Biblio

Dynamic firewalls with stateful inspection have added a lot of security features over the stateless traditional static filters. Dynamic firewalls need to be adaptive. In this paper, we have designed a framework for dynamic firewalls based on probabilistic ontology using Multi Entity Bayesian Networks (MEBN) logic. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated substructures and can express a probability distribution over models of any consistent first order theory. The motivation of our proposed work is about preventing novel attacks (i.e. those attacks for which no signatures have been generated yet). The proposed framework is in two important parts: first part is the data flow architecture which extracts important connection based features with the prime goal of an explicit rule inclusion into the rule base of the firewall; second part is the knowledge flow architecture which uses semantic threat graph as well as reasoning under uncertainty to fulfill the required objective of providing futuristic threat prevention technique in dynamic firewalls.

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.

To protect complex power-grid control networks, power operators need efficient security assessment techniques that take into account both cyber side and the power side of the cyber-physical critical infrastructures. In this paper, we present CPINDEX, a security-oriented stochastic risk management technique that calculates cyber-physical security indices to measure the security level of the underlying cyber-physical setting. CPINDEX installs appropriate cyber-side instrumentation probes on individual host systems to dynamically capture and profile low-level system activities such as interprocess communications among operating system assets. CPINDEX uses the generated logs along with the topological information about the power network configuration to build stochastic Bayesian network models of the whole cyber-physical infrastructure and update them dynamically based on the current state of the underlying power system. Finally, CPINDEX implements belief propagation algorithms on the created stochastic models combined with a novel graph-theoretic power system indexing algorithm to calculate the cyber-physical index, i.e., to measure the security-level of the system's current cyber-physical state. The results of our experiments with actual attacks against a real-world power control network shows that CPINDEX, within few seconds, can efficiently compute the numerical indices during the attack that indicate the progressing malicious attack correctly.
 

Practical intrusion detection in Wireless Multihop Networks (WMNs) is a hard challenge. The distributed nature of the network makes centralized intrusion detection difficult, while resource constraints of the nodes and the characteristics of the wireless medium often render decentralized, node-based approaches impractical. We demonstrate that an active-probing-based network intrusion detection system (AP-NIDS) is practical for WMNs. The key contribution of this paper is to optimize the active probing process: we introduce a general Bayesian model and design a probe selection algorithm that reduces the number of probes while maximizing the insights gathered by the AP-NIDS. We validate our model by means of testbed experimentation. We integrate it to our open source AP-NIDS DogoIDS and run it in an indoor wireless mesh testbed utilizing the IEEE 802.11s protocol. For the example of a selective packet dropping attack, we develop the detection states for our Bayes model, and show its feasibility. We demonstrate that our approach does not need to execute the complete set of probes, yet we obtain good detection rates.

This paper presents the application of fusion meth- ods to a visual surveillance scenario. The range of relevant features for re-identifying vehicles is discussed, along with the methods for fusing probabilistic estimates derived from these estimates. In particular, two statistical parametric fusion methods are considered: Bayesian Networks and the Dempster Shafer approach. The main contribution of this paper is the development of a metric to allow direct comparison of the benefits of the two methods. This is achieved by generalising the Kelly betting strategy to accommodate a variable total stake for each sample, subject to a fixed expected (mean) stake. This metric provides a method to quantify the extra information provided by the Dempster-Shafer method, in comparison to a Bayesian Fusion approach. 

As information and communication networks are highly interconnected with the power grid, cyber security of the supervisory control and data acquisition (SCADA) system has become a critical issue in the power system. By intruding into the SCADA system via the remote access points, the attackers are able to eavesdrop critical data and reconfigure devices to trip the system breakers. The cyber attacks are able to impact the reliability of the power system through the SCADA system. In this paper, six cyber attack scenarios in the SCADA system are considered. A Bayesian attack graph model is used to evaluate the probabilities of successful cyber attacks on the SCADA system, which will result in breaker trips. A forced outage rate (FOR) model is proposed considering the frequencies of successful attacks on the generators and transmission lines. With increased FOR values resulted from the cyber attacks, the loss of load probabilities (LOLP) in reliability test system 79 (RTS79) are estimated. The results of the simulations demonstrate that the power system becomes less reliable as the frequency of successful attacks increases.

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack, exhausts the resources of server/service and makes it unavailable for legitimate users. With increasing use of online services and attacks on these services, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS attacks has also grown. Detection accuracy & CPU utilization of Data mining based IDS is directly proportional to the quality of training dataset used to train it. Various preprocessing methods like normalization, discretization, fuzzification are used by researchers to improve the quality of training dataset. This paper evaluates the effect of various data preprocessing methods on the detection accuracy of DoS/DDoS attack detection IDS and proves that numeric to binary preprocessing method performs better compared to other methods. Experimental results obtained using KDD 99 dataset are provided to support the efficiency of proposed combination.