ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning.

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.

Development of an Efficiency Platform Based on MQTT for UAV Controlling and DoS Attack Detection.

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios.

The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. One of the main reasons for this is the availability of techniques provided by this paradigm, such as environmental monitoring employing user data and everyday objects. The facilities provided by the IoT infrastructure allow the development of a wide range of new business models and applications (e.g., smart homes, smart cities, or e-health). However, there are still concerns over the security measures which need to be addressed to ensure a suitable deployment. Distributed Denial of Service (DDoS) attacks are among the most severe virtual threats at present and occur prominently in this scenario, which can be mainly owed to their ease of execution. In light of this, several research studies have been conducted to find new strategies as well as improve existing techniques and solutions. The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. However, the high granularity that characterizes the IoT scenarios and the wide range of techniques explored during the DDoS attacks make the task of finding and implementing new solutions quite challenging. This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios. To fill this knowledge gap, in this study we carry out an in-depth investigation of the state-of-the-art and create a taxonomy that describes and characterizes existing solutions and highlights their main limitations. Our taxonomy provides a comprehensive view of the reasons for the deployment of the solutions, and the scenario in which they operate. The results of this study demonstrate the main benefits and drawbacks of each solution set when applied to specific scenarios by examining current trends and future perspectives, for example, the adoption of emerging technologies based on Cloud and Edge (or Fog) Computing.

New DoS Defense Method Based on Strong Designated Verifier Signatures.

We present a novel technique for source authentication of a packet stream in a network, which intends to give guarantees that a specific network flow really comes from a claimed origin. This mechanism, named packet level authentication (PLA), can be an essential tool for addressing Denial of Service (DoS) attacks. Based on designated verifier signature schemes, our proposal is an appropriate and unprecedented solution applying digital signatures for DoS prevention. Our scheme does not rely on an expensive public-key infrastructure and makes use of light cryptography machinery that is suitable in the context of the Internet of Things (IoT). We analyze our proposed scheme as a defense measure considering known DoS attacks and present a formal proof of its resilience face to eventual adversaries. Furthermore, we compare our solution to already existent strategies, highlighting its advantages and drawbacks.

A Methodological Approach for Assessing Amplified Reflection Distributed Denial of Service on the Internet of Things.

Concerns about security on Internet of Things (IoT) cover data privacy and integrity, access control, and availability. IoT abuse in distributed denial of service attacks is a major issue, as typical IoT devices' limited computing, communications, and power resources are prioritized in implementing functionality rather than security features. Incidents involving attacks have been reported, but without clear characterization and evaluation of threats and impacts. The main purpose of this work is to methodically assess the possible impacts of a specific class-amplified reflection distributed denial of service attacks (AR-DDoS)-against IoT. The novel approach used to empirically examine the threat represented by running the attack over a controlled environment, with IoT devices, considered the perspective of an attacker. The methodology used in tests includes that perspective, and actively prospects vulnerabilities in computer systems. This methodology defines standardized procedures for tool-independent vulnerability assessment based on strategy, and the decision flows during execution of penetration tests (pentests). After validation in different scenarios, the methodology was applied in amplified reflection distributed denial of service (AR-DDoS) attack threat assessment. Results show that, according to attack intensity, AR-DDoS saturates reflector infrastructure. Therefore, concerns about AR-DDoS are founded, but expected impact on abused IoT infrastructure and devices will be possibly as hard as on final victims.