Technology independent honeynet description language

Several languages have been proposed for the task of describing networks of systems, either to help on managing, simulate or deploy testbeds for testing purposes. However, there is no one specifically designed to describe the honeynets, covering the specific characteristics in terms of applications and tools included in the honeypot systems that make the honeynet. In this paper, the requirements of honeynet description are studied and a survey of existing description languages is presented, concluding that a CIM (Common Information Model) match the basic requirements. Thus, a CIM like technology independent honeynet description language (TIHDL) is proposed. The language is defined being independent of the platform where the honeynet will be deployed later, and it can be translated, either using model-driven techniques or other translation mechanisms, into the description languages of honeynet deployment platforms and tools. This approach gives flexibility to allow the use of a combination of heterogeneous deployment platforms. Besides, a flexible virtual honeynet generation tool (HoneyGen) based on the approach and description language proposed and capable of deploying honeynets over VNX (Virtual Networks over LinuX) and Honeyd platforms is presented for validation purposes

Citrus:Orchestrating Security Mechanisms via Adversarial Deception

Despite the Internet being an apex of human achievement for many years, sophisticated targeted attacks are becoming more prevalent than ever before. Large scale data collection using threat sources such as honeypots have recently been employed to gather information relating to these attacks. While this data naturally details attack properties, there exists challenges in extracting the relevant information from vast data sets to provide valuable insight and a standard description of the attack. Traditionally, threats are identified through the use of signatures that are crafted manually through the composition of IOCs (Indicators of Compromise) extracted from telemetry captured during an attack process, which is often administered by an experienced engineer. These signatures have been proven effective in their use by IDSs (Intrusion Detection Systems) to detect emerging threats. However, little research has been made in automating the extraction of emerging IOCs and the generation of corresponding signatures which incorporate host artefacts. In this paper we present Citrus: a novel approach to the generation of signatures by incorporating host based telemetry extracted from honeypot endpoints. Leveraging this visibility at an endpoint grants a detailed understanding of bleeding edge attack tactics, techniques, and procedures gathered from host logs

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

SGNET: A Worldwide Deployable Framework to Support the Analysis of Malware Threat Models

The dependability community has expressed a growing interest in the recent years for the effects of malicious, ex-ternal, operational faults in computing systems, ie. intru-sions. The term intrusion tolerance has been introduced to emphasize the need to go beyond what classical fault toler-ant systems were able to offer. Unfortunately, as opposed to well understood accidental faults, the domain is still lack-ing sound data sets and models to offer rationales in the design of intrusion tolerant solutions. In this paper, we de-scribe a framework similar in its spirit to so called honey-farms but built in a way that makes its large-scale deploy-ment easily feasible. Furthermore, it offers a very rich level of interaction with the attackers without suffering from the drawbacks of expensive high interaction systems. The sys-tem is described, a prototype is presented as well as some preliminary results that highlight the feasibility as well as the usefulness of the approach.

Three Decades of Deception Techniques in Active Cyber Defense -- Retrospect and Outlook

Deception techniques have been widely seen as a game changer in cyber defense. In this paper, we review representative techniques in honeypots, honeytokens, and moving target defense, spanning from the late 1980s to the year 2021. Techniques from these three domains complement with each other and may be leveraged to build a holistic deception based defense. However, to the best of our knowledge, there has not been a work that provides a systematic retrospect of these three domains all together and investigates their integrated usage for orchestrated deceptions. Our paper aims to fill this gap. By utilizing a tailored cyber kill chain model which can reflect the current threat landscape and a four-layer deception stack, a two-dimensional taxonomy is developed, based on which the deception techniques are classified. The taxonomy literally answers which phases of a cyber attack campaign the techniques can disrupt and which layers of the deception stack they belong to. Cyber defenders may use the taxonomy as a reference to design an organized and comprehensive deception plan, or to prioritize deception efforts for a budget conscious solution. We also discuss two important points for achieving active and resilient cyber defense, namely deception in depth and deception lifecycle, where several notable proposals are illustrated. Finally, some outlooks on future research directions are presented, including dynamic integration of different deception techniques, quantified deception effects and deception operation cost, hardware-supported deception techniques, as well as techniques developed based on better understanding of the human element.Comment: 19 page

Framework For Modeling Attacker Capabilities with Deception

In this research we built a custom experimental range using opensource emulated and custom pure honeypots designed to detect or capture attacker activity. The focus is to test the effectiveness of a deception in its ability to evade detection coupled with attacker skill levels. The range consists of three zones accessible via virtual private networking. The first zone houses varying configurations of opensource emulated honeypots, custom built pure honeypots, and real SSH servers. The second zone acts as a point of presence for attackers. The third zone is for administration and monitoring. Using the range, both a control and participant-based experiment were conducted. We conducted control experiments to baseline and empirically explore honeypot detectability amongst other systems through adversarial testing. We executed a series of tests such as network service sweep, enumeration scanning, and finally manual execution. We also selected participants to serve as cyber attackers against the experiment range of varying skills having unique tactics, techniques and procedures in attempting to detect the honeypots. We have concluded the experiments and performed data analysis. We measure the anticipated threat by presenting the Attacker Bias Perception Profile model. Using this model, each participant is ranked based on their overall threat classification and impact. This model is applied to the results of the participants which helps align the threat to likelihood and impact of a honeypot being detected. The results indicate the pure honeypots are significantly difficult to detect. Emulated honeypots are grouped in different categories based on the detection and skills of the attackers. We developed a framework abstracting the deceptive process, the interaction with system elements, the use of intelligence, and the relationship with attackers. The framework is illustrated by our experiment case studies and the attacker actions, the effects on the system, and impact to the success

Studying Malicious Websites and the Underground Economy on the Chinese Web

The World Wide Web gains more and more popularity within China with more than 1.31 million websites on the Chinese Web in June 2007. Driven by the economic profits, cyber criminals are on the rise and use the Web to exploit innocent users. In fact, a real underground black market with thousand of participants has developed which brings together malicious users who trade exploits, malware, virtual assets, stolen credentials, and more. In this paper, we provide a detailed overview of this underground black market and present a model to describe the market. We substantiate our model with the help of measurement results within the Chinese Web. First, we show that the amount of virtual assets traded on this underground market is huge. Second, our research proofs that a significant amount of websites within China’s part of the Web are malicious: our measurements reveal that about 1.49% of the examined sites contain some kind of malicious content

Creating Synthetic Attacks with Evolutionary Algorithms for Proactive Defense of Industrial Control Systems

Industrial control systems (ICS) play an important role in critical infrastructure. Cybersecurity defenders can use honeypots (decoy systems) to capture and study malicious ICS traffic. A problem with existing ICS honeypots is their low interactivity, causing intruders to quickly abandon the attack attempts. This research aims to improve ICS honeypots by feeding them realistic artificially generated packets and examining their behavior to proactively identify functional gaps in defenses. Our synthetic attack generator (SAGO) uses an evolutionary algorithm on known attack traffic to create new variants of Log4j exploits (CVE-2021-44228) and Industroyer2 malware. We tested over 5,200 and 256 unique Log4j and IEC 104 variations respectively, with success rates up to 70 percent for Log4j and 40 percent for IEC 104. We identified improvements to our honeypot’s interactivity based on its responses to these attacks. Our technique can aid defenders in hardening perimeter protection against new attack variants