Biblio
Aiming at the problem of internal attackers of database system, anomaly detection method of user behaviour is used to detect the internal attackers of database system. With using Discrete-time Markov Chains (DTMC), an anomaly detection system of user behavior is proposed, which can detect the internal threats of database system. First, we make an analysis on SQL queries, which are user behavior features. Then, we use DTMC model extract behavior features of a normal user and the detected user and make a comparison between them. If the deviation of features is beyond threshold, the detected user behavior is judged as an anomaly behavior. The experiments are used to test the feasibility of the detction system. The experimental results show that this detction system can detect normal and abnormal user behavior precisely and effectively.
In this paper we present results of a research on automatic extremist text detection. For this purpose an experimental dataset in the Russian language was created. According to the Russian legislation we cannot make it publicly available. We compared various classification methods (multinomial naive Bayes, logistic regression, linear SVM, random forest, and gradient boosting) and evaluated the contribution of differentiating features (lexical, semantic and psycholinguistic) to classification quality. The results of experiments show that psycholinguistic and semantic features are promising for extremist text detection.
Data analytics is being increasingly used in cyber-security problems, and found to be useful in cases where data volumes and heterogeneity make it cumbersome for manual assessment by security experts. In practical cyber-security scenarios involving data-driven analytics, obtaining data with annotations (i.e. ground-truth labels) is a challenging and known limiting factor for many supervised security analytics task. Significant portions of the large datasets typically remain unlabelled, as the task of annotation is extensively manual and requires a huge amount of expert intervention. In this paper, we propose an effective active learning approach that can efficiently address this limitation in a practical cyber-security problem of Phishing categorization, whereby we use a human-machine collaborative approach to design a semi-supervised solution. An initial classifier is learnt on a small amount of the annotated data which in an iterative manner, is then gradually updated by shortlisting only relevant samples from the large pool of unlabelled data that are most likely to influence the classifier performance fast. Prioritized Active Learning shows a significant promise to achieve faster convergence in terms of the classification performance in a batch learning framework, and thus requiring even lesser effort for human annotation. An useful feature weight update technique combined with active learning shows promising classification performance for categorizing Phishing/malicious URLs without requiring a large amount of annotated training samples to be available during training. In experiments with several collections of PhishMonger's Targeted Brand dataset, the proposed method shows significant improvement over the baseline by as much as 12%.
The correct prediction of faulty modules or classes has a number of advantages such as improving the quality of software and assigning capable development resources to fix such faults. There have been different kinds of fault/defect prediction models proposed in literature, but a great majority of them makes use of static code metrics as independent variables for making predictions. Recently, process metrics have gained a considerable attention as alternative metrics to use for making trust-worthy predictions. The objective of this paper is to investigate different combinations of static code and process metrics for evaluating fault prediction performance. We have used publicly available data sets, along with a frequently used classifier, Naive Bayes, to run our experiments. We have, both statistically and visually, analyzed our experimental results. The statistical analysis showed evidence against any significant difference in fault prediction performances for a variety of different combinations of metrics. This reinforced earlier research results that process metrics are as good as predictors of fault proneness as static code metrics. Furthermore, the visual inspection of box plots revealed that the best set of metrics for fault prediction is a mix of both static code and process metrics. We also presented evidence in support of some process metrics being more discriminating than others and thus making them as good predictors to use.
This paper presents a modular, computationally-distributed “multi-robot” cyberphysical system designed to assist children with developmental delays in learning to walk. The system consists of two modules, each assisting a different aspect of gait: a tethered cable pelvic module with up to 6 degrees of freedom (DOF), which can modulate the motion of the pelvis in three dimensions, and a two DOF wearable hip module assisting lower limb motion, specifically hip flexion. Both modules are designed to be lightweight and minimally restrictive to the user, and the modules can operate independently or in cooperation with each other, allowing flexible system configuration to provide highly customized and adaptable assistance. Motion tracking performance of approximately 2 mm root mean square (RMS) error for the pelvic module and less than 0.1 mm RMS error for the hip module was achieved. We demonstrate coordinated operation of the two modules on a mannequin test platform with articulated and instrumented lower limbs.
It is a challenge to select the most appropriate vantage points in a measurement platform with a wide selection. RIPE Atlas [2], for example currently has over 9600 active measurement vantage points, with selections based on AS, country, etc. A user is limited to how many vantage points they can use in a measurement. This is not only due to limitations the measurement platform imposes, but data from a large number of vantage points would produce a large volume to analyse and store. So it makes sense to optimize for a minimal set of vantage points with a maximum chance of observing the phenomenon in which the user is interested. Network operators often need to debug with only limited information about the problem ("Our network is slow for users in France!"). doing a minimal set of measurements that would allow testing through a wide diversity of networks could be a valuable add-on to the tools available to network operators. Given platforms with numerous vantage points, we have the luxury of testing a large set of end-customer outgoing paths. A diversity metric would allow selection of the most dissimilar vantage points, while exploring from as diverse angles as possible, even with a limited probing budget. If one finds an interesting network phenomenon, one could use the similarity metric to advantage by selecting the most similar vantage points to the one exhibiting the phenomenon, to validate the phenomenon from multiple vantage points. We propose a novel means of selecting vantage points, not based on categorical properties such as origin AS, or geographic location, but rather on topological (dis)similarity between vantage points. We describe a similarity metric across RIPE Atlas probes, and show how it performs better for the purpose of topology discovery than the default probe selection mechanism built into RIPE Atlas.
Information shared on Twitter is ever increasing and users-recipients are overwhelmed by the number of tweets they receive, many of which of no interest. Filters that estimate the interest of each incoming post can alleviate this problem, for example by allowing users to sort incoming posts by predicted interest (e.g., "top stories" vs. "most recent" in Facebook). Global and personal filters have been used to detect interesting posts in social networks. Global filters are trained on large collections of posts and reactions to posts (e.g., retweets), aiming to predict how interesting a post is for a broad audience. In contrast, personal filters are trained on posts received by a particular user and the reactions of the particular user. Personal filters can provide recommendations tailored to a particular user's interests, which may not coincide with the interests of the majority of users that global filters are trained to predict. On the other hand, global filters are typically trained on much larger datasets compared to personal filters. Hence, global filters may work better in practice, especially with new users, for which personal filters may have very few training instances ("cold start" problem). Following Uysal and Croft, we devised a hybrid approach that combines the strengths of both global and personal filters. As in global filters, we train a single system on a large, multi-user collection of tweets. Each tweet, however, is represented as a feature vector with a number of user-specific features.
Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. In this paper, we re-define the recommender system as a multistakeholder environment in which different stakeholders are served by delivering recommendations, and we suggest a utility-based approach to evaluating recommendations in such an environment that is capable of distinguishing among the distributions of utility delivered to different stakeholders.
High accurate time synchronization is very important for many applications and industrial environments. In a computer network, synchronization of time for connected devices is provided by the Precision Time Protocol (PTP), which in principal allows for device time synchronization down to microsecond level. However, PTP and network infrastructures are vulnerable to cyber-attacks, which can de-synchronize an entire network, leading to potentially devastating consequences. This paper will focus on the issue of internal attacks on time synchronization networks and discuss how counter-measures based on public key infrastructures, trusted platform modules, network intrusion detection systems and time synchronization supervisors can be adopted to defeat or at least detect such internal attacks.
When clients interact with a cloud-based service, they expect certain levels of quality of service guarantees. These are expressed as security and privacy policies, interaction authorization policies, and service performance policies among others. The main security challenge in a cloud-based service environment, typically modeled using service-oriented architecture (SOA), is that it is difficult to trust all services in a service composition. In addition, the details of the services involved in an end-to-end service invocation chain are usually not exposed to the clients. The complexity of the SOA services and multi-tenancy in the cloud environment leads to a large attack surface. In this paper we propose a novel approach for end-to-end security and privacy in cloud-based service orchestrations, which uses a service activity monitor to audit activities of services in a domain. The service monitor intercepts interactions between a client and services, as well as among services, and provides a pluggable interface for different modules to analyze service interactions and make dynamic decisions based on security policies defined over the service domain. Experiments with a real-world service composition scenario demonstrate that the overhead of monitoring is acceptable for real-time operation of Web services.
Intrusion detection systems do not perform well when it comes to detecting zero-day attacks, therefore improving their performance in that regard is an active research topic. In this study, to detect zero-day attacks with high accuracy, we proposed two deep learning based anomaly detection models using autoencoder and denoising autoencoder respectively. The key factor that directly affects the accuracy of the proposed models is the threshold value which was determined using a stochastic approach rather than the approaches available in the current literature. The proposed models were tested using the KDDTest+ dataset contained in NSL-KDD, and we achieved an accuracy of 88.28% and 88.65% respectively. The obtained results show that, as a singular model, our proposed anomaly detection models outperform any other singular anomaly detection methods and they perform almost the same as the newly suggested hybrid anomaly detection models.
In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.
Used by both information systems designers and security personnel, the Attack Tree method provides a graphical analysis of the ways in which an entity (a computer system or network, an entire organization, etc.) can be attacked and indicates the countermeasures that can be taken to prevent the attackers to reach their objective. In this paper, we built an Attack Tree focused on the goal “compromising the security of a Web platform”, considering the most common vulnerabilities of the WordPress platform identified by CVE (Common Vulnerabilities and Exposures), a global reference system for recording information regarding computer security threats. Finally, based on the likelihood of the attacks, we made a quantitative analysis of the probability that the security of the Web platform can be compromised.
The Behavior-Interaction-Priority (BIP) framework, rooted in rigorous semantics, allows modeling heterogeneous component-based systems. BIP is supported by a textual modeling language, as well as a tool-set including run-time platforms and verification tools. We present a web-based design studio that allows specifying BIP behavior and interaction models in a purely graphical way and generating the equivalent textual specifications. To facilitate scaling and reusability of BIP models, we have extended architecture diagrams, a graphical language for modeling architecture styles, to define parameterized BIP models. We present the various services provided by the design studio, including model repositories, design guidance mechanisms, code generators, and integration with the BIP tool-set.