Biblio

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.

Common Vulnerability Scoring System (CVSS) is intended to capture the key characteristics of a vulnerability and correspondingly produce a numerical score to indicate the severity. Important efforts are conducted for building a CVSS stochastic model in order to provide a high-level risk assessment to better support cybersecurity decision-making. However, these efforts consider nothing regarding HPC (High-Performance Computing) networks using a Science Demilitary Zone (DMZ) architecture that has special design principles to facilitate data transition, analysis, and store through in a broadband backbone. In this paper, an HPCvul (CVSS-based vulnerability and risk assessment) approach is proposed for HPC networks in order to provide an understanding of the ongoing awareness of the HPC security situation under a dynamic cybersecurity environment. For such a purpose, HPCvul advocates the standardization of the collected security-related data from the network to achieve data portability. HPCvul adopts an attack graph to model the likelihood of successful exploitation of a vulnerability. It is able to merge multiple attack graphs from different HPC subnets to yield a full picture of a large HPC network. Substantial results are presented in this work to demonstrate HPCvul design and its performance.

The fifth-generation (5G) mobile telecom network has been garnering interest in both academia and industry, with better flexibility and higher performance compared to previous generations. Along with functionality improvements, new attack vectors also made way. Network operators and regulatory organizations wish to have a more precise idea about the security posture of 5G environments. Meanwhile, various security metrics for IT environments have been around and attracted the community’s attention. However, 5G-specific factors are less taken into consideration.This paper considers such 5G-specific factors to identify potential gaps if existing security metrics are to be applied to the 5G environments. In light of the layered nature and multi-ownership, the paper proposes a new approach to the modular computation of security metrics based on cross-layer projection as a means of information sharing between layers. Finally, the proposed approach is evaluated through simulation.

Cyber security risk assessment is very important to quantify the security level of communication-based train control (CBTC) systems. In this paper, a methodology is proposed to assess the cyber security risk of CBTC systems that integrates complex network theory and attack graph method. On one hand, in order to determine the impact of malicious attacks on train control, we analyze the connectivity of movement authority (MA) paths based on the working state of nodes, the connectivity of edges. On the other hand, attack graph is introduced to quantify the probabilities of potential attacks that combine multiple vulnerabilities in the cyber world of CBTC. Experiments show that our methodology can assess the security risks of CBTC systems and improve the security level after implementing reinforcement schemes.

Probabilistic security metrics estimate the vulnerability of a network in terms of the likelihood of an attacker reaching the goal states (of a network) by exploiting the attack graph paths. The probability computation depends upon several assumptions regarding the possible attack scenarios. In this paper, we extend the existing security metric to model networks with intrusion detection systems and their associated uncertainties and time latencies. We consider learning capabilities of attackers as well as detection systems. Estimation of risk is obtained by using the attack paths that are undetectable owing to the latency of the detection system. Thus, we define the overall vulnerability (of a network) as a function of the time window available to an attacker for repeated exploring (via learning) and exploitation of a network, before the attack is mitigated by the detection system. Finally, we consider the realistic scenario where an attacker explores and abandons various partial paths in the attack graph before the actual exploitation. A dynamic programming formulation of the vulnerability computation methodology is proposed for this scenario. The nature of these metrics are explained using a case study showing the vulnerability spectrum from the case of zero detection latency to a no detection scenario.

Aiming at the disadvantages of traditional methods such as low penetration path generation efficiency and low attack type recognition accuracy, an optimal penetration path generation algorithm based on the knowledge map power WEB system unknown attack is proposed. First, establish a minimum penetration path test model. And use the model to test the unknown attack of the penetration path under the power WEB system. Then, the ontology of the knowledge graph is designed. Finally, the design of the optimal penetration path generation algorithm based on the knowledge graph is completed. Experimental results show that the algorithm improves the efficiency of optimal penetration path generation, overcomes the shortcomings of traditional methods that can only describe known attacks, and can effectively guarantee the security of power WEB systems.

The numerous attacks on ICS systems have made severe threats to critical infrastructure. Extensive studies have focussed on the risk assessment of discovering vulnerabilities. However, to identify Zero-day vulnerabilities is challenging because they are unknown to defenders. Here we sought to measure ICS system zero-day risk by building an enhanced attack graph for expected attack path exploiting zero-day vulnerability. In this study, we define the security metrics of Zero-day vulnerability for an ICS. Then we created a Zero-day attack graph to guide how to harden the system by measuring attack paths that exploiting zero-day vulnerabilities. Our studies identify the vulnerability assessment method on ICS systems considering Zero-day Vulnerability by zero-day attack graph. Together, our work is essential to ICS systems security. By assessing unknown vulnerability risk to close the imbalance between attackers and defenders.

In this paper, we propose a decision support process that is designed to help network and security operators in understanding the complexity of a current security situation and decision making concerning ongoing cyber-attacks and threats. The process focuses on enterprise missions and uses a graph-based mission decomposition model that captures the missions, underlying hosts and services in the network, and functional and security requirements between them. Knowing the vulnerabilities and attacker's position in the network, the process employs logical attack graphs and Bayesian network to infer the probability of the disruption of the confidentiality, integrity, and availability of the missions. Based on the probabilities of disruptions, the process suggests the most resilient mission configuration that would withstand the current security situation.

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.

Attack graph is an effective way to analyze the vulnerabilities for industrial control networks. We develop a vulnerability correlation method and a practical visualization technology for industrial control network. First of all, we give a complete attack graph analysis for industrial control network, which focuses on network model and vulnerability context. Particularly, a practical attack graph algorithm is proposed, including preparing environments and vulnerability classification and correlation. Finally, we implement a three-dimensional interactive attack graph visualization tool. The experimental results show validation and verification of the proposed method.

An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components

Network security is an important issue in today's world with existence of network systems that communicate data and information about all aspects of our life, work and business. Network security is an important issue with connected networks and data communication between organisations of that specialized in different areas. Network security engineers spend a considerable amount of time to investigate network for security breaches and to enhance the security of their networks and data communications on their networks. They use Attack Graphs (AGs) which are graphical representation of networks to assist them in analysing large networks. With increase size of networks and their complexity, the use of attack graphs alone does not provide the necessary risk analysis and assessment facilities. There is a need for automated intelligent systems such as multiagent systems to assist in analysing, assessing and testing networks. Network systems changes with the increase in the size of organisation and connectivity of network of organisations based on the business needs or organisational or governmental rules and regulations. In this paper a multi-agent system is developed assist in analysing interconnected network to identify security risks. The multi-agent system is capable of security network analysis to identify paths using an attack graph of the network under consideration to protect network systems, as the networks grow and change, against possible attacks. The multiagent system uses a model developed by Mohammadian [3] for converting AGs to Fuzzy Cognitive Maps (FCMs) to identify attack paths from attack graphs and perform security risk analysis. In this paper a novel decision-making approach using FCMs is employed.

Cyber-Physical-Systems are subject to cyber-attacks due to existing vulnerabilities in the various components constituting them. System Resiliency is concerned with the extent the system is able to bounce back to a normal state under attacks. In this paper, two communication Networks are analyzed, formally described, and modeled using Architecture Analysis & Design Language (AADL), identifying their architecture, connections, vulnerabilities, resources, possible attack instances as well as their pre-and post-conditions. The generated network models are then verified against a security property using JKind model checker integrated tool. The union of the generated attack sequences/scenarios resulting in overall network compromise (given by its loss of stability) is the Attack graph. The generated Attack graph is visualized graphically using Unity software, and then used to assess the worst Level of Resilience for both networks.

The proliferation of IoT devices in smart homes, hospitals, and enterprise networks is wide-spread and continuing to increase in a superlinear manner. The question is: how can one assess the security of an IoT network in a holistic manner? In this paper, we have explored two dimensions of security assessment- using vulnerability information and attack vectors of IoT devices and their underlying components (compositional security scores) and using SIEM logs captured from the communications and operations of such devices in a network (dynamic activity metrics). These measures are used to evaluate the security of IoT devices and the overall IoT network, demonstrating the effectiveness of attack circuits as practical tools for computing security metrics (exploitability, impact, and risk to confidentiality, integrity, and availability) of the network. We decided to approach threat modeling using attack graphs. To that end, we propose the notion of attack circuits, which are generated from input/output pairs constructed from CVEs using NLP, and an attack graph composed of these circuits. Our system provides insight into possible attack paths an adversary may utilize based on their exploitability, impact, or overall risk. We have performed experiments on IoT networks to demonstrate the efficacy of the proposed techniques.

Because of the high-value data of an enterprise, sophisticated cyber-attacks targeted at enterprise networks have become prominent. Attack graphs are useful tools that facilitate a scalable security analysis of enterprise networks. However, the administrators face difficulties in effectively modelling security problems and making right decisions when constructing attack graphs as their risk assessment experience is often limited. In this paper, we propose an innovative method of security assessment through an ontology- and graph-based approach. An ontology is designed to represent security knowledge such as assets, vulnerabilities, attacks, countermeasures, and relationships between them in a common vocabulary. An efficient algorithm is proposed to generate an attack graph based on the inference ability of the security ontology. The proposed algorithm is evaluated with different sizes and topologies of test networks; the results show that our proposed algorithm facilitates a scalable security analysis of enterprise networks.

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

This paper surveys some prior work regarding attack models in a cyber-physical system and discusses the potential benefits. For comparison, the full paper will model a bad data injection attack scenario in power grid using the surveyed prior work.

A detailed model of an attack on the power grid involves both a preparation stage as well as an execution stage of the attack. This paper introduces a novel Hybrid Attack Model (HAM) that combines Probabilistic Learning Attacker, Dynamic Defender (PLADD) model and a Markov Chain model to simulate the planning and execution stages of a bad data injection attack in power grid. We discuss the advantages and limitations of the prior work models and of our proposed Hybrid Attack Model and show that HAM is more effective compared to individual PLADD or Markov Chain models.

Organizations perform security analysis for assessing network health and safe-guarding their growing networks through Vulnerability Assessments (AKA VA Scans). The output of VA scans is reports on individual hosts and its vulnerabilities, which, are of little use as the origin of the attack can't be located from these. Attack Graphs, generated without an in-depth analysis of the VA reports, are used to fill in these gaps, but only provide cursory information. This study presents an effective model of depicting the devices and the data flow that efficiently identifies the weakest nodes along with the concerned vulnerability's origin.The complexity of the attach graph using MulVal has been greatly reduced using the proposed approach of using the risk and CVSS base score as evaluation criteria. This makes it easier for the user to interpret the attack graphs and thus reduce the time taken needed to identify the attack paths and where the attack originates from.

In this paper, we propose a game-attack graph-based risk assessment model for industrial Internet system. Firstly, use non-destructive asset profiling to scan components and devices included in the system and their open services and communication protocols. Further compare the CNVD and CVE to find the vulnerability through the search engine keyword segment matching method, and generate an asset threat list. Secondly, build the attack rule base based on the network information, and model the system using the attribute attack graph. Thirdly, combine the game theory with the idea of the established model. Finally, optimize and quantify the analysis to get the best attack path and the best defense strategy.

SCADA/ICS (Supervisory Control and Data Acqui-sition/Industrial Control Systems) networks are becoming targets of advanced multi-faceted attacks, and use of attack-graphs has been proposed to model complex attacks scenarios that exploit interdependence among existing atomic vulnerabilities to stitch together the attack-paths that might compromise a system-level security property. While such analysis of attack scenarios enables security administrators to establish appropriate security measurements to secure the system, practical considerations on time and cost limit their ability to address all system vulnerabilities at once. In this paper, we propose an approach that identifies label-cuts to automatically identify a set of critical-attacks that, when blocked, guarantee system security. We utilize the Strongly-Connected-Components (SCCs) of the given attack graph to generate an abstracted version of the attack-graph, a tree over the SCCs, and next use an iterative backward search over this tree to identify set of backward reachable SCCs, along with their outgoing edges and their labels, to identify a cut with a minimum number of labels that forms a critical-attacks set. We also report the implementation and validation of the proposed algorithm to a real-world case study, a SCADA network for a water treatment cyber-physical system.

In enterprise environments, the amount of managed assets and vulnerabilities that can be exploited is staggering. Hackers' lateral movements between such assets generate a complex big data graph, that contains potential hacking paths. In this vision paper, we enumerate risk-reduction security requirements in large scale environments, then present the Agile Security methodology and technologies for detection, modeling, and constant prioritization of security requirements, agile style. Agile Security models different types of security requirements into the context of an attack graph, containing business process targets and critical assets identification, configuration items, and possible impacts of cyber-attacks. By simulating and analyzing virtual adversary attack paths toward cardinal assets, Agile Security examines the business impact on business processes and prioritizes surgical requirements. Thus, handling these requirements backlog that are constantly evaluated as an outcome of employing Agile Security, gradually increases system hardening, reduces business risks and informs the IT service desk or Security Operation Center what remediation action to perform next. Once remediated, Agile Security constantly recomputes residual risk, assessing risk increase by threat intelligence or infrastructure changes versus defender's remediation actions in order to drive overall attack surface reduction.

By accurately measuring risk for enterprise networks, attack graphs allow network defenders to understand the most critical threats and select the most effective countermeasures. This paper describes substantial enhancements to the NetSPA attack graph system required to model additional present-day threats (zero-day exploits and client-side attacks) and countermeasures (intrusion prevention systems, proxy firewalls, personal firewalls, and host-based vulnerability scans). Point-to-point reachability algorithms and structures were extensively redesigned to support "reverse" reachability computations and personal firewalls. Host-based vulnerability scans are imported and analyzed. Analysis of an operational network with 84 hosts demonstrates that client-side attacks pose a serious threat. Experiments on larger simulated networks demonstrated that NetSPA's previous excellent scaling is maintained. Less than two minutes are required to completely analyze a four-enclave simulated network with more than 40,000 hosts protected by personal firewalls.

The network attack graph is a powerful tool for analyzing network security, but the generation of a large-scale graph is non-trivial. The main challenge is from the explosion of network state space, which greatly increases time and storage costs. In this paper, three parallel algorithms are proposed to generate scalable attack graphs. An OpenMP-based programming implementation is used to test their performance. Compared with the serial algorithm, the best performance from the proposed algorithms provides a 10X speedup.

Binary particle swarm optimization (BPSO) is a technique widely used to solve combinatorial problems. In this paper, we propose a variant of BPSO to find most likely attack paths in an attack graph. The aim is to find an attack path with the highest attack probability and least path length. In such combinatorial optimization problem, the set of feasible solutions is usually discrete and an exhaustive search may lead to unnecessary examination of those segments of the search space, which are assured to not include a solution. The paper introduces the concept of bounding the solution space of BPSO. The minimum and maximum value of each objective called bound of the solution is computed. The search space of BPSO is restricted within these solution bounds and hence we name our approach as bounded binary particle swarm optimization (BBPSO). By bounding the solution space, those particles of BPSO which are guaranteed to be infeasible are not considered for feasibility check. Experimental results show that the proposed approach provide a 50 percent performance improvement as compared to the conventional BPSO.