Fingerprinting Internet DNS Amplification DDoS Activities

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) activities using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo-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. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.Comment: 5 pages, 2 figure

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 Distributed Architecture for Spam Mitigation on 4G Mobile Networks

The 4G of mobile networks is considered a technology-opportunistic and user-centric system combining the economical and technological advantages of various transmission technologies. Part of its new architecture dubbed as the System Architecture Evolution, 4G mobile networks will implement an evolved packet core. Although this will provide various critical advantages, it will however expose telecom networks to serious IP-based attacks. One often adopted solution by the industry to mitigate such attacks is based on a centralized security architecture. This centralized approach nonetheless, requires large processing resources to handle huge amount of traffic, which results in a significant over dimensioning problem in the centralized nodes causing this approach to fail from achieving its security task.\\ In this thesis, we primarily contribute by highlighting on two Spam flooding attacks, namely RTP VoIP SPIT and SMTP SPAM and demonstrating, through simulations and comparisons, their feasibility and DoS impact on 4G mobile networks and subsequent effects on mobile network operators. We further contribute by proposing a distributed architecture on the mobile architecture that is secure by mitigating those attacks, efficient by solving the over dimensioning problem and cost-effective by utilizing `off the shelf' low-cost hardware in the distributed nodes. Through additional simulation and analysis, we reveal the viability and effectiveness of our approach

On the Sequential Pattern and Rule Mining in the Analysis of Cyber Security Alerts

Data mining is well-known for its ability to extract concealed and indistinct patterns in the data, which is a common task in the field of cyber security. However, data mining is not always used to its full potential among cyber security community. In this paper, we discuss usability of sequential pattern and rule mining, a subset of data mining methods, in an analysis of cyber security alerts. First, we survey the use case of data mining, namely alert correlation and attack prediction. Subsequently, we evaluate sequential pattern and rule mining methods to find the one that is both fast and provides valuable results while dealing with the peculiarities of security alerts. An experiment was performed using the dataset of real alerts from an alert sharing platform. Finally, we present lessons learned from the experiment and a comparison of the selected methods based on their performance and soundness of the results

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation

Behavioral Service Graphs: A Big Data Approach for Prompt Investigation of Internet-Wide Infections

The 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS 2016), Larnaca, Cyprus, 21-23 November 2016The task of generating network-based evidence to support network forensic investigation is becoming increasingly prominent. Undoubtedly, such evidence is significantly imperative as it not only can be used to diagnose and respond to various network-related issues (i.e., performance bottlenecks, routing issues, etc.) but more importantly, can be leveraged to infer and further investigate network security intrusions and infections. In this context, this paper proposes a proactive approach that aims at generating accurate and actionable network-based evidence related to groups of compromised network machines. The approach is envisioned to guide investigators to promptly pinpoint such malicious groups for possible immediate mitigation as well as empowering network and digital forensic specialists to further examine those machines using auxiliary collected data or extracted digital artifacts. On one hand, the promptness of the approach is successfully achieved by monitoring and correlating perceived probing activities, which are typically the very first signs of an infection or misdemeanors. On the other hand, the generated evidence is accurate as it is based on an anomaly inference that fuses big data behavioral analytics in conjunction with formal graph theoretical concepts. We evaluate the proposed approach as a global capability in a security operations center. The empirical evaluations, which employ 80 GB of real darknet traffic, indeed demonstrates the accuracy, effectiveness and simplicity of the generated network-based evidence

Ransomware Detection and Classification Strategies

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransomware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.Comment: 9 pages, 2 figure

Assessing Internet-wide Cyber Situational Awareness of Critical Sectors

In this short paper, we take a first step towards empirically assessing Internet-wide malicious activities generated from and targeted towards Internet-scale business sectors (i.e., financial, health, education, etc.) and critical infrastructure (i.e., utilities, manufacturing, government, etc.). Facilitated by an innovative and a collaborative large-scale effort, we have conducted discussions with numerous Internet entities to obtain rare and private information related to allocated IP blocks pertaining to the aforementioned sectors and critical infrastructure. To this end, we employ such information to attribute Internet-scale maliciousness to such sectors and realms, in an attempt to provide an in-depth analysis of the global cyber situational posture. We draw upon close to 16.8 TB of darknet data to infer probing activities (typically generated by malicious/infected hosts) and DDoS backscatter, from which we distill IP addresses of victims. By executing week-long measurements, we observed an alarming number of more than 11,000 probing machines and 300 DDoS attack victims hosted by critical sectors. We also generate rare insights related to the maliciousness of various business sectors, including financial, which typically do not report their hosted and targeted illicit activities for reputation-preservation purposes. While we treat the obtained results with strict confidence due to obvious sensitivity reasons, we postulate that such generated cyber threat intelligence could be shared with sector/critical infrastructure operators, backbone networks and Internet service providers to contribute to the overall threat remediation objective

SDN Testbed for Evaluation of Large Exo-Atmospheric EMP Attacks

Large-scale nuclear electromagnetic pulse (EMP) attacks and natural disasters can cause extensive network failures across wide geographic regions. Although operational networks are designed to handle most single or dual faults, recent efforts have also focused on more capable multi-failure disaster recovery schemes. Concurrently, advances in software-defined networking (SDN) technologies have delivered highly-adaptable frameworks for implementing new and improved service provisioning and recovery paradigms in real-world settings. Hence this study leverages these new innovations to develop a robust disaster recovery (counter-EMP) framework for large backbone networks. Detailed findings from an experimental testbed study are also presented