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

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

Stability analysis of new generalized mean-square stochastic fractional differential equations and their applications in technology

Stability theory has significant applications in technology, especially in control systems. On the other hand, the newly defined generalized mean-square stochastic fractional (GMSF) operators are particularly interesting in control theory and systems due to their various controllable parameters. Thus, the combined study of stability theory and GMSF operators becomes crucial. In this research work, we construct a new class of GMSF differential equations and provide a rigorous proof of the existence of their solutions. Furthermore, we investigate the stability of these solutions using the generalized Ulam-Hyers-Rassias stability criterion. Some examples are also provided to demonstrate the effectiveness of the proposed approach in solving fractional differential equations (FDEs) and evaluating their stability. The paper concludes by discussing potential applications of the proposed results in technology and outlining avenues for future research

Blockchain for Email Security: A Perspective on Existing and Potential Solutions for Phishing Attacks

Email security is critical to all types of businesses, as it represents 80% of the plethora of official communication tools used by most organizations worldwide. Attackers use several techniques to trick users to perform harmful actions, mainly via emails. Identifying such activities or circumventing them is better than relying on the end-user\u27s behavior of being unaware. Traditional e-mail systems use centralized servers to provide services, making them a single point of failure if servers are attacked or at least private information is leaked. Thus, a decentralized e-mail system can provide more trust and reliability. This study is an initial attempt to explore the use of Blockchain-based solutions to improve the security and privacy of traditional e-mail systems. This paper presents two-fold coverage of this problem. First, a summary of common email security architectures is presented, outlined, and criticized for various parameters. Second, we propose a technique for solving the problem of phishing emails by targeting changes in the email system structure using Blockchain technology thereby preventing a considerable number of phishing attempts. We discuss the approach along with its advantages and disadvantages