ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses

Spatiotemporal patterns and predictability of cyberattacks

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing {\em macroscopic} properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches

Darknet as a Source of Cyber Threat Intelligence: Investigating Distributed and Reflection Denial of Service Attacks

Cyberspace has become a massive battlefield between computer criminals and computer security experts. In addition, large-scale cyber attacks have enormously matured and became capable to generate, in a prompt manner, significant interruptions and damage to Internet resources and infrastructure. Denial of Service (DoS) attacks are perhaps the most prominent and severe types of such large-scale cyber attacks. Furthermore, the existence of widely available encryption and anonymity techniques greatly increases the difficulty of the surveillance and investigation of cyber attacks. In this context, the availability of relevant cyber monitoring is of paramount importance. An effective approach to gather DoS cyber intelligence is to collect and analyze traffic destined to allocated, routable, yet unused Internet address space known as darknet. In this thesis, we leverage big darknet data to generate insights on various DoS events, namely, Distributed DoS (DDoS) and Distributed Reflection DoS (DRDoS) activities. First, we present a comprehensive survey of darknet. We primarily define and characterize darknet and indicate its alternative names. We further list other trap-based monitoring systems and compare them to darknet. In addition, we provide a taxonomy in relation to darknet technologies and identify research gaps that are related to three main darknet categories: deployment, traffic analysis, and visualization. Second, we characterize darknet data. Such information could generate indicators of cyber threat activity as well as provide in-depth understanding of the nature of its traffic. Particularly, we analyze darknet packets distribution, its used transport, network and application layer protocols and pinpoint its resolved domain names. Furthermore, we identify its IP classes and destination ports as well as geo-locate its source countries. We further investigate darknet-triggered threats. The aim is to explore darknet inferred threats and categorize their severities. Finally, we contribute by exploring the inter-correlation of such threats, by applying association rule mining techniques, to build threat association rules. Specifically, we generate clusters of threats that co-occur targeting a specific victim. Third, we propose a DDoS inference and forecasting model that aims at providing insights to organizations, security operators and emergency response teams during and after a DDoS attack. Specifically, this work strives to predict, within minutes, the attacks’ features, namely, intensity/rate (packets/sec) and size (estimated number of compromised machines/bots). The goal is to understand the future short-term trend of the ongoing DDoS attacks in terms of those features and thus provide the capability to recognize the current as well as future similar situations and hence appropriately respond to the threat. Further, our work aims at investigating DDoS campaigns by proposing a clustering approach to infer various victims targeted by the same campaign and predicting related features. To achieve our goal, our proposed approach leverages a number of time series and fluctuation analysis techniques, statistical methods and forecasting approaches. Fourth, we propose a novel approach to infer and characterize Internet-scale DRDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring DDoS activities using darknet, this work shows that we can extract DoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DRDoS activities such as intensity, rate and geographic location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks and the expectation maximization and k-means clustering techniques in an attempt to identify campaigns of DRDoS attacks. Finally, we conclude this work by providing some discussions and pinpointing some future work

Data-driven curation, learning and analysis for inferring evolving IoT botnets in the wild

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including, the lack of IoT-centric data that can be collected, analyzed and correlated, due to the highly heterogeneous nature of such devices and their widespread deployments in Internet-wide environments. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. This not only aims at classifying and inferring Internet-scale compromised IoT devices by solely observing such one-way network traffic, but also endeavors to uncover, track and report on orchestrated "in the wild" IoT botnets. Initially, to prepare the effective utilization of such data, a novel probabilistic model is designed and developed to cleanse such traffic from noise samples (i.e., misconfiguration traffic). Subsequently, several shallow and deep learning models are evaluated to ultimately design and develop a multi-window convolution neural network trained on active and passive measurements to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is deployed by scrutinizing a set of innovative and efficient network feature sets. By analyzing 3.6 TB of recent darknet traffic, the proposed approach uncovers a momentous 440,000 compromised IoT devices and generates evidence-based artifacts related to 350 IoT botnets. While some of these detected botnets refer to previously documented campaigns such as the Hide and Seek, Hajime and Fbot, other events illustrate evolving threats such as those with cryptojacking capabilities and those that are targeting industrial control system communication and control services

A Characterization of Cybersecurity Posture from Network Telescope Data

Data-driven understanding of cybersecurity posture is an important problem that has not been adequately explored. In this paper, we analyze some real data collected by CAIDA's network telescope during the month of March 2013. We propose to formalize the concept of cybersecurity posture from the perspectives of three kinds of time series: the number of victims (i.e., telescope IP addresses that are attacked), the number of attackers that are observed by the telescope, and the number of attacks that are observed by the telescope. Characterizing cybersecurity posture therefore becomes investigating the phenomena and statistical properties exhibited by these time series, and explaining their cybersecurity meanings. For example, we propose the concept of {\em sweep-time}, and show that sweep-time should be modeled by stochastic process, rather than random variable. We report that the number of attackers (and attacks) from a certain country dominates the total number of attackers (and attacks) that are observed by the telescope. We also show that substantially smaller network telescopes might not be as useful as a large telescope

Web attack risk awareness with lessons learned from high interaction honeypots

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Com a evolução da web 2.0, a maioria das empresas elabora negócios através da Internet usando aplicações web. Estas aplicações detêm dados importantes com requisitos cruciais como confidencialidade, integridade e disponibilidade. A perda destas propriedades influencia directamente o negócio colocando-o em risco. A percepção de risco providencia o necessário conhecimento de modo a agir para a sua mitigação. Nesta tese foi concretizada uma colecção de honeypots web de alta interacção utilizando diversas aplicações e sistemas operativos para analisar o comportamento do atacante. A utilização de ambientes de virtualização assim como ferramentas de monitorização de honeypots amplamente utilizadas providencia a informação forense necessária para ajudar a comunidade de investigação no estudo do modus operandi do atacante, armazenando os últimos exploits e ferramentas maliciosas, e a desenvolver as necessárias medidas de protecção que lidam com a maioria das técnicas de ataque. Utilizando a informação detalhada de ataque obtida com os honeypots web, o comportamento do atacante é classificado entre diferentes perfis de ataque para poderem ser analisadas as medidas de mitigação de risco que lidam com as perdas de negócio. Diferentes frameworks de segurança são analisadas para avaliar os benefícios que os conceitos básicos de segurança dos honeypots podem trazer na resposta aos requisitos de cada uma e a consequente mitigação de risco.With the evolution of web 2.0, the majority of enterprises deploy their business over the Internet using web applications. These applications carry important data with crucial requirements such as confidentiality, integrity and availability. The loss of those properties influences directly the business putting it at risk. Risk awareness provides the necessary know-how on how to act to achieve its mitigation. In this thesis a collection of high interaction web honeypots is deployed using multiple applications and diverse operating systems in order to analyse the attacker behaviour. The use of virtualization environments along with widely used honeypot monitoring tools provide the necessary forensic information that helps the research community to study the modus operandi of the attacker gathering the latest exploits and malicious tools and to develop adequate safeguards that deal with the majority of attacking techniques. Using the detailed attacking information gathered with the web honeypots, the attacking behaviour will be classified across different attacking profiles to analyse the necessary risk mitigation safeguards to deal with business losses. Different security frameworks commonly used by enterprises are analysed to evaluate the benefits of the honeypots security concepts in responding to each framework’s requirements and consequently mitigating the risk

Approaches and Techniques for Fingerprinting and Attributing Probing Activities by Observing Network Telescopes

The explosive growth, complexity, adoption and dynamism of cyberspace over the last decade has radically altered the globe. A plethora of nations have been at the very forefront of this change, fully embracing the opportunities provided by the advancements in science and technology in order to fortify the economy and to increase the productivity of everyday's life. However, the significant dependence on cyberspace has indeed brought new risks that often compromise, exploit and damage invaluable data and systems. Thus, the capability to proactively infer malicious activities is of paramount importance. In this context, generating cyber threat intelligence related to probing or scanning activities render an effective tactic to achieve the latter. In this thesis, we investigate such malicious activities, which are typically the precursors of various amplified, debilitating and disrupting cyber attacks. To achieve this task, we analyze real Internet-scale traffic targeting network telescopes or darknets, which are defined by routable, allocated yet unused Internet Protocol addresses. First, we present a comprehensive survey of the entire probing topic. Specifically, we categorize this topic by elaborating on the nature, strategies and approaches of such probing activities. Additionally, we provide the reader with a classification and an exhaustive review of various techniques that could be employed in such malicious activities. Finally, we depict a taxonomy of the current literature by focusing on distributed probing detection methods. Second, we focus on the problem of fingerprinting probing activities. To this end, we design, develop and validate approaches that can identify such activities targeting enterprise networks as well as those targeting the Internet-space. On one hand, the corporate probing detection approach uniquely exploits the information that could be leaked to the scanner, inferred from the internal network topology, to perform the detection. On the other hand, the more darknet tailored probing fingerprinting approach adopts a statistical approach to not only detect the probing activities but also identify the exact technique that was employed in the such activities. Third, for attribution purposes, we propose a correlation approach that fuses probing activities with malware samples. The approach aims at detecting whether Internet-scale machines are infected or not as well as pinpointing the exact malware type/family, if the machines were found to be compromised. To achieve the intended goals, the proposed approach initially devises a probabilistic model to filter out darknet misconfiguration traffic. Consequently, probing activities are correlated with malware samples by leveraging fuzzy hashing and entropy based techniques. To this end, we also investigate and report a rare Internet-scale probing event by proposing a multifaceted approach that correlates darknet, malware and passive dns traffic. Fourth, we focus on the problem of identifying and attributing large-scale probing campaigns, which render a new era of probing events. These are distinguished from previous probing incidents as (1) the population of the participating bots is several orders of magnitude larger, (2) the target scope is generally the entire Internet Protocol (IP) address space, and (3) the bots adopt well-orchestrated, often botmaster coordinated, stealth scan strategies that maximize targets' coverage while minimizing redundancy and overlap. To this end, we propose and validate three approaches. On one hand, two of the approaches rely on a set of behavioral analytics that aim at scrutinizing the generated traffic by the probing sources. Subsequently, they employ data mining and graph theoretic techniques to systematically cluster the probing sources into well-defined campaigns possessing similar behavioral similarity. The third approach, on the other hand, exploit time series interpolation and prediction to pinpoint orchestrated probing campaigns and to filter out non-coordinated probing flows. We conclude this thesis by highlighting some research gaps that pave the way for future work

A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive

The Cybercrime Triangle

Information technology can increase the convergence of three dimensions of the crime triangle due to the spatial and temporal confluence in the virtual world. In other words, its advancement can lead to facilitating criminals with more chances to commit a crime against suitable targets living in different real-world time zones without temporal and spatial orders. However, within this mechanism, cybercrime can be discouraged “…if the cyber-adversary is handled, the target/victim is guarded, or the place is effectively managed” (Wilcox & Cullen, 2018, p. 134). In fact, Madensen and Eck (2013) assert that only one effective controller is enough to prevent a crime. Given this condition of the crime triangle, it must be noted that each of these components (the offender, the target, and the place) or controllers (i.e., handler, guardian, and manager) can play a pivotal role in reducing cybercrime. To date, scholars and professionals have analyzed the phenomenon of cybercrime and developed cybercrime prevention strategies relying predominantly on cybercrime victimization (suitable targets) but have yet to utilize the broader framework of the crime triangle commonly used in the analysis and prevention of crime. More specifically, the dimensions of cybercrime offenders, places, or controllers have been absent in prior scientific research and in guiding the establishment and examination of cybercrime prevention strategies. Given this gap, much remains to be known as to how these conceptual entities operate in the virtual realm and whether they share similarities with what we know about other crimes in the physical world. Thus, the purpose of this study is to extend the application of the “Crime Triangle,” a derivative of Routine Activity Theory, to crime events in the digital realm to provide scholars, practitioners, and policy makers a more complete lens to improve understanding and prevention of cybercrime incidents. In other words, this dissertation will endeavor to devise a comprehensive framework for our society to use to form cybersecurity policies to implement a secure and stable digital environment that supports continued economic growth as well as national security. The findings of this study suggest that both criminological and technical perspectives are crucial in comprehending cybercrime incidents. This dissertation attempts to independently explore these three components in order to portray the characteristics of cybercriminals, cybercrime victims, and place management. Specifically, this study first explores the characteristics of cybercriminals via a criminal profiling method primarily using court criminal record documents (indictments/complaints) provided by the FIU law library website. Second, the associations between cybercrime victims, digital capable guardianship, perceived risks of cybercrime, and online activity are examined using Eurobarometer survey data. Third, the associations between place management activities and cybercrime prevention are examined using “Phishing Campaign” and “Cybersecurity Awareness Training Program” data derived from FIU’s Division of Information Technology