ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / Telemetry systems are migrating from links to networks. Security solutions that simply encrypt radio links no longer protect the network of Test Articles or the networks that support them. The use of network telemetry is dramatically expanding and new risks and vulnerabilities are challenging issues for telemetry networks. Most of these vulnerabilities are silent in nature and cannot be detected with simple tools such as traffic monitoring. The Intrusion Detection System (IDS) is a security mechanism suited to telemetry networks that can help detect abnormal behavior in the network. Our previous research in Network Intrusion Detection Systems focused on "Password" attacks and "Syn" attacks. This paper presents a generalized method that can detect both "Password" attack and "Syn" attack. In this paper, a K-means Clustering algorithm is used for vector quantization of network traffic. This reduces the scope of the problem by reducing the entropy of the network data. In addition, a Hidden-Markov Model (HMM) is then employed to help to further characterize and analyze the behavior of the network into states that can be labeled as normal, attack, or anomaly. Our experiments show that IDS can discover and expose telemetry network vulnerabilities using Vector Quantization and the Hidden Markov Model providing a more secure telemetry environment. Our paper shows how these can be generalized into a Network Intrusion system that can be deployed on telemetry networks.

Context. The intrusion detection systems are being widely used for detecting the malicious traffic in many industries and they use a variety of technologies. Each IDs had different architecture and are deployed for detecting malicious activity. Intrusion detection system has a different set of rules which can defined based on requirement. Therefore, choosing intrusion detection system for and the appropriate environment is not an easy task. Objectives. The goal of this research is to evaluate three most used open source intrusion detection systems in terms of performance. And we give details about different types of attacks that can be detected using intrusion detection system. The tools that we select are Snort, Suricata, OSSEC. Methods. The experiment is conducted using TCP, SCAN, ICMP, FTP attack. Each experiment was run in different traffic rates under normal and malicious traffics all rule are active. All these tests are conducted in a virtual environment. Results. We can calculate the performance of IDS by using CPU usage, memory usage, packet loss and a number of alerts generated. These results are calculated for both normal and malicious traffic. Conclusions. We conclude that results vary in different IDS for different traffic rates. Specially snort showed better performance in alerts identification and OSSEC in the performance of IDS. These results indicated that alerts are low when the traffic rates high are which indicates this is due to the packet loss. Overall OSSEC provides better performance. And Snort provides better performance and accuracy for alert detection.

Advances in mobile and pervasive computing, electronics technology, and the exponential growth in Internet of Things (IoT) applications and services has led to Building Automation System (BAS) that enhanced the buildings we live by delivering more energy-saving, intelligent, comfortable, and better utilization. Through the use of integrated protocols, a BAS can interconnects a wide range of building assets so that the control and management of asset operations and their services can be performed in one protocol. Moreover, through the use of distributed computing and IP based communication, a BAS can implement remote monitor and control in adaptive and real-time manner. However, the use of IoT and distributed computing techniques in BAS are leading to challenges to secure and protect information and services due to the significant increase in the attack surface and the inherent vulnerabilities of BAS integrated protocols. Since there is no intrusion detection and prevention available for BAS network, proposing a reliable security mechanism which can monitor the behavior of BAS assets becomes a major design issue. Anomaly Based Intrusion Detection is a security mechanism that uses baseline model to describe the normal behaviors of a system, so that malicious behaviors occurred in a system can be detected by comparing the observed behavior to the baseline model. With its ability of detecting novel and new attacks, Anomaly based Behavior Analysis (ABA) has been actively pursued by researchers for designing Intrusion Detection Systems. Since the information acquired from a BAS system can be from a variety of sources (e.g. sensors, network protocols, temporal and spatial information), the traditional ABA methodology which merely focuses on analyzing the behavior of communication protocols will not be effective in protecting BAS networks. In this dissertation we aim at developing a general methodology named Context Aware Anomaly based Behavior Analysis (CAABA) which combines Context Awareness technique with Anomaly based Behavior Analysis in order to detect any type of anomaly behaviors occurred in Building Automation Systems. Context Awareness is a technique which is widely used in pervasive computing and it aims at gathering information about a system's environment so it can accurately characterize the current operational context of the BAS network and its services. The CAABA methodology can be used to protect a variety of BAS networks in a sustainable and reliable way. To handle the heterogeneous BAS information, we developed a novel Context Aware Data Structure to represent the information acquired from the sensors and resources during execution of the BAS system which can explicitly describe the system's behavior. By performing Anomaly based Behavior Analysis over the set of context arrays using either data mining algorithm or statistical functions, the BAS baseline models are generated. To validate our methodology, we have applied it to two different building application scenarios: a smart building system which is usually implemented in industrial and commercial office buildings and a smart home system which is implemented in residential buildings, where we have achieved good detection results with low detection errors.

<div><div><div><p>The presence of attacks in day-to-day traffic flow in connected networks is considerably less compared to genuine traffic flow. Yet, the consequences of these attacks are disastrous. It is very important to identify if the network is being attacked and block these attempts to protect the network system. Failure to block these attacks can lead to loss of confidential information and reputation and can also lead to financial loss. One of the strategies to identify these attacks is to use machine learning algorithms that learn to identify attacks by looking at previous examples. But since the number of attacks is small, it is difficult to train these machine learning algorithms. This study aims to use generative techniques to create new attack samples that can be used to train the machine learning based intrusion detection systems to identify more attacks. Two metrics are used to verify that the training has improved and a binary classifier is used to perform a two-sample test for verifying the generated attacks.</p></div></div></div>

The subject of the master's thesis is a design of security measures for securing of an industrial control system. It includes an analysis of characteristics of communication environment and specifics of industrial communication systems, a comparison of available technological means and a design of a solution according to investor's requirements.

Workshop "Netz- und Service-Infrastrukturen" Dieser Beitrag zum Workshop "Netz- und Service-Infrastrukturen" behandelt den Aufbau eines Network Intrusion Detection System im Campusnetz.

Using low energy devices to communicate over the air presents many challenges to reach security as resources in the world of Internet Of Things (IoT) are limited. Any extra overhead of computing or radio transmissions that extra security might add affects cost of both increased computing time and energy consumption which are all scarce resources in IoT. This thesis details the current state of security mechanisms built into the commercially available protocol stacks Zigbee, Z-wave, and Bluetooth Low Energy, and collects implemented and proposed solutions to common ways of attacking systems built on these protocol stacks. Attacks evaluated are denial of service/sleep, man-in-the-middle, replay, eavesdropping, and in mesh networks, sinkhole, black hole, selective forwarding, sybil, wormhole, and hello flood. An intrusion detection system is proposed to detect sinkhole, selective forwarding, and sybil attacks in the routing protocol present in the communication stack Rime implemented in the operating system Contiki. The Sinkhole and Selective forwarding mitigation works close to perfection in larger lossless networks but suffers an increase in false positives in lossy environments. The Sybil Detection is based on Received Signal Strength and strengthens the blacklist used in the sinkhole and selective forwarding detection, as a node changing its ID to avoid the blacklist will be detected as in the same geographical position as the blacklisted node.

In recent years, a new generation of architecture has emerged in the world of computer networks, known as software-defined networking (SDN), that aims to improve and remove the limitations of traditional networks. Although SDN provides viable benefits, it has faced many security threats and vulnerability-related issues. To solve security issues in the SDN, one of the most vital solutions is employing an intrusion detection system (IDS). Merging IDS into the SDN network remains efficient due to the unique features of SDN, such as high manageability, flexibility, and programmability. In this paper, we propose a new approach as a kangaroo-based intrusion detection system (KIDS), which is an SDN-based architecture for attack detection and malicious behaviors in the data plane. Designing a zone-based architecture in the KIDS assists us in achieving a distributed architecture which is scalable in both area and anomaly detection. In the KIDS architecture, the IDS module supplies the flow-based and packet-based intrusion detection components based on monitoring packet parser and Flow tables of the SDN switches. In the proposed approach, the IDS uses consecutive jumps like a kangaroo for announcing the attacks both to the SDN controller and other IDSs, contributing to improved scalability and efficiency. The evaluation of the proposed approach shows an enhanced performance against that of peer approaches in detecting malicious packets.

Networking and distributed computing system have provided computational resources for machine learning (ML) application for a long time. Network system itself also can benefit from ML technologies. For example high performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing demand in the line rate for core networks, a great challenge is to use hand-tuned heuristic approaches to design a scalable and high performance packet classification solution. By exploiting the sparsity present in a ruleset, in this thesis an algorithm is proposed to use few effective bits (EBs) to extract a large number of candidate rules with just a few number of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be fully matched to improve the online system performance. Utilizing reinforcement learning allows the proposed solution to be learning based rather than heuristic based algorithms. So proposed learning-based selection method is independent of the ruleset, which can be applied to different rulesets without relying on the heuristics. Proposed multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs. Furthermore, many field packet classification are required for openFlow supported switches. With the proliferation of fields in the packet header, a traditional 5-field classification technique isn’t applicable for an efficient classification engine for those openFlow supported switches. Although the algorithmic insights obtained from 5-field classification techniques could still be applied for many field classification engine. To decompose given fields of a ruleset, different grouping metrics like standard deviation of individual fields and a novel metric called Diversity Index (DI) is considered for such many field scenarios. A detailed discussion and evaluation of how to decompose rule fields/dimension into subgroup, how a decision tree construction can be considered as reinforcement learning problem, and how to encode state and action space, reward calculation to effectively build trees for each subgroup with a global optimization objective is introduced in this work. Finally, to identify benign or malicious heterogeneous type of traffic present in a modern home network, a deep neural network based approach is introduced. A split architecture of such traffic classifier, in application of home network intrusion detection system consists of multiple machine learning (ML) models. These models trained on two separate dataset for heterogeneous traffic types. An analysis of run-time implementation performance of the proposed IDS models is also discussed.

Indiana University-Purdue University Indianapolis (IUPUI) / In computer networks, the convenient internet access facilitates internet services, but at the same time also augments the spread of malicious software which could represent an attack or unauthorized access. Thereby, making the intrusion detection an important area to explore for detecting these unwanted activities. This thesis concentrates on combining the Model and Instance Based Machine Learning for detecting intrusions through a series of algorithms starting from clustering the similar hosts. Similar hosts have been found based on the supervised machine learning techniques like Support Vector Machines, Decision Trees and K Nearest Neighbors using our proposed Data Fusion algorithm. Maximal cliques of Graph Theory has been explored to find the clusters. A recursive way is proposed to merge the decision areas of best features. The idea is to implement a combination of model and instance based machine learning and analyze how it performs as compared to a conventional machine learning algorithm like Random Forest for intrusion detection. The system has been evaluated on three datasets by CTU-13. The results show that our proposed method gives better detection rate as compared to traditional methods which might overfit the data. The research work done in model merging, instance based learning, random forests, data mining and ensemble learning with regards to intrusion detection have been studied and taken as reference.