A Framework for Cybersecurity Alert Distribution and Response Network (ADRIAN)

Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges such as inefficiency of alert management and integration with real-time communication tools. These challenges cause delays and cost penalties for organizations in their efforts to resolve the alerts and potential security breaches. This paper introduces a cybersecurity Alert Distribution and Response Network (Adrian) system. Adrian introduces a novel enhancement to SIEM platforms by integrating SIEM functionalities with real-time collaboration platforms. Adrian leverages the uniquity of mobile applications of collaboration platforms to provide real-time alerts, enabling a two-way communication channel that facilitates immediate response to security incidents and efficient SIEM platform management. To demonstrate Adrian’s capabilities, we have introduced a case-study that integrates Wazuh, a SIEM platform, to Slack, a collaboration platform. The case study demonstrates all the functionalities of Adrian including the real-time alert distribution, alert customization, alert categorization, and enablement of management activities, thereby increasing the responsiveness and efficiency of Adrian’s capabilities. The study concludes with a discussion on the potential expansion of Adrian’s capabilities including the incorporation of artificial intelligence (AI) for enhanced alert prioritization and response automation.

The History, Trend, Types, and Mitigation of Distributed Denial of Service Attacks

Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global information source for every being. Despite all this, attacker knowledge by cybercriminals has advanced and resulted in different attack methodologies on the internet and its data stores. This paper will discuss the origin and significance of Denial of Service (DoS) and Distributed Denial of Service (DDoS). These kinds of attacks remain the most effective methods used by the bad guys to cause substantial damage in terms of operational, reputational, and financial damage to organizations globally. These kinds of attacks have hindered network performance and availability. The victim’s network is flooded with massive illegal traffic hence, denying genuine traffic from passing through for authorized users. The paper will explore detection mechanisms, and mitigation techniques for this network threat.

Human-as-a-security-sensor for harvesting threat intelligence

Humans are commonly seen as the weakest link in corporate information security.This led to a lot of effort being put into security training and awareness campaigns,which resulted in employees being less likely the target of successful attacks.Existing approaches,however,do not tap the full potential that can be gained through these campaigns.On the one hand,human perception offers an additional source of contextual information for detected incidents,on the other hand it serves as information source for incidents that may not be detectable by automated procedures.These approaches only allow a text-based reporting of basic incident information.A structured recording of human delivered information that also provides compatibility with existing SIEM systems is still missing.In this work,we propose an approach,which allows humans to systematically report perceived anomalies or incidents in a structured way.Our approach furthermore supports the integration of such reports into analytics systems.Thereby,we identify connecting points to SIEM systems,develop a taxonomy for structuring elements reportable by humans acting as a security sensor and develop a structured data format to record data delivered by humans.A prototypical human-as-a-security-sensor wizard applied to a real-world use-case shows our proof of concept.

Human-as-a-security-sensor for harvesting threat intelligence

Humans are commonly seen as the weakest link in corporate information security.This led to a lot of effort being put into security training and awareness campaigns,which resulted in employees being less likely the target of successful attacks.Existing approaches,however,do not tap the full potential that can be gained through these campaigns.On the one hand,human perception offers an additional source of contextual information for detected incidents,on the other hand it serves as information source for incidents that may not be detectable by automated procedures.These approaches only allow a text-based reporting of basic incident information.A structured recording of human delivered information that also provides compatibility with existing SIEM systems is still missing.In this work,we propose an approach,which allows humans to systematically report perceived anomalies or incidents in a structured way.Our approach furthermore supports the integration of such reports into analytics systems.Thereby,we identify connecting points to SIEM systems,develop a taxonomy for structuring elements reportable by humans acting as a security sensor and develop a structured data format to record data delivered by humans.A prototypical human-as-a-security-sensor wizard applied to a real-world use-case shows our proof of concept.