Global DDoS Threat Landscape Tracking Network Anomalies using Elliptic Curve Cryptography

Devices, such as in mobile devices or RFID. In brief, ECC based algorithms can be easily comprised into existing protocols to get the same retrograde compatibility and security with lesser resources.: Recent variants of Distributed Denial-of-Service (DDoS) attacks influence the flexibility of application-layer procedures to disguise malicious activities as normal traffic patterns, while concurrently overwhelming the target destination with a large application rate. New countermeasures are necessary, aimed at guaranteeing an early and dependable identification of the compromised network nodes (the botnet). This work familiarizes a formal model for the above-mentioned class of attacks, and we devise an implication algorithm that estimates the botnet hidden in the network, converging to the true solution as time developments. Notably, the analysis is validated over real network traces. An important building block for digital communication is the Public-key cryptography systems. Public-Key cryptography (PKC) systems can be used to provide secure substructures over insecure channels without swapping a secret key. Applying Public-Key cryptography organizations is a challenge for most submission stages when several factors have to be considered in selecting the application platform. The most popular public-key cryptography systems nowadays are RSA and Elliptic Curve Cryptography (ECC). The compensations can be achieved from smaller key sizes including storing, speed and efficient use of power and bandwidth. The use of shorter keys means lower space necessities for key storage and quicker calculation operations. These advantages are essential when public-key cryptography is applied in constrained

Leveraging Conventional Internet Routing Protocol Behavior to Defeat DDoS and Adverse Networking Conditions

The Internet is a cornerstone of modern society. Yet increasingly devastating attacks against the Internet threaten to undermine the Internet\u27s success at connecting the unconnected. Of all the adversarial campaigns waged against the Internet and the organizations that rely on it, distributed denial of service, or DDoS, tops the list of the most volatile attacks. In recent years, DDoS attacks have been responsible for large swaths of the Internet blacking out, while other attacks have completely overwhelmed key Internet services and websites. Core to the Internet\u27s functionality is the way in which traffic on the Internet gets from one destination to another. The set of rules, or protocol, that defines the way traffic travels the Internet is known as the Border Gateway Protocol, or BGP, the de facto routing protocol on the Internet. Advanced adversaries often target the most used portions of the Internet by flooding the routes benign traffic takes with malicious traffic designed to cause widespread traffic loss to targeted end users and regions. This dissertation focuses on examining the following thesis statement. Rather than seek to redefine the way the Internet works to combat advanced DDoS attacks, we can leverage conventional Internet routing behavior to mitigate modern distributed denial of service attacks. The research in this work breaks down into a single arc with three independent, but connected thrusts, which demonstrate that the aforementioned thesis is possible, practical, and useful. The first thrust demonstrates that this thesis is possible by building and evaluating Nyx, a system that can protect Internet networks from DDoS using BGP, without an Internet redesign and without cooperation from other networks. This work reveals that Nyx is effective in simulation for protecting Internet networks and end users from the impact of devastating DDoS. The second thrust examines the real-world practicality of Nyx, as well as other systems which rely on real-world BGP behavior. Through a comprehensive set of real-world Internet routing experiments, this second thrust confirms that Nyx works effectively in practice beyond simulation as well as revealing novel insights about the effectiveness of other Internet security defensive and offensive systems. We then follow these experiments by re-evaluating Nyx under the real-world routing constraints we discovered. The third thrust explores the usefulness of Nyx for mitigating DDoS against a crucial industry sector, power generation, by exposing the latent vulnerability of the U.S. power grid to DDoS and how a system such as Nyx can protect electric power utilities. This final thrust finds that the current set of exposed U.S. power facilities are widely vulnerable to DDoS that could induce blackouts, and that Nyx can be leveraged to reduce the impact of these targeted DDoS attacks

Intrusion Detection in Industrial Networks via Data Streaming

Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in\ua0 Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations.In this chapter we explain that\ua0 stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to\ua0 facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems).We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment

WARDOG: Awareness detection watchbog for Botnet infection on the host device

Botnets constitute nowadays one of the most dangerous security threats worldwide. High volumes of infected machines are controlled by a malicious entity and perform coordinated cyber-attacks. The problem will become even worse in the era of the Internet of Things (IoT) as the number of insecure devices is going to be exponentially increased. This paper presents WARDOG – an awareness and digital forensic system that informs the end-user of the botnet’s infection, exposes the botnet infrastructure, and captures verifiable data that can be utilized in a court of law. The responsible authority gathers all information and automatically generates a unitary documentation for the case. The document contains undisputed forensic information, tracking all involved parties and their role in the attack. The deployed security mechanisms and the overall administration setting ensures non-repudiation of performed actions and enforces accountability. The provided properties are verified through theoretic analysis. In simulated environment, the effectiveness of the proposed solution, in mitigating the botnet operations, is also tested against real attack strategies that have been captured by the FORTHcert honeypots, overcoming state-of-the-art solutions. Moreover, a preliminary version is implemented in real computers and IoT devices, highlighting the low computational/communicational overheads of WARDOG in the field

On modeling and mitigating new breed of dos attacks

Denial of Service (DoS) attacks pose serious threats to the Internet, exerting in tremendous impact on our daily lives that are heavily dependent on the good health of the Internet. This dissertation aims to achieve two objectives:1) to model new possibilities of the low rate DoS attacks; 2) to develop effective mitigation mechanisms to counter the threat from low rate DoS attacks. A new stealthy DDoS attack model referred to as the quiet attack is proposed in this dissertation. The attack traffic consists of TCP traffic only. Widely used botnets in today\u27s various attacks and newly introduced network feedback control are integral part of the quiet attack model. The quiet attack shows that short-lived TCP flows used as attack flows can be intentionally misused. This dissertation proposes another attack model referred to as the perfect storm which uses a combination of UDP and TCP. Better CAPTCHAs are highlighted as current defense against botnets to mitigate the quiet attack and the perfect storm. A novel time domain technique is proposed that relies on the time difference between subsequent packets of each flow to detect periodicity of the low rate DoS attack flow. An attacker can easily use different IP address spoofing techniques or botnets to launch a low rate DoS attack and fool the detection system. To mitigate such a threat, this dissertation proposes a second detection algorithm that detects the sudden increase in the traffic load of all the expired flows within a short period. In a network rate DoS attacks, it is shown that the traffic load of all the expired flows is less than certain thresholds, which are derived from real Internet traffic analysis. A novel filtering scheme is proposed to drop the low rate DoS attack packets. The simulation results confirm attack mitigation by using proposed technique. Future research directions will be briefly discussed

GUIDE FOR THE COLLECTION OF INSTRUSION DATA FOR MALWARE ANALYSIS AND DETECTION IN THE BUILD AND DEPLOYMENT PHASE

During the COVID-19 pandemic, when most businesses were not equipped for remote work and cloud computing, we saw a significant surge in ransomware attacks. This study aims to utilize machine learning and artificial intelligence to prevent known and unknown malware threats from being exploited by threat actors when developers build and deploy applications to the cloud. This study demonstrated an experimental quantitative research design using Aqua. The experiment\u27s sample is a Docker image. Aqua checked the Docker image for malware, sensitive data, Critical/High vulnerabilities, misconfiguration, and OSS license. The data collection approach is experimental. Our analysis of the experiment demonstrated how unapproved images were prevented from running anywhere in our environment based on known vulnerabilities, embedded secrets, OSS licensing, dynamic threat analysis, and secure image configuration. In addition to the experiment, the forensic data collected in the build and deployment phase are exploitable vulnerability, Critical/High Vulnerability Score, Misconfiguration, Sensitive Data, and Root User (Super User). Since Aqua generates a detailed audit record for every event during risk assessment and runtime, we viewed two events on the Audit page for our experiment. One of the events caused an alert due to two failed controls (Vulnerability Score, Super User), and the other was a successful event meaning that the image is secure to deploy in the production environment. The primary finding for our study is the forensic data associated with the two events on the Audit page in Aqua. In addition, Aqua validated our security controls and runtime policies based on the forensic data with both events on the Audit page. Finally, the study’s conclusions will mitigate the likelihood that organizations will fall victim to ransomware by mitigating and preventing the total damage caused by a malware attack