Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi

Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

Reliability Analysis of Electric Power Systems Considering Cyber Security

The new generation of the electric power system is the modern smart grid which is essentially a cyber and physical system (CPS). Supervisory control and data acquisition (SCADA)/energy management system (EMS) is the key component of CPS, which is becoming the main target of both external and insider cyberattacks. Cybersecurity of the SCADA/EMS system is facing big challenges and influences the reliability of the electric power system. Characteristics of cyber threats will impact the system reliability. System reliability can be influenced by various cyber threats with different attack skill levels and attack paths. Additionally, the change of structure of the target system may also result in the change of the system reliability. However, very limited research is related to the reliability analysis of the electric power system considering cybersecurity issue. A large amount of mathematical methods can be used to quantify the cyber threats and simulation processes can be applied to build the reliability analysis model. For instance, to analyze the vulnerabilities of the SCADA/EMS system in the electric power system, Bayesian Networks (BNs) can be used to model the attack paths of cyberattacks on the exploited vulnerabilities. The mean time-to-compromise (MTTC) and mean time-to-failure (MTTF) based on the Common Vulnerability Scoring System (CVSS) can be applied to characterize the properties of cyberattacks. What’s more, simulation approaches like non-sequential or sequential Monte Carlo Simulation (MCS) is able to simulate the system reliability analysis and calculate the reliability indexes. In this thesis, reliability of the SCADA/EMS system in the electric power system considering different cybersecurity issues is analyzed. The Bayesian attack path models of cyberattacks on the SCADA/EMS components are built by Bayesian Networks (BNs), and cyberattacks are quantified by its mean time-to-compromise (MTTC) by applying a modified Semi-Markov Process (SMP) and MTTC models. Based on the IEEE Reliability Test System (RTS) 96, the system reliability is analyzed by calculating the electric power system reliability indexes like LOLP and EENS through MCS. What’s more, cyberattacks with different lurking strategies are considered and analyzed. According to the simulation results, it shows that the system reliability of the SCADA/EMS system in the electric power system considering cyber security is closely related to the MTTC of cyberattacks, which is influenced by the attack paths, attacking skill levels, and the complexity of the target structure. With the increase of the MTTC values of cyberattacks, LOLP values decrease, which means that the reliability of the system is better, and the system is safer. In addition, with the difficulty level of lurking strategies of cyberattacks getting higher and higher, though the LOLP values of scenarios don’t increase a lot, the EENS values of the corresponding scenarios increase dramatically, which indicates that the system reliability is more unpredictable, and the cyber security is worse. Finally, insider attacks are discussed and corresponding LOLP values and EENS values considering lurking behavior are estimated and compared. Both LOLP and EENS values dramatically increase owing to the insider attacks that result in the lower MTTCs. This indicates that insider attacks can lead to worse impact on system reliability than external cyber attacks. The results of this thesis may contribute to the establishment of perfect countermeasures against with cyber attacks on the electric power system

TANDI: Threat Assessment of Network Data and Information

Current practice for combating cyber attacks typically use Intrusion Detection Sensors (IDSs) to passively detect and block multi-stage attacks. This work leverages Level-2 fusion that correlates IDS alerts belonging to the same attacker, and proposes a threat assessment algorithm to predict potential future attacker actions. The algorithm, TANDI, reduces the problem complexity by separating the models of the attacker\u27s capability and opportunity, and fuse the two to determine the attacker\u27s intent. Unlike traditional Bayesian-based approaches, which require assigning a large number of edge probabilities, the proposed Level-3 fusion procedure uses only 4 parameters. TANDI has been implemented and tested with randomly created attack sequences. The results demonstrate that TANDI predicts future attack actions accurately as long as the attack is not part of a coordinated attack and contains no insider threats. In the presence of abnormal attack events, TANDI will alarm the network analyst for further analysis. The attempt to evaluate a threat assessment algorithm via simulation is the first in the literature, and shall open up a new avenue in the area of high level fusion

Game Theory Meets Network Security: A Tutorial at ACM CCS

The increasingly pervasive connectivity of today's information systems brings up new challenges to security. Traditional security has accomplished a long way toward protecting well-defined goals such as confidentiality, integrity, availability, and authenticity. However, with the growing sophistication of the attacks and the complexity of the system, the protection using traditional methods could be cost-prohibitive. A new perspective and a new theoretical foundation are needed to understand security from a strategic and decision-making perspective. Game theory provides a natural framework to capture the adversarial and defensive interactions between an attacker and a defender. It provides a quantitative assessment of security, prediction of security outcomes, and a mechanism design tool that can enable security-by-design and reverse the attacker's advantage. This tutorial provides an overview of diverse methodologies from game theory that includes games of incomplete information, dynamic games, mechanism design theory to offer a modern theoretic underpinning of a science of cybersecurity. The tutorial will also discuss open problems and research challenges that the CCS community can address and contribute with an objective to build a multidisciplinary bridge between cybersecurity, economics, game and decision theory

Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

© 2004-2012 IEEE. The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, software-defined networks (SDNs) allow the decoupling of network control from the data plane. There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations (i.e., two hospitals and two clinics in Hong Kong, two hospitals and two clinics in Singapore, and two hospitals and two clinics in China). Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment. Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i.e., our approach could decrease the trust values of malicious devices faster than similar approaches