable behavior against suspicious patterns. Traditional IDSs monitor computer activities on a single host or network traffic in a sub-network. They do not have a global view of intrusions and are not effective in detecting fast spreading attacks, unknown, or new threats. In turn, they can achieve better detection accuracy through collaboration. An Intrusion Detection Network (IDN) is such a collaboration network allowing IDSs to exchange information with each other and to benefit from the collective knowledge and experience shared by others. IDNs enhance the overall accuracy of intrusion assessment as well as the ability to detect new intrusion types. Building an effective IDN is however a challenging task. For example, adversaries may compromise some IDSs in the network and then leverage the compromised nodes to send false information, or even attack others in the network, which can compromise the efficiency of the IDN. It is, therefore, important for an IDN to detect and isolate malicious insiders. Another challenge is how to make efficient intrusion detection assessment based on the collective diagnosis from other IDSs. Appropriate selection of collaborators and incentive-compatible resource management in support of IDSs' interaction with others are also key challenges in IDN design. To achieve efficiency, robustness, and scalability, we propose an IDN architecture and especially focus on the design of four of its essential components, namely, trust management, acquaintance management, resource management, and feedback aggregation. We evaluate our proposals and compare them with prominent ones in the literature and show their superiority using several metrics, including efficiency, robustness, scalability, incentive-compatibility, and fairness. Our IDN design provides guidelines for the deployment of a secure and scalable IDN where effective collaboration can be established between IDSs

A Social approach to security: Using social networks to help detect malicious web content

In the past six years, tremendous growth in the size and popularity of social networking has fundamentally changed the way we use the Internet. As social aspects to the Internet continue to expand in both quantity and scope, security of the users of social networking sites and the data generated by them will ultimately become an unavoidable concern. This is a realization that malicious users have already had, as viruses, spyware, and phishing scams continue to propagate through social networks at an alarming rate. It is now becoming increasingly critical that the average user also understands this potential for the exploitation of trust among the social networking community. Yet, the security industry has been slow to respond in the act of providing adequate tools for protecting the user. This thesis describes the development of a proof-of-concept application that uses social networking data to aid in the detection of malicious web content as it propagates through the user\u27s network. While this methodology certainly has its limitations, including user impersonation and false positive rates, the results of testing this application against known phishing and malware sites with real-world user profiles have shown surprisingly positive results

AI-based algorithm for intrusion detection on a real Dataset

[Abstract]: In this Project, Novel Machine Learning proposals are given to produce a Network Intrusion Detection System (NIDS). For this, a state of the art Dataset for Cyclo Stationary NIDS has been used, together with a previously proposed standard methodology to compare the results of different models over the same Dataset. An extensive research has been done for this Project about the different Datasets available for NIDS, as has been done to expose the evolution and functioning of IDSs. Finally, experiments have been made with Outlier Detectors, Ensemble Methods, Deep Learning and Conventional Classifiers to compare with previously published results over the same Dataset and with the same methodology. The findings reveal that the Ensemble Methods have been capable to improve the results from prior research being the best approach the Extreme Gradient Boosting method.[Resumen]: En este Proyecto, se presentan novedosas propuestas de Aprendizaje Automático para producir un Sistema de Detección de Intrusos en Red (NIDS). Para ello, se ha utilizado un Dataset de última generación para NIDS Cicloestacionarios, junto con una metodología estándar previamente propuesta para comparar los resultados de diferentes modelos sobre el mismo Dataset. Para este Proyecto se ha realizado una extensa investigación sobre los diferentes conjuntos de datos disponibles para NIDS, así como se ha expuesto la evolución y funcionamiento de los IDSs. Por último, se han realizado experimentos con Detectores de Anomalias, Métodos de Conjunto, Aprendizaje Profundo y Clasificadores Convencionales para comparar con resultados previamente publicados sobre el mismo Dataset y con la misma metodología. Los resultados revelan que los Métodos de Conjunto han sido capaces de mejorar los resultados de investigaciones previas siendo el mejor enfoque el método de Extreme Gradient Boosting.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202

Malware Propagation in Online Social Networks: Modeling, Analysis and Real-world Implementations

The popularity and wide spread usage of online social networks (OSNs) have attracted hackers and cyber criminals to use OSNs as an attack platform to spread malware. Over the last few years, Facebook users have experienced hundreds of malware attacks. A successful attack can lead to tens of millions of OSN accounts being compromised and computers being infected. Cyber criminals can mount massive denial of service attacks against Internet infrastructures or systems using compromised accounts and computers. Malware infecting a user's computer have the ability to steal login credentials and other confidential information stored on the computer, install ransomware and infect other computers on the same network. Therefore, it is important to understand propagation dynamics of malware in OSNs in order to detect, contain and remove them as early as possible. The objective of this dissertation is thus to model and study propagation dynamics of various types of malware in social networks such as Facebook, LinkedIn and Orkut. In particular, - we propose analytical models that characterize propagation dynamics of cross-site scripting and Trojan malware, the two major types of malware propagating in OSNs. Our models assume the topological characteristics of real-world social networks, namely, low average shortest distance, power-law distribution of node degrees and high clustering coefficient. The proposed models were validated using a real-world social network graph. - we present the design and implementation of a cellular botnet named SoCellBot that uses the OSN platform as a means to recruit and control cellular bots on smartphones. SoCellBot utilizes OSN messaging systems as communication channels between bots. We then present a simulation-based analysis of the botnet's strategies to maximize the number of infected victims within a short amount of time and, at the same time, minimize the risk of being detected. - we describe and analyze emerging malware threats in OSNs, namely, clickjacking, extension-based and Magnet malware. We discuss their implementations and working mechanics, and analyze their propagation dynamics via simulations. - we evaluate the performance of several selective monitoring schemes used for malware detection in OSNs. With selective monitoring, we select a set of important users in the network and monitor their and their friends activities and posts for malware threats. These schemes differ in how the set of important users is selected. We evaluate and compare the effectiveness of several selective monitoring schemes in terms of malware detection in OSNs