Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy

Detection of DNS Based Covert Channels

Information theft or data exfiltration, whether personal or corporate, is now a lucrative mainstay of cybercrime activity. Recent security reports have suggested that while information, such as credit card data is still a prime target, other data such as corporate secrets, employee files and intellectual property are increasingly sought after on the black market. Malicious actors that are intent on exfiltrating valuable data, usually employ some form of Advanced Persistent Threat (APT) in order to exfiltrate large amounts of data over a long period of time with a high degree of covertness. Botnets are prime examples of APTs that are usually established on targeted systems through malware or exploit kits that leverage system vulnerabilities. Once established, Botnets rely on covert command and control (C&C) communications with a central server, this allows a malicious actor to keep track of compromised systems and to send out instructions for compromised systems to do their biding. Covert channels provide an ideal mechanism for data exfiltration and the exchange of command and control messages that are essential to a Botnets effectiveness. Our work focuses on one particular form of covert channel that enables communication of hidden messages over normal Domain Name Server (DNS) network traffic. Covert channels based on DNS traffic are of particular interest, as DNS requests are an essential part of most Internet traffic and as a result are rarely filtered or blocked by firewalls. As part of our work we have created a test bed system that uses a covert DNS channel to exfiltrate data from a compromised host. Using this system we have carried out network traffic analysis that uses baseline comparisons as a means to fingerprint covert DNS activity. Even though detection of covert DNS activity is relatively straightforward, there is anecdotal evidence to suggest that most organisations do not filter or pay enough attention to DNS traffic and are therefore susceptible to data exfiltration attacks once a host on their network has been compromised. Our work shows that freely available covert DNS tools have particular traffic signatures that can be detected in order to mitigate data exfiltration and C&C traffic

DYNAMIC DATA EXFILTRATION OVER COMMON PROTOCOLS VIA SOCKET LAYER PROTOCOL CUSTOMIZATION

Obfuscated data exfiltration perpetrated by malicious actors presents a significant threat to organizations looking to protect sensitive data. Socket layer protocol customization presents the potential to enhance obfuscated data exfiltration by providing a protocol-agnostic means of embedding targeted data within application payloads of established socket connections. Fully evaluating and characterizing this technique will serve as an important step in the development of suitable mitigations. This thesis evaluated the performance of this method of data exfiltration through experimentation to determine its viability and identify its limitations. The evaluation assessed the effectiveness of exfiltration via socket layer customization with various application protocols and characterized its use to determine the most suitable protocols. Basic host-based and network-based security controls were introduced to test the exfiltration method’s ability to bypass typical security controls implemented to prevent data exfiltration. The experimentation results indicate that this exfiltration method is both viable and applicable across multiple application protocols. It proved flexible enough in its design and configuration to bypass basic host-based access controls and general network intrusion prevention system packet inspection. Deep packet inspection was identified as a potential solution; however, the required inspection and filtering granularity might make implementation infeasible.Office of Naval Research, Arlington, VA 22203-1995Outstanding ThesisPetty Officer First Class, United States NavyApproved for public release. Distribution is unlimited

DNS in Computer Forensics

The Domain Name Service (DNS) is a critical core component of the global Internet and integral to the majority of corporate intranets. It provides resolution services between the human-readable name-based system addresses and the machine operable Internet Protocol (IP) based addresses required for creating network level connections. Whilst structured as a globally dispersed resilient tree data structure, from the Global and Country Code Top Level Domains (gTLD/ccTLD) down to the individual site and system leaf nodes, it is highly resilient although vulnerable to various attacks, exploits and systematic failures

Network-based APT profiler

Constant innovation in attack methods presents a significant problem for the security community which struggles to remain current in attack prevention, detection and response. The practice of threat hunting provides a proactive approach to identify and mitigate attacks in real-time before the attackers complete their objective. In this research, I present a matrix of adversary techniques inspired by MITRE’s ATT&CK matrix. This study allows threat hunters to classify the actions of advanced persistent threats (APTs) according to network-based behaviors

Real time detection of malicious DoH traffic using statistical analysis

The DNS protocol plays a fundamental role in the operation of ubiquitous networks. All devices connected to these networks need DNS to work, both for traditional domain name to IP address translation, and for more advanced services such as resource discovery. DNS over HTTPS (DoH) solves certain security problems present in the DNS protocol. However, malicious DNS tunnels, a covert way of encapsulating malicious traffic in a DNS connection, are difficult to detect because the encrypted data prevents performing an analysis of the content of the DNS traffic. In this study, we introduce a real-time system for detecting malicious DoH tunnels, which is based on analyzing DoH traffic using statistical methods. Our research demonstrates that it is feasible to identify in real-time malicious traffic by analyzing specific parameters extracted from DoH traffic. In addition, we conducted statistical analysis to identify the most significant features that distinguish malicious traffic from benign traffic. Using the selected features, we achieved satisfactory results in classifying DoH traffic as either benign or malicious

A novel deep-learning based approach to DNS over HTTPS network traffic detection

Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification

Towards a Near-real-time Protocol Tunneling Detector based on Machine Learning Techniques

In the very last years, cybersecurity attacks have increased at an unprecedented pace, becoming ever more sophisticated and costly. Their impact has involved both private/public companies and critical infrastructures. At the same time, due to the COVID-19 pandemic, the security perimeters of many organizations expanded, causing an increase of the attack surface exploitable by threat actors through malware and phishing attacks. Given these factors, it is of primary importance to monitor the security perimeter and the events occurring in the monitored network, according to a tested security strategy of detection and response. In this paper, we present a protocol tunneling detector prototype which inspects, in near real time, a company's network traffic using machine learning techniques. Indeed, tunneling attacks allow malicious actors to maximize the time in which their activity remains undetected. The detector monitors unencrypted network flows and extracts features to detect possible occurring attacks and anomalies, by combining machine learning and deep learning. The proposed module can be embedded in any network security monitoring platform able to provide network flow information along with its metadata. The detection capabilities of the implemented prototype have been tested both on benign and malicious datasets. Results show 97.1% overall accuracy and an F1-score equals to 95.6%.Comment: 12 pages, 4 figures, 4 table

Federated Agentless Detection of Endpoints Using Behavioral and Characteristic Modeling

During the past two decades computer networks and security have evolved that, even though we use the same TCP/IP stack, network traffic behaviors and security needs have significantly changed. To secure modern computer networks, complete and accurate data must be gathered in a structured manner pertaining to the network and endpoint behavior. Security operations teams struggle to keep up with the ever-increasing number of devices and network attacks daily. Often the security aspect of networks gets managed reactively instead of providing proactive protection. Data collected at the backbone are becoming inadequate during security incidents. Incident response teams require data that is reliably attributed to each individual endpoint over time. With the current state of dissociated data collected from networks using different tools it is challenging to correlate the necessary data to find origin and propagation of attacks within the network. Critical indicators of compromise may go undetected due to the drawbacks of current data collection systems leaving endpoints vulnerable to attacks. Proliferation of distributed organizations demand distributed federated security solutions. Without robust data collection systems that are capable of transcending architectural and computational challenges, it is becoming increasingly difficult to provide endpoint protection at scale. This research focuses on reliable agentless endpoint detection and traffic attribution in federated networks using behavioral and characteristic modeling for incident response

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST