Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)

The proliferation of insecure Internet-connected devices gave rise to the IoT botnets which can grow very large rapidly and may perform high-impact cyber-attacks. The related studies for tackling IoT botnets are concerned with either capturing or analyzing IoT botnet samples, using honeypots and sandboxes, respectively. The lack of integration between the two implies that the samples captured by the honeypots must be manually submitted for analysis in sandboxes, introducing a delay during which a botnet may change its operation. Furthermore, the effectiveness of the proposed sandboxes is limited by the potential use of anti-analysis techniques and the inability to identify features for effective detection and identification of IoT botnets. In this paper, we propose and evaluate a novel framework, the IoT-BDA framework, for automated capturing, analysis, identification, and reporting of IoT botnets. The framework consists of honeypots integrated with a novel sandbox that supports a wider range of hardware and software configurations, and can identify indicators of compromise and attack, along with anti-analysis, persistence, and anti-forensics techniques. These features can make botnet detection and analysis, and infection remedy more effective. The framework reports the findings to a blacklist and abuse service to facilitate botnet suspension. The paper also describes the discovered anti-honeypot techniques and the measures applied to reduce the risk of honeypot detection. Over the period of seven months, the framework captured, analyzed, and reported 4077 unique IoT botnet samples. The analysis results show that some IoT botnets used anti-analysis, persistence, and anti-forensics techniques typically seen in traditional botnets

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

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communicatio

Cyberpsychology and Human Factors

The online environment has become a significant focus of the everyday behaviour and activities of individuals and organisations in contemporary society. The increasing mediation of communication has led to concerns about the potential risks and associated negative experiences which can occur to users, particularly children and young people. This is related to the emergence of the online environment as a location for criminal and abusive behaviour (e.g., harassment, sexual exploitation, fraud, hacking, malware). One of the key aspects of understanding online victimisation and engagement in criminal behaviours is the characteristics of online communication that are related to the affordances of the technologies, services and applications which constitute digital environments. The aim of this paper is to examine the influence of these characteristics on individual and group behaviour, as well as the associated opportunities for victimisation and criminal behaviour. These issues are of relevance for those involved in the design and implementation of technologies and services, as the ability to assess their potential use in this way can enhance strategies for improving the security of systems and users. It can also inform educational strategies for increasing user understanding of potential informational, privacy and personal risks, and associated steps to improve their security and privacy. Each of the main characteristics of mediated communication is examined, as well as their potential impact on individual and group behaviour, and associated opportunities for victimisation and offending. The article ends by considering the importance of recognising these issues when designing and implementing new technologies, services and applications