Quantitative Evaluation of Extensive Vulnerability Set Using Cost Benefit Analysis

In view of the characteristics of large-scale, coordinated and multi-stage network attacks, a network security situation assessment method based on attack Graph model was proposed. Firstly, the network attack behavior characteristics are constructed by integrating the spatio-temporal characteristics of attack events and multi-source alarm data. Secondly, attack nodes are mapped based on alarm information to associate multi-step attack paths. Thirdly, on the basis of constructing the attack Graph, the transfer probability table of attack nodes is constructed combining with the transfer sequence, and the transfer probability is introduced into the attack graph to infer the attack intention of the attacker. Finally, according to the maximum possible attack path, the security situation of attack nodes with high probability is evaluated, and the security situation of potential attack nodes after network attack is scientifically quantified, providing theoretical support and scientific basis for network security management personnel to do a good job of protection in advance.

As networks continue to grow in size and complexity, automatic assessment of the security vulnerability becomes increasingly important. The typical means by which an attacker breaks into a network is through a series of exploits, where each exploit in the series satisfies the pre-condition for subsequent exploits and makes a causal relationship among them. Such a series of exploits constitutes an attack path where the set of all possible attack paths form an attack graph. Attack graphs reveal the threat by enumerating all possible sequences of exploits that can be followed to compromise a given critical resource. The contribution of this chapter is to identify the most probable attack path based on the attack surface measures of the individual hosts for a given network and also identify the minimum possible network securing options for a given attack graph in an automated fashion. The identified network securing options are exhaustive and the proposed approach aims at detecting cycles in forward reachable attack graphs. As a whole, the chapter deals with identification of probable attack path and risk mitigation which may facilitate in improving the overall security of an enterprise network.

Due to advances in network technologies, the amount of people using networks is rising rapidly. This has resulted in a large amount of transmission information being generated and moved across the network. However, this data is vulnerable to attacks and intrusions. To prevent network intrusions, security measures must be implemented, which can detect anomalies and identify potential threats. Network security researchers and labs have done extensive research in network security. The purpose of this study was to perform a noninvasive inspection to give a large general mechanism on recent advances in abnormality detection. The study reviewed recent research published in the past five years, which examined new technologies and potential future opportunities in anomaly detection. The literature review focused specifically on anomaly detection systems used in network traffic. This included various applications such as Wireless Sensor Networks (WSN), Internet of Things (IoT), High Performance Computing, Industrial Control Systems (ICS), and Software Defined Networking (SDN) environments. The review concludes by highlighting several unresolved issues that need to be addressed in order to improve anomaly detection systems

Attack graphs are the graphs which describe attack scenarios, play important roles in analyzing network threats. These attack graphs are able to reveal such potential threats by evaluating the all possible sequences that an attacker can follow to compromise given critical resources or nodes. An Attack graph specifies an attack scenario that results in compromising network values. There are so many methods proposed to evaluate the network security in attack graphs. But no method specifies the overhead occurred due to the evaluation of network security at each and every node. This paper addresses the problem of overhead occurred due to the network security evaluation in Short-Normalizedattack graphs by evaluating a factor called network security risk. In this paper first the possible n valid attack paths are going to be calculated and then the security risk is going to be calculated for those n valid paths. This security risk denotes the amount of overhead occurred due to this evaluation.

As computer networks continue to grow, it becomes increasingly more important to automate the process of evaluating their vulnerability to attacks. Despite the best efforts of software architects and developers, network hosts inevitably contain a number of vulnerabilities. Hence, it is not feasible for a network administrator to remove all vulnerabilities present in the network hosts. Therefore, the recent focus in security of such networks is on analysis of vulnerabilities globally, finding exploits that are more critical, and preventing them to thwart an intruder. When evaluating the security of a network, it is rarely enough to consider the presence or absence of isolated vulnerabilities. This is because intruders often combine exploits against multiple vulnerabilities in order to reach their goals (Abadi & Jalili, 2005). For example, an intruder might exploit the vulnerability of a particular version of FTP to overwrite the .rhosts file on a victim host. In the next step, the intruder could remotely log in to the victim. In a subsequent step, the intruder could use the victim host as a base to launch another exploit on a new victim, and so on. (Phillips & Swiler, 1998) proposed the concept of attack graphs, where each node represents a possible attack state. Edges represent a change of state caused by a single action taken by the intruder. (Sheyner et al., 2002) used a modified version of the model checker NuSMV (NuSMV, 2010) to produce attack graphs. (Ammann et al., 2002) introduced a monotonicity assumption and used it to develop a polynomial algorithm to encode all of the edges in an attack graph without actually computing the graph itself. These attack graphs are essentially similar to (Phillips & Swiler, 1998), where any path in the graph from an initial node to a goal node shows a sequence of exploits that an intruder can launch to reach his goal. (Noel et al., 2005) presented a number of techniques for managing attack graph complexity through visualization. (Mehta et al., 2006) presented a ranking scheme for the nodes of an attack graph. Rank of a node shows its importance based on factors like the probability of an intruder reaching that node. Given a ranked attack graph, the system administrator can concentrate on relevant subgraphs to figure out how to start deploying security measures. (Ou et al., 2006) presented logical attack graphs, which directly illustrate logical dependencies among attack goals and configuration information. Their attack graph generation tool builds upon MulVAL (Ou et al., 2005), a network security analyzer based on logical programming. The aim of minimization analysis of network attack graphs is to find a minimum critical set of exploits that completely disconnect the initial nodes and the goal nodes of the graph.

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method.

Attack graphs are important tools for analyzing network security vulnerabilities. Recently, the generation method of attack graphs is a hot topic to the security researchers. As previous works encounter the scalability problem and inaccurate input information problem, we propose a novel method to automatic construction of attack graphs based on probability. After introducing prior-probability, match-probability,and transition-probability into attack graphs generation process, we develop a new attack model and relevant generation algorithms. Our method uses threshold and key states to control the scale of result attack graphs with important attack paths reserved. The following experiments show our approach could get meaningful results with less time and space, especially when one wants to get a few shortest attack paths quickly.

Vulnerabilities do enormous harm to personal computers. What is worse, with emergence of much more applications of computer networks nowadays, network security caused by vulnerabilities has become an important issue. And attacks turned more proficient, making it much more difficult to protect our computer networks. In view of the security issue of complex network, effective identification of attack paths contributes to network security defense a lot. In order to obtain the paths, attack graphs are used. And to construct it requires preconditions like vulnerabilities information, network configuration, and etc. In this paper, we present a simplified model to construct the attack paths. We discuss the construction of attack paths taking advantage of logic programming and corresponding preconditions generated by the logic rule and facts.

Attack Graphs have been widely used by the network security administrators to gain an understanding of possible attack paths, an attacker may follow to compromise critical resources. As networks get larger and more complex, one needs to use databases to perform iterative, interactive analysis tasks with attack graphs. In this paper we investigate how property graph based graph databases like Neo4j can be effectively used towards this application. We show that extending the standard property graph model with a constraint specification mechanism aids attack graph modeling and interactive analytics. We briefly sketch a preliminary attempt to implement a constraint layer over Neo4j and show how attack graph generation and analysis can be performed faithfully in Neo4j using the extended model.

Nowadays, networks are exposed to a set of risks and threats that can potentially cause damage and losses for companies. The security of networks must be assessed in order to measure the effectiveness of the protective measures that have been implemented. However, the impact of the dynamic behavior of these systems on the attacker's strategy is rarely considered. In this paper, we propose an attack graph-based solution that consider the evolution of system properties such as network topology changes, vulnerability discovery and patching, as well as attack detection and wiping of some system components. The topology of the attack graph evolves over time considering the evolution of the system state. Several simulations of the attacker infiltration in the system are performed by following the attack paths present in the attack graph in order to assess the security of the system. The proposed solution has been tested on a use case where a user is in remote work. By considering the changes in the network topology, new attack paths can be identified.

The identification and analysis of potential paths that an adversary may exploit to attack Cyber Physical Systems comprising sub-systems enables the comprehensive understanding of the attacks and the impact that may have to the overall system, thus facilitating the definition of appropriate countermeasures that will satisfy the pertinent security requirements. To this end, several attack modelling techniques can be employed, the attack graph being the most prevalent among them. Unfortunately, the discovery and analysis of all possible attack paths in an attack graph is not possible in systems even of a moderate size. In this work we propose a novel systematic method for discovering and analyzing attack paths in real-world scale interconnected Cyber Physical Systems. The method considers the criticality of each sub-system in discovering paths and the risk to the overall system that each path presents to analyze and prioritize paths. We illustrate the workings of the method by applying to the navigational Cyber Physical Systems of the Cyber-Enabled Ship to identify and analyze highly critical attack paths originating from the Automatic Identification System (AIS) and targeting the Autonomous Navigation System (ANS).

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.

In order to discover the network vulnerability timely and solve the very serious problems of network security, this paper puts forward the attack graph which is based on intrusion detection method. The method uses the generation global network attack graph algorithm to build network initial attack graph, and call attack graph optimization algorithm to remove global attack graph unreasonable path, and achieve the goal of simply attack graph. Finally, management personnel get the basis which is computed nodes in each state attack graph algorithms degree of loss to optimize the network security. Experiments show that the intrusion detection method is reasonable and effective, and has the advantages of simple and easy.

Attack graph plays an important role in network security, as it directly shows the existence of vulnerabilities in network and how attackers use these vulnerabilities to implement an effective attack, the analysis on the attack graph or the simulation of dynamic attacks through attack graph can help us easily find out the vulnerabilities in network, and take corresponding security measures, in order to strengthen network security. Previous attack graph generation methods are generally not suitable for large network, because of their high complexity of time, high consumption of space, and the large scale of attack graphs. Based on substantive analysis of the vulnerabilities in network, this paper describes a model for automatically generating and analyzing network attack graph. Besides, a prototype system bases on this model has been designed. At last, this prototype system was tested by a model network we built, and it was proved to be simple, flexible, and efficient.

Exploring the efficient vulnerability scanning and detection technology of various tools is one fundamental aim of network security. This network security technique ameliorates the tremendous number of IoT security challenges and the threats they face daily. However, among various tools, Shodan Eye scanning technology has proven to be very helpful for network administrators and security personnel to scan, detect and analyze vulnerable ports and traffic in organizations' networks. This work presents a simulated network scanning activity and manual vulnerability analysis of an internet-connected industrial equipment of two chosen industrial networks (Industry A and B) by running Shodan on a virtually hosted (Oracle Virtual Box)-Linux-based operating system (Kali Linux). The result shows that the shodan eye is a a promising tool for network security and efficient vulnerability research.