Distributed Port Scanning Detection

Conventional Network Intrusion Detection System (NIDS) have heavyweight processing and memory requirements as they maintain per flow state using data structures like linked lists or trees. This is required for some specialized jobs such as Stateful Packet Inspection (SPI) where the network communications between entities are recreated in its entirety to inspect application level data. The downside to this approach is that the NIDS must be in a position to view all inbound and outbound traffic of the protected network. The NIDS can be overwhelmed by a DDoS attack since most of these try and exhaust the available state of network entities. For some applications like port scan detection, we do not require to reconstruct the complete network tra�c. We propose to integrate a detector into all routers so that a more distributed detection approach can be achieved. Since routers are devices with limited memory and processing capabilities, conventional NIDS approaches do not work while integrating a detector in them. We describe a method to detect port scans using aggregation. A data structure called a Partial Completion Filter(PCF) or a counting Bloom filter is used to reduce the per flow state

MFIRE-2: A Multi Agent System for Flow-based Intrusion Detection Using Stochastic Search

Detecting attacks targeted against military and commercial computer networks is a crucial element in the domain of cyberwarfare. The traditional method of signature-based intrusion detection is a primary mechanism to alert administrators to malicious activity. However, signature-based methods are not capable of detecting new or novel attacks. This research continues the development of a novel simulated, multiagent, flow-based intrusion detection system called MFIRE. Agents in the network are trained to recognize common attacks, and they share data with other agents to improve the overall effectiveness of the system. A Support Vector Machine (SVM) is the primary classifier with which agents determine an attack is occurring. Agents are prompted to move to different locations within the network to find better vantage points, and two methods for achieving this are developed. One uses a centralized reputation-based model, and the other uses a decentralized model optimized with stochastic search. The latter is tested for basic functionality. The reputation model is extensively tested in two configurations and results show that it is significantly superior to a system with non-moving agents. The resulting system, MFIRE-2, demonstrates exciting new network defense capabilities, and should be considered for implementation in future cyberwarfare applications

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

Constructing Cost-Effective and Targetable ICS Honeypots Suited for Production Networks

Honeypots are a technique that can mitigate the risk of cyber threats. Effective honeypots are authentic and targetable, and their design and implementation must accommodate risk tolerance and financial constraints. The proprietary, and often expensive, hardware and software used by Industrial Control System (ICS) devices creates the challenging problem of building a flexible, economical, and scalable honeypot. This research extends Honeyd into Honeyd+, making it possible to use the proxy feature to create multiple high interaction honeypots with a single Programmable Logic Controller (PLC). Honeyd+ is tested with a network of 75 decoy PLCs, and the interactions with the decoys are compared to a physical PLC to test for authenticity. The performance test evaluates the impact of multiple simultaneous connections to the PLC. The functional test is successful in all cases. The performance test demonstrated that the PLC is a limiting factor, and that introducing Honeyd+ has a marginal impact on performance. Notable findings are that the Raspberry Pi is the preferred hosting platform, and more than five simultaneous connections were not optimal

On the Design of an Immersive Environment for Security-Related Studies

The Internet has become an essential part of normal operations of both public and private sectors. Many security issues are not addressed in the original Internet design, and security now has become a large concern for networking research and study. There is an imperative need to have an simulation environment that can be used to help study security-related research problems. In the thesis we present our effort to build such an environment: Real-time Immersive Network Simulation Environment (RINSE). RINSE features flexible configuration of models using various networking protocols and real-time user interaction. We also present the Estimate Next Infection (ENI) model we developed for Internet scanning worms using RINSE, and the effort of combining multiple resolutions in worm modeling