Software development needs continuous quality control for a timely detection and removal of quality problems. This includes frequent quality assessments, which need to be automated as far as possible to be feasible. One way of automation in assessing the security of software are application scanners that test an executing software for vulnerabilities. At present, common quality assessments do not integrate such scanners for giving an overall quality statement. This paper presents an integration of application scanners into a general quality assessment method based on explicit quality models and Bayesian nets. Its applicability and the detection capabilities of common scanners are investigated in a case study with two open-source web shops.
@cite_20 use Dynamic Bayesian Networks to investigate the security of networked systems. Their focus is more on the combined effects of different vulnerabilities as opposed to a complete quality statement for the system incorporating scan results.
@cite_20
2030014066
Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics have generally focused on measuring individual vulnerabilities without considering their combined effects. Our previous work tackle this issue by exploring the causal relationships between vulnerabilities encoded in an attack graph. However, the evolving nature of vulnerabilities and networks has largely been ignored. In this paper, we propose a Dynamic Bayesian Networks (DBNs)-based model to incorporate temporal factors, such as the availability of exploit codes or patches. Starting from the model, we study two concrete cases to demonstrate the potential applications. This novel model provides a theoretical foundation and a practical framework for continuously measuring network security in a dynamic environment. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . Buffer overflows have been the most common form of security vulnerability for the last ten years. More over, buffer overflow vulnerabilities dominate the area of remote network penetration vulnerabilities, where an anonymous Internet user seeks to gain partial or total control of a host. If buffer overflow vulnerabilities could be effectively eliminated, a very large portion of the most serious security threats would also be eliminated. In this paper, we survey the various types of buffer overflow vulnerabilities and attacks, and survey the various defensive measures that mitigate buffer overflow vulnerabilities, including our own StackGuard method. We then consider which combinations of techniques can eliminate the problem of buffer overflow vulnerabilities, while preserving the functionality and performance of existing systems. In the last years there has been an increasing interest in the security of process control and SCADA systems. Furthermore, recent computer attacks such as the Stuxnet worm, have shown there are parties with the motivation and resources to effectively attack control systems. While previous work has proposed new security mechanisms for control systems, few of them have explored new and fundamentally different research problems for securing control systems when compared to securing traditional information technology (IT) systems. In particular, the sophistication of new malware attacking control systems--malware including zero-days attacks, rootkits created for control systems, and software signed by trusted certificate authorities--has shown that it is very difficult to prevent and detect these attacks based solely on IT system information. In this paper we show how, by incorporating knowledge of the physical system under control, we are able to detect computer attacks that change the behavior of the targeted control system. By using knowledge of the physical system we are able to focus on the final objective of the attack, and not on the particular mechanisms of how vulnerabilities are exploited, and how the attack is hidden. We analyze the security and safety of our mechanisms by exploring the effects of stealthy attacks, and by ensuring that automatic attack-response mechanisms will not drive the system to an unsafe state. A secondary goal of this paper is to initiate the discussion between control and security practitioners--two areas that have had little interaction in the past. We believe that control engineers can leverage security engineering to design--based on a combination of their best practices--control algorithms that go beyond safety and fault tolerance, and include considerations to survive targeted attacks. Recent studies show that network coding can provide significant benefits to network protocols, such as increased throughput, reduced network congestion, higher reliability, and lower power consumption. The core principle of network coding is that intermediate nodes actively mix input packets to produce output packets. This mixing subjects network coding systems to a severe security threat, known as a , where attacker nodes inject corrupted packets into the network. Corrupted packets propagate in an epidemic manner, depleting network resources and significantly decreasing throughput. Pollution attacks are particularly dangerous in wireless networks, where attackers can easily inject packets or compromise devices due to the increased network vulnerability. In this paper, we address pollution attacks against network coding systems in wireless mesh networks. We demonstrate that previous solutions to the problem are impractical in wireless networks, incurring an unacceptably high degradation of throughput. We propose a lightweight scheme, DART, that uses time-based authentication in combination with random linear transformations to defend against pollution attacks. We further improve system performance and propose EDART, which enhances DART with an optimistic forwarding scheme. A detailed security analysis shows that the probability of a polluted packet passing our verification procedure is very low. Performance results using the well-known MORE protocol and realistic link quality measurements from the Roofnet experimental testbed show that our schemes improve system performance over 20 times compared to previous solutions. Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive---new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used. Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95 at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems. Abstract Fuzzing is an effective and widely used technique for finding security bugs and vulnerabilities in software. It inputs irregular test data into a target program to try to trigger a vulnerable condition in the program execution. Since the first random fuzzing system was constructed, fuzzing efficiency has been greatly improved by combination with several useful techniques, including dynamic symbolic execution, coverage guide, grammar representation, scheduling algorithms, dynamic taint analysis, static analysis and machine learning. In this paper, we will systematically review these techniques and their corresponding representative fuzzing systems. By introducing the principles, advantages and disadvantages of these techniques, we hope to provide researchers with a systematic and deeper understanding of fuzzing techniques and provide some references for this field. Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image classification domain, i.e., they leave some opportunities for an attacker to fool the networks into misclassification by introducing imperceptible changes to the original pixels in an image. The vulnerability raises some concerns in applying deep neural networks in security-critical areas, such as intrusion detection. In this paper, we investigate the performances of the state-of-the-art attack algorithms against deep learning-based intrusion detection on the NSL-KDD data set. The vulnerabilities of neural networks employed by the intrusion detection systems are experimentally validated. The roles of individual features in generating adversarial examples are explored. Based on our findings, the feasibility and applicability of the attack methodologies are discussed. Incorporating conventional, unsupervised features into a neural architecture has the potential to improve modeling effectiveness, but this aspect is often overlooked in the research of deep learning models for information retrieval. We investigate this incorporation in the context of answer sentence selection, and show that combining a set of query matching, readability, and query focus features into a simple convolutional neural network can lead to markedly increased effectiveness. Our results on two standard question-answering datasets show the effectiveness of the combined model. Recent studies show that widely used Deep neural networks (DNNs) are vulnerable to the carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L_p distance for penalizing perturbations. Different defense methods have also been explored to defend against such adversarial attacks. While the effectiveness of L_p distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large L_p distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted. This paper explores the relationships between the hard security concepts of identity and privacy on the one hand, and the soft security concepts of trust and reputation on the other hand. We specifically focus on two vulnerabilities that current trust and reputation systems have: the change of identity and multiple identities problems. As a result, we provide a privacy preserving solution to these vulnerabilities which integrates the explored relationships among identity, privacy, trust and reputation. We also provide a prototype of our solution to these vulnerabilities and an application scenario. The Smart Grid, generally referred to as the next-generation power system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with the integration of advanced computing and communication technologies, the Smart Grid is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Along with the silent features of the Smart Grid, cyber security emerges to be a critical issue because millions of electronic devices are inter-connected via communication networks throughout critical power facilities, which has an immediate impact on reliability of such a widespread infrastructure. In this paper, we present a comprehensive survey of cyber security issues for the Smart Grid. Specifically, we focus on reviewing and discussing security requirements, network vulnerabilities, attack countermeasures, secure communication protocols and architectures in the Smart Grid. We aim to provide a deep understanding of security vulnerabilities and solutions in the Smart Grid and shed light on future research directions for Smart Grid security. Modern smartphone operating systems support the development of third-party applications with open system APIs. In addition to an open API, the Android operating system also provides a rich inter-application message passing system. This encourages inter-application collaboration and reduces developer burden by facilitating component reuse. Unfortunately, message passing is also an application attack surface. The content of messages can be sniffed, modified, stolen, or replaced, which can compromise user privacy. Also, a malicious application can inject forged or otherwise malicious messages, which can lead to breaches of user data and violate application security policies. We examine Android application interaction and identify security risks in application components. We provide a tool, ComDroid, that detects application communication vulnerabilities. ComDroid can be used by developers to analyze their own applications before release, by application reviewers to analyze applications in the Android Market, and by end users. We analyzed 20 applications with the help of ComDroid and found 34 exploitable vulnerabilities; 12 of the 20 applications have at least one vulnerability. Intrusion detection systems define an important and dynamic research area for cybersecurity. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system. One of the more specific research areas related to intrusion detection is anomaly detection. Anomaly-based intrusion detection in networks refers to the problem of finding untypical events in the observed network traffic that do not conform to the expected normal patterns. It is assumed that everything that is untypical anomalous could be dangerous and related to some security events. To detect anomalies many security systems implements a classification or clustering algorithms. However, recent research proved that machine learning models might misclassify adversarial events, e.g. observations which were created by applying intentionally non-random perturbations to the dataset. Such weakness could increase of false negative rate which implies undetected attacks. This fact can lead to one of the most dangerous vulnerabilities of intrusion detection systems. The goal of the research performed was verification of the anomaly detection systems ability to resist this type of attack. This paper presents the preliminary results of tests taken to investigate existence of attack vector, which can use adversarial examples to conceal a real attack from being detected by intrusion detection systems. Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the @math distance for penalizing perturbations. Researchers have explored different defense methods to defend against such adversarial attacks. While the effectiveness of @math distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large @math distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted. In this work, we develop a general solution to a broad class of grasping and manipulation problems that we term as C-SLAM for contact simultaneous localization and modeling, where the robots need to accurately track the motions of the contacted bodies and the locations of contacts, while simultaneously estimating important system parameters, such as body dimensions, masses and friction coefficients between contacting surfaces. Our solution framework is based on a dynamic Bayesian inference framework, and hence, we refer to it as Dynamic Bayesian C-SLAM (DBC-SLAM). DBC-SLAM combines an NCP-based dynamic model with the dynamic Bayesian network, and incorporates model parameter estimation as an intrinsic part of the overall inference procedure. We show two preliminary “proof-of-concept” examples that demonstrate the use of DBC-SLAM in robotic contact tasks. With the growing realization that current Internet protocols are reaching the limits of their senescence, several ongoing research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denialof-Service (DoS) attacks that plague today’s Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) – a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN’s resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking. With the growing realization that current Internet protocols are reaching the limits of their senescence, several on-going research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denial-of-Service (DoS) attacks that plague today's Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) - a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN's resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking. This chapter deals with the applications of keystroke dynamics to authenticate verify access to computer systems and networks. It presents our novel contribution to this area along with other related works. The use of computer systems and networks has spread at a rate completely unexpected a decade ago. Computer systems and network are being used in almost every aspect of our daily life. As a result, the security threats to computers and networks have also increased significantly. We give a background information including the goals of any security system for computers and networks, followed by types of security attacks on computers and networks. We present the applications of keystroke dynamics using interkey times and hold times as features to authenticate access to computer systems and networks.