Experimenting with an intrusion detection system for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) analyse network traffic to detect instances of malicious activity. Typically, this is only possible when the network traffic is accessible for analysis. With the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer access this crucial audit data. In this paper, we present an implementation and evaluation of our approach proposed in Goh et al. (2009). It is based on Shamir's secret-sharing scheme and allows a NIDS to function normally in a VPN without any modifications and without compromising the confidentiality afforded by the VPN.

Intrusion detection system for encrypted networks using secret-sharing schemes

Secret-sharing schemes describe methods to securely share a secret among a group of participants. A properly constructed secret-sharing scheme guarantees that the share belonging to one participant does not reveal anything about the shares of others or even the secret itself. Besides the obvious feature which is to distribute a secret, secret-sharing schemes have also been used in secure multi-party computations and redundant residue number systems for error correction codes. In this paper, we propose that the secret-sharing scheme be used as a primitive in a Network-based Intrusion Detection System (NIDS) to detect attacks in encrypted networks. Encrypted networks such as Virtual Private Networks (VPNs) fully encrypt network traffic which can include both malicious and non-malicious traffic. Traditional NIDS cannot monitor encrypted traffic. Our work uses a combination of Shamir's secret-sharing scheme and randomised network proxies to enable a traditional NIDS to function normally in a VPN environment. In this paper, we introduce a novel protocol that utilises a secret-sharing scheme to detect attacks in encrypted networks.

Rule-Based Intrusion Detection System for SCADA Networks

Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified using a ruled based method.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

Intrusion Detection System for IEC 60870-5-104 Based SCADA Networks

Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified via Snort rules.

Intrusion detection framework for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) monitor network traffic for signs of malicious activities that have the potential to disrupt entire network infrastructures and services. NIDS can only operate when the network traffic is available and can be extracted for analysis. However, with the growing use of encrypted networks such as Virtual Private Networks (VPNs) that encrypt and conceal network traffic, a traditional NIDS can no longer access network traffic for analysis. The goal of this research is to address this problem by proposing a detection framework that allows a commercial off-the-shelf NIDS to function normally in a VPN without any modification. One of the features of the proposed framework is that it does not compromise on the confidentiality afforded by the VPN. Our work uses a combination of Shamir’s secret-sharing scheme and randomised network proxies to securely route network traffic to the NIDS for analysis. The detection framework is effective against two general classes of attacks – attacks targeted at the network hosts or attacks targeted at framework itself. We implement the detection framework as a prototype program and evaluate it. Our evaluation shows that the framework does indeed detect these classes of attacks and does not introduce any additional false positives. Despite the increase in network overhead in doing so, the proposed detection framework is able to consistently detect intrusions through encrypted networks.

Extending snBench to Support a Video-Based Intrusion Detection and Alerting System with a Centralized Hash Table

In this project we design and implement a centralized hashing table in the snBench sensor network environment. We discuss the feasibility of this approach and compare and contrast with the distributed hashing architecture, with particular discussion regarding the conditions under which a centralized architecture makes sense. There are numerous computational tasks that require persistence of data in a sensor network environment. To help motivate the need for data storage in snBench we demonstrate a practical application of the technology whereby a video camera can monitor a room to detect the presence of a person and send an alert to the appropriate authorities.

Intrusion Detection System for Network Security in Synchrophasor Systems

Synchrophasor systems will play a crucial role in next generation Smart Grid monitoring, protection and control. However these systems also introduce a multitude of potential vulnerabilities from malicious and inadvertent attacks, which may render erroneous operation or severe damage. This paper proposes a Synchrophasor Specific Intrusion Detection System (SSIDS) for malicious cyber attack and unintended misuse. The SSIDS comprises a heterogeneous whitelist and behavior-based approach to detect known attack types and unknown and so-called ‘zero-day’ vulnerabilities and attacks. The paper describes reconnaissance, Man-in-the-Middle (MITM) and Denial-of-Service (DoS) attack types executed against a practical synchrophasor system which are used to validate the real-time effectiveness of the proposed SSIDS cyber detection method.

Agent Based Intrusion Detection and Response System for Wireless LANs

Wireless LAN technology, despite the numerous advantages it has over competing technologies, has not seen widespread deployment. A primary reason for markets not adopting this technology is its failure to provide adequate security. Data that is sent over wireless links can be compromised with utmost ease. In this project, we propose a distributed agent based intrusion detection and response system for wireless LANs that can detect unauthorized wireless elements like access points, wireless clients that are in promiscuous mode etc. The system reacts to intrusions by either notifying the concerned personnel, in case of rogue access points and promiscuous nodes, or by blocking unauthorized users from accessing the network resources.

Distributed Hybrid Agent Based Intrusion Detection and Real Time Response System

Wireless LANs are growing rapidly and security has always been a concern. We have implemented a hybrid system, which will not only detect active attacks like identity theft causing denial of service attacks, but will also detect the usage of access point discovery tools. The system responds in real time by sending out an alert to the network administrator.

Decentralized detector generation in cooperative intrusion detection system

We consider Cooperative Intrusion Detection System (CIDS) which is a distributed AIS-based (Artificial Immune System) IDS where nodes collaborate over a peer-to-peer overlay network. The AIS uses the negative selection algorithm for the selection of detectors (e.g., vectors of features such as CPU utilization, memory usage and network activity). For better detection performance, selection of all possible detectors for a node is desirable but it may not be feasible due to storage and computational overheads. Limiting the number of detectors on the other hand comes with the danger of missing attacks. We present a scheme for the controlled and decentralized division of detector sets where each IDS is assigned to a region of the feature space. We investigate the trade-off between scalability and robustness of detector sets. We address the problem of self-organization in CIDS so that each node generates a distinct set of the detectors to maximize the coverage of the feature space while pairs of nodes exchange their detector sets to provide a controlled level of redundancy. Our contribution is twofold. First, we use Symmetric Balanced Incomplete Block Design, Generalized Quadrangles and Ramanujan Expander Graph based deterministic techniques from combinatorial design theory and graph theory to decide how many and which detectors are exchanged between which pair of IDS nodes. Second, we use a classical epidemic model (SIR model) to show how properties from deterministic techniques can help us to reduce the attack spread rate.

Multidimensional Intrusion Detection System for IEC 61850 based SCADA Networks

Emerging cybersecurity vulnerabilities in supervisory control and data acquisition (SCADA) systems are becoming urgent engineering issues for modern substations. This paper proposes a novel intrusion detection system (IDS) tailored for cybersecurity of IEC 61850 based substations. The proposed IDS integrates physical knowledge, protocol specifications and logical behaviours to provide a comprehensive and effective solution that is able to mitigate various cyberattacks. The proposed approach comprises access control detection, protocol whitelisting, model-based detection, and multi-parameter based detection. This SCADA-specific IDS is implemented and validated using a comprehensive and realistic cyber-physical test-bed and data from a real 500kV smart substation.