Biblio

Blockchain technology is the cornerstone of digital trust and systems' decentralization. The necessity of eliminating trust in computing systems has triggered researchers to investigate the applicability of Blockchain to decentralize the conventional security models. Specifically, researchers continuously aim at minimizing trust in the well-known Public Key Infrastructure (PKI) model which currently requires a trusted Certificate Authority (CA) to sign digital certificates. Recently, the Automated Certificate Management Environment (ACME) was standardized as a certificate issuance automation protocol. It minimizes the human interaction by enabling certificates to be automatically requested, verified, and installed on servers. ACME only solved the automation issue, but the trust concerns remain as a trusted CA is required. In this paper we propose decentralizing the ACME protocol by using the Blockchain technology to enhance the current trust issues of the existing PKI model and to eliminate the need for a trusted CA. The system was implemented and tested on Ethereum Blockchain, and the results showed that the system is feasible in terms of cost, speed, and applicability on a wide range of devices including Internet of Things (IoT) devices.

Advancements in computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized in practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements in many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist in the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed "in the wild" is still in its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements in combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices in diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed in local IoT realms. To support largescale empirical data analytics in the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, in addition to contributing to IoT cyber security situational awareness.

It is clear that cyber-attacks are a danger that must be addressed with great resolve, as they threaten the information infrastructure upon which we all depend. Many studies have been published expressing varying levels of success with machine learning approaches to combating cyber-attacks, but many modern studies still focus on training and evaluating with very outdated datasets containing old attacks that are no longer a threat, and also lack data on new attacks. Recent datasets like UNSW-NB15 and SANTA have been produced to address this problem. Even so, these modern datasets suffer from class imbalance, which reduces the efficacy of predictive models trained using these datasets. Herein we evaluate several pre-processing methods for addressing the class imbalance problem; using several of the most popular machine learning algorithms and a variant of UNSW-NB15 based upon the attributes from the SANTA dataset.

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, inferring probing events, which are commonly the first stage of any cyber attack, render a promising tactic to achieve that task. We have been receiving for the past three years 12 GB of daily malicious real darknet data (i.e., Internet traffic destined to half a million routable yet unallocated IP addresses) from more than 12 countries. This paper exploits such data to propose a novel approach that aims at capturing the behavior of the probing sources in an attempt to infer their orchestration (i.e., coordination) pattern. The latter defines a recently discovered characteristic of a new phenomenon of probing events that could be ominously leveraged to cause drastic Internet-wide and enterprise impacts as precursors of various cyber attacks. To accomplish its goals, the proposed approach leverages various signal and statistical techniques, information theoretical metrics, fuzzy approaches with real malware traffic and data mining methods. The approach is validated through one use case that arguably proves that a previously analyzed orchestrated probing event from last year is indeed still active, yet operating in a stealthy, very low rate mode. We envision that the proposed approach that is tailored towards darknet data, which is frequently, abundantly and effectively used to generate cyber threat intelligence, could be used by network security analysts, emergency response teams and/or observers of cyber events to infer large-scale orchestrated probing events for early cyber attack warning and notification.
 

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) 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.
 

Cyber scanning refers to the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. This misdemeanor is often the primarily methodology that is adopted by attackers prior to launching a targeted cyber attack. Hence, it is of paramount importance to research and adopt methods for the detection and attribution of cyber scanning. Nevertheless, with the surge of complex offered services from one side and the proliferation of hackers' refined, advanced, and sophisticated techniques from the other side, the task of containing cyber scanning poses serious issues and challenges. Furthermore recently, there has been a flourishing of a cyber phenomenon dubbed as cyber scanning campaigns - scanning techniques that are highly distributed, possess composite stealth capabilities and high coordination - rendering almost all current detection techniques unfeasible. This paper presents a comprehensive survey of the entire cyber scanning topic. It categorizes cyber scanning by elaborating on its nature, strategies and approaches. It also provides the reader with a classification and an exhaustive review of its techniques. Moreover, it offers a taxonomy of the current literature by focusing on distributed cyber scanning detection methods. To tackle cyber scanning campaigns, this paper uniquely reports on the analysis of two recent cyber scanning incidents. Finally, several concluding remarks are discussed.
 

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) 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.