Biblio

In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.

Conpot is a low-interaction SCADA honeypot system that mimics a Siemens S7-200 proprietary device on default deployments. Honeypots operating using standard configurations can be easily detected by adversaries using scanning tools such as Shodan. This study focuses on the capabilities of the Conpot honeypot, and how these competences can be used to lure attackers. In addition, the presented research establishes a framework that enables for the customized configuration, thereby enhancing its functionality to achieve a high degree of deceptiveness and realism when presented to the Shodan scanners. A comparison between the default and configured deployments is further conducted to prove the modified deployments' effectiveness. The resulting annotations can assist cybersecurity personnel to better acknowledge the effectiveness of the honeypot's artifacts and how they can be used deceptively. Lastly, it informs and educates cybersecurity audiences on how important it is to deploy honeypots with advanced deceptive configurations to bait cybercriminals.

Today's global network is filled with attackers both live and automated seeking to identify and compromise vulnerable devices, with initial scanning and attack activity occurring within minutes or even seconds of being connected to the Internet. To better understand these events, honeypots can be deployed to monitor and log activity by simulating actual Internet facing services such as SSH, Telnet, HTTP, or FTP, and malicious activity can be logged as attempts are made to compromise them. In this study six multi-service honeypots are deployed in locations around the globe to collect and catalog traffic over a period of several months between March and December, 2020. Analysis is performed on various characteristics including source and destination IP addresses and port numbers, usernames and passwords utilized, commands executed, and types of files downloaded. In addition, Cowrie log data is restructured to observe individual attacker sessions, study command sequences, and monitor tunneling activity. This data is then correlated across honeypots to compare attack and traffic patterns with the goal of learning more about the tactics being employed. By gathering data gathered from geographically separate zones over a long period of time a greater understanding can be developed regarding attacker intent and methodology, can aid in the development of effective approaches to identifying malicious behavior and attack sources, and can serve as a cyber-threat intelligence feed.

The extensive utilization of network by smart devices, computers and servers makes it vulnerable to malicious activities where intruders and attackers tends to violate system security policies and authenticity to slither essential information. Honeypots are designed to create a virtual trap against hackers. The trap is to attract intruders and gather information about attackers and attack features. Honeypots mimics as a computer application, billing systems, webpages and client server-based applications to understand attackers behavior by gathering attack features and common foot prints used by hackers to forge information. In this papers, authors analyse amazon web services honeypot (AWSH) data to determine geo-spatial distribution of targeted attacks originated from different locations. The categorization of attacks is made on the basis of internet protocols and frequency of attack occurrences worldwide.

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

The security of Industrial Control system (ICS) of cybersecurity networks ensures that control equipment fails and that regular procedures are available at its control facilities and internal industrial network. For this reason, it is essential to improve the security of industrial control facility networks continuously. Since network security is threatening, industrial installations are irreparable and perhaps environmentally hazardous. In this study, the industrialized Early Intrusion Detection System (EIDS) was used to modify the Intrusion Detection System (IDS) method. The industrial EIDS was implemented using routers, IDS Snort, Industrial honeypot, and Iptables MikroTik. EIDS successfully simulated and implemented instructions written in IDS, Iptables router, and Honeypots. Accordingly, the attacker's information was displayed on the monitoring page, which had been designed for the ICS. The EIDS provides cybersecurity and industrial network systems against vulnerabilities and alerts industrial network security heads in the shortest possible time.

Cybercrime is growing dramatically in the technological world nowadays. World Wide Web criminals exploit the personal information of internet users and use them to their advantage. Unethical users leverage the dark web to buy and sell illegal products or services and sometimes they manage to gain access to classified government information. A number of illegal activities that can be found in the dark web include selling or buying hacking tools, stolen data, digital fraud, terrorists activities, drugs, weapons, and more. The aim of this project is to collect evidence of any malicious activity in the dark web by using computer security mechanisms as traps called honeypots.

To gain strategic insight into defending against the network reconnaissance stage of advanced persistent threats, we recreate the escalating competition between scans and deceptive views on a Software Defined Network (SDN). Our threat model presumes the defense is a deceptive network view unique for each node on the network. It can be configured in terms of the number of honeypots and subnets, as well as how real nodes are distributed across the subnets. It assumes attacks are NMAP ping scans that can be configured in terms of how many IP addresses are scanned and how they are visited. Higher performing defenses detect the scanner quicker while leaking as little information as possible while higher performing attacks are better at evading detection and discovering real nodes. By using Artificial Intelligence in the form of a competitive coevolutionary genetic algorithm, we can analyze the configurations of high performing static defenses and attacks versus their evolving adversary as well as the optimized configuration of the adversary itself. When attacks and defenses both evolve, we can observe that the extent of evolution influences the best configurations.

We propose a novel cross-stack sensor framework for realizing lightweight, context-aware, high-interaction network and endpoint deceptions for attacker disinformation, misdirection, monitoring, and analysis. In contrast to perimeter-based honeypots, the proposed method arms production workloads with deceptive attack-response capabilities via injection of booby-traps at the network, endpoint, operating system, and application layers. This provides defenders with new, potent tools for more effectively harvesting rich cyber-threat data from the myriad of attacks launched by adversaries whose identities and methodologies can be better discerned through direct engagement rather than purely passive observations of probe attempts. Our research provides new tactical deception capabilities for cyber operations, including new visibility into both enterprise and national interest networks, while equipping applications and endpoints with attack awareness and active mitigation capabilities.

Inspired by the simple, yet effective, method of tweeting gibberish to attract automated social agents (bots), we attempt to create localised honeypots in the South African political context. We produce a series of defined techniques and combine them to generate interactions from users on Twitter. The paper offers two key contributions. Conceptually, an argument is made that honeypots should not be confused for bot detection methods, but are rather methods to capture low-quality users. Secondly, we successfully generate a list of 288 local low quality users active in the political context.

This work explores attack and attacker profiles using a VoIP-based Honeypot. We implemented a low interaction honeypot environment to identify the behaviors of the attackers and the services most frequently used. We watched honeypot for 180 days and collected 242.812 events related to FTP, SIP, MSSQL, MySQL, SSH, SMB protocols. The results provide an in-depth analysis about both attacks and attackers profile, their tactics and purposes. It also allows understanding user interaction with a vulnerable honeypot environment.

Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.

The ever-increasing sophistication of malware has made malicious binary collection and analysis an absolute necessity for proactive defenses. Meanwhile, malware authors seek to harden their binaries against analysis by incorporating environment detection techniques, in order to identify if the binary is executing within a virtual environment or in the presence of monitoring tools. For security researchers, it is still an open question regarding how to remove the artifacts from virtual machines to effectively build deceptive "honeypots" for malware collection and analysis. In this paper, we explore a completely different and yet promising approach by using Linux containers. Linux containers, in theory, have minimal virtualization artifacts and are easily deployable on low-power devices. Our work performs the first controlled experiments to compare Linux containers with bare metal and 5 major types of virtual machines. We seek to measure the deception capabilities offered by Linux containers to defeat mainstream virtual environment detection techniques. In addition, we empirically explore the potential weaknesses in Linux containers to help defenders to make more informed design decisions.

This article is about study of honeypots. In this work, we use some honeypot sensors deployment and analysis to identify, currently, what are the main attacks and security breaches explored by attackers to compromise systems. For example, a common server or service exposed to the Internet can receive a million of hits per day, but sometimes would not be easy to identify the difference between legitimate access and an attacker trying to scan, and then, interrupt the service. Finally, the objective of this research is to investigate the efficiency of the honeypots sensors to identify possible safety gaps and new ways of attacks. This research aims to propose some guidelines to avoid or minimize the damage caused by these attacks in real systems.

In a world where highly skilled actors involved in cyber-attacks are constantly increasing and where the associated underground market continues to expand, organizations should adapt their defence strategy and improve consequently their security incident management. In this paper, we give an overview of Advanced Persistent Threats (APT) attacks life cycle as defined by security experts. We introduce our own compiled life cycle model guided by attackers objectives instead of their actions. Challenges and opportunities related to the specific camouflage actions performed at the end of each APT phase of the model are highlighted. We also give an overview of new APT protection technologies and discuss their effectiveness at each one of life cycle phases.

Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic. In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\textbackslash% certainty.

Defending information systems against advanced attacks is a challenging task; even if all the systems have been properly updated and all the known vulnerabilities have been patched, there is still the possibility of previously unknown zero day attack compromising the system. Honeypots offer a more proactive tool for detecting possible attacks. What is more, they can act as a tool for understanding attackers intentions. In this paper, we propose a design for a diversified honeypot. By increasing variability present in software, diversification decreases the number of assumptions an attacker can make about the target system.

In this work, we address the problem of designing and implementing honeypots for Industrial Control Systems (ICS). Honeypots are vulnerable systems that are set up with the intent to be probed and compromised by attackers. Analysis of those attacks then allows the defender to learn about novel attacks and general strategy of the attacker. Honeypots for ICS systems need to satisfy both traditional ICT requirements, such as cost and maintainability, and more specific ICS requirements, such as time and determinism. We propose the design of a virtual, high-interaction and server-based ICS honeypot to satisfy the requirements, and the deployment of a realistic, cost-effective, and maintainable ICS honeypot. An attacker model is introduced to complete the problem statement and requirements. Based on our design and the MiniCPS framework, we implemented a honeypot mimicking a water treatment testbed. To the best of our knowledge, the presented honeypot implementation is the first academic work targeting Ethernet/IP based ICS honeypots, the first ICS virtual honeypot that is high-interactive without the use of full virtualization technologies (such as a network of virtual machines), and the first ICS honeypot that can be managed with a Software-Defined Network (SDN) controller.

Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.

Honey pots and honey nets are popular tools in the area of network security and network forensics. The deployment and usage of these tools are influenced by a number of technical and legal issues, which need to be carefully considered together. In this paper, we outline privacy issues of honey pots and honey nets with respect to technical aspects. The paper discusses the legal framework of privacy, legal ground to data processing, and data collection. The analysis of legal issues is based on EU law and is supported by discussions on privacy and related issues. This paper is one of the first papers which discuss in detail privacy issues of honey pots and honey nets in accordance with EU law.

The United States has US CYBERCOM to protect the US Military Infrastructure and DHS to protect the nation's critical cyber infrastructure. These organizations deal with wide ranging issues at a national level. This leaves local and state governments to largely fend for themselves in the cyber frontier. This paper will focus on how to determine the threat to a community and what indications and warnings can lead us to suspect an attack is underway. To try and help answer these questions we utilized the concepts of Honey pots and Honey nets and extended them to a multi-organization concept within a geographic boundary to form a Honey Community. The initial phase of the research done in support of this paper was to create a fictitious community with various components to entice would-be attackers and determine if the use of multiple sectors in a community would aid in the determination of an attack.

The number of vulnerabilities and reported attacks on Web systems are showing increasing trends, which clearly illustrate the need for better understanding of malicious cyber activities. In this paper we use clustering to classify attacker activities aimed at Web systems. The empirical analysis is based on four datasets, each in duration of several months, collected by high-interaction honey pots. The results show that behavioral clustering analysis can be used to distinguish between attack sessions and vulnerability scan sessions. However, the performance heavily depends on the dataset. Furthermore, the results show that attacks differ from vulnerability scans in a small number of features (i.e., session characteristics). Specifically, for each dataset, the best feature selection method (in terms of the high probability of detection and low probability of false alarm) selects only three features and results into three to four clusters, significantly improving the performance of clustering compared to the case when all features are used. The best subset of features and the extent of the improvement, however, also depend on the dataset.