This thesis will cover the topic of cyber security in vehicles. Current vehicles contain many computers which communicate over a controller area network. This network has many vulnerabilities which can be leveraged by attackers. To combat these attackers, intrusion detection systems have been implemented. The latest research has mostly focused on the use of deep learning techniques for these intrusion detection systems. However, these deep learning techniques are not foolproof and possess their own security vulnerabilities. One such vulnerability comes in the form of adversarial samples. These are attacks that are manipulated to evade detection by these intrusion detection systems. In this thesis, the aim is to show that the known vulnerabilities of deep learning techniques are also present in the current state-of-the-art intrusion detection systems. The presence of these vulnerabilities shows that these deep learning based systems are still to immature to be deployed in actual vehicles. Since if an attacker is able to use these weaknesses to circumvent the intrusion detection system, they can still control many parts of the vehicles such as the windows, the brakes and even the engine. Current research regarding deep learning weaknesses has mainly focused on the image recognition domain. Relatively little research has investigated the influence of these weaknesses for intrusion detection, especially on vehicle networks. To show these weaknesses, firstly two baseline deep learning intrusion detection systems were created. Additionally, two state-of-the-art systems from recent research papers were recreated. Afterwards, adversarial samples were generated using the fast gradient-sign method on one of the baseline systems. These adversarial samples were then used to show the drop in performance of all systems. The thesis shows that the adversarial samples negatively impact the two baseline models and one state-of-the-art model. The state-of-the-art model’s drop in performance goes as high as 60% in the f1-score. Additionally, some of the adversarial samples need as little as 2 bits to be changed in order to evade the intrusion detection systems. / Detta examensarbete kommer att täcka ämnet cybersäkerhet i fordon. Nuvarande fordon innehåller många datorer som kommunicerar över ett så kallat controller area network. Detta nätverk har många sårbarheter som kan utnyttjas av angripare. För att bekämpa dessa angripare har intrångsdetekteringssystem implementerats. Den senaste forskningen har mestadels fokuserat på användningen av djupinlärningstekniker för dessa intrångsdetekteringssystem. Dessa djupinlärningstekniker är dock inte idiotsäkra och har sina egna säkerhetsbrister. En sådan sårbarhet kommer i form av kontradiktoriska prover. Dessa är attacker som manipuleras för att undvika upptäckt av dessa intrångsdetekteringssystem. I det här examensarbetet kommer vi att försöka visa att de kända sårbarheterna hos tekniker för djupinlärning också finns i de nuvarande toppmoderna systemen för intrångsdetektering. Förekomsten av dessa sårbarheter visar att dessa djupinlärningsbaserade system fortfarande är för omogna för att kunna användas i verkliga fordon. Eftersom om en angripare kan använda dessa svagheter för att kringgå intrångsdetekteringssystemet, kan de fortfarande kontrollera många delar av fordonet som rutorna, bromsarna och till och med motorn. Aktuell forskning om svagheter i djupinlärning har främst fokuserat på bildigenkänningsdomänen. Relativt lite forskning har undersökt inverkan av dessa svagheter för intrångsdetektering, särskilt på fordonsnätverk. För att visa dessa svagheter skapades först två baslinjesystem för djupinlärning intrångsdetektering. Dessutom återskapades två toppmoderna system från nya forskningsartiklar. Efteråt genererades motstridiga prover med hjälp av den snabba gradient-teckenmetoden på ett av baslinjesystemen. Dessa kontradiktoriska prover användes sedan för att visa nedgången i prestanda för alla system. Avhandlingen visar att de kontradiktoriska proverna negativt påverkar de två baslinjemodellerna och en toppmodern modell. Den toppmoderna modellens minskning av prestanda går så högt som 60% i f1-poängen. Dessutom behöver några av de kontradiktoriska samplen så lite som 2 bitar att ändras för att undvika intrångsdetekteringssystem.

Approved for public release, distribution is unlimited / One of the most significant challenges with modern intrusion detection systems is the high rate of false alarms that they generate. In order to lower this rate, we propose to reduce the amount of traffic sent a given intrusion detection system via a filtering process termed stream splitting. Each packet arriving at the system is treated as belonging to a connection. Each connection is then assigned to a network stream. A network stream can then be sent to an analysis engine tailored specifically for that type of data. To demonstrate a stream-splitting capability, both an extendable multi-threaded architecture and prototype were developed. This system was tested to ensure the ability to capture traffic and found to be able to do so with minimal loss at network speeds up to 20 Mb/s, comparable to several open-source analysis programs. The stream splitter was also shown to be able to correctly implement a traffic separation scheme. / Ensign, United States Navy

<div> <div> <div> <p>Support Vector Machines (SVMs) have generally been effective in detecting instances of network intrusion. However, from a practical point of view, a standard SVM is not able to handle large-scale data efficiently due to the computation complexity of the algorithm and extensive memory requirements. To cope with the limitation, this study presents an incremental SVM method combined with a k-nearest neighbors (KNN) based candidate support vectors (CSV) selection strategy in order to speed up training and test process. The proposed incremental SVM method constructs or updates the pattern classes by incrementally incorporating new signatures without having to load and access the entire previous dataset in order to cope with evolving DoS and DDoS attacks. Performance of the proposed method is evaluated with experiments and compared with the standard SVM method and the simple incremental SVM method in terms of precision, recall, F1-score, and training and test duration.<br></p> </div> </div> </div>

Intrusion Detection & Countermeasure Systems (IDCS) and architectures commonly used in commercial, as well as research environments, suffer from a number of problems that limit their effectiveness. The most common shortcoming of current IDCSs is their inability to tolerate failures. These failures can occur naturally, such as hardware or software failures, or can be the result of attackers attempting to compromise the IDCS itself. Currently, the WPI System Security Laboratory at Worcester Polytechnic Institute is developing a Secure Architecture and Fault-Resilient Engine (S.A.F.E.), a system capable of tolerating failures. This system makes use of solutions to the Byzantine General's Problem, developed earlier by Lamport and others. Byzantine Agreement Protocols will be used to achieve consensus about which nodes have been compromised or failed, with a series of synchronized, secure rounds of message exchanges. Once a consensus has been reached, the offending nodes can be isolated and countermeasure actions can be initiated by the system. We consider the necessary and sufficient conditions for the application of Byzantine Agreement Protocols to the intrusion detection problem. Further, a first implementation of this algorithm will be embedded in the Distributed Trust Manager (DTM) module of S.A.F.E. The DTM is the key module responsible for assuring trust amongst the members of the system. Finally, we will evaluate the DTM, as a standalone unit, to ensure that it performs correctly.

In most of critical infrastructures, Wireless Sensor Networks (WSNs) are deployed due to their low-cost, flexibility and efficiency as well as their wide usage in several infrastructures. Regardless of these advantages, WSNs introduce various security vulnerabilities such as different types of attacks and intruders due to the open nature of sensor nodes and unreliable wireless links. Therefore, the implementation of an efficient Intrusion Detection System (IDS) that achieves an acceptable security level is a stimulating issue that gained vital importance. In this thesis, we investigate the problem of security provisioning in WSNs based critical monitoring infrastructures. We propose a trust based hierarchical model for malicious nodes detection specially for Black-hole attacks. We also present various Machine Learning (ML)-driven IDSs schemes for wirelessly connected sensors that track critical infrastructures. In this thesis, we present an in-depth analysis of the use of machine learning, deep learning, adaptive machine learning, and reinforcement learning solutions to recognize intrusive behaviours in the monitored network. We evaluate the proposed schemes by using KDD'99 as real attacks data-sets in our simulations. To this end, we present the performance metrics for four different IDSs schemes namely the Clustered Hierarchical Hybrid IDS (CHH-IDS), Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS) and Q-learning based IDS (QL-IDS) to detect malicious behaviours in a sensor network. Through simulations, we analyzed all presented schemes in terms of Accuracy Rates (ARs), Detection Rates (DRs), False Negative Rates (FNRs), Precision-recall ratios, F_1 scores and, the area under curves (ROC curves) which are the key performance parameters for all IDSs. To this end, we show that QL-IDS performs with ~ 100% detection and accuracy rates.

<div> 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. </div><div> 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.</div><div> 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. </div>

The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intrusion detection system is created, particularly with the support of a multi-class supervised classification technique. In agent-based IDS, there is no central control and therefore no central point of failure. Agents can detect and take predefined actions against malicious activities, which can be detected with the help of data mining techniques. The proposed IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDSs with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on a multiagent platform along with a supervised classification technique. Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. To facilitate information exchange between different agents in the proposed hybrid layered multiagent architecture, a low cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that this system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time on the network.

To address the overload conditions caused by the increasing network traffic volume, recent literature in the network intrusion detection and prevention field has proposed the use of clusters of network intrusion detection and prevention systems (NIDPSs). We observe that simple traffic distribution schemes are usually used for NIDPS clusters. These schemes have two major drawbacks: (1) the loss of correlation information caused by the traffic distribution because correlated flows are not sent to the same NIDPS and (2) the unbalanced loads of the NIDPSs. The first drawback severely affects the ability to detect intrusions that require analysis of correlated flows. The second drawback greatly increases the chance of overloading an NIDPS even when loads of the others are low. In this thesis, we address these two drawbacks. In particular, we propose two novel traffic distribution systems: the Correlation-Based Load Balancer and the Correlation-Based Load Manager as two different solutions to the NIDPS traffic distribution problem. On the one hand, the Load Balancer and the Load Manager both consider the current loads of the NIDPSs while distributing traffic to provide fine-grained load balancing and dynamic load distribution, respectively. On the other hand, both systems take into account traffic correlation in their distributions, thereby significantly reducing the loss of correlation information during their distribution of traffic. We have implemented prototypes of both systems and evaluated them using extensive simulations and real traffic traces. Overall, the evaluation results show that both systems have low overhead in terms of the delays introduced to the packets. More importantly, compared to the naive hash-based distribution, the Load Balancer significantly improves the anomaly-based detection accuracy of DDoS attacks and port scans -- the two major attacks that require the analysis of correlated flows -- meanwhile, the Load Manager successfully maintains the anomaly-based detection accuracy of these two major attacks of the NIDPSs.

To address the overload conditions caused by the increasing network traffic volume, recent literature in the network intrusion detection and prevention field has proposed the use of clusters of network intrusion detection and prevention systems (NIDPSs). We observe that simple traffic distribution schemes are usually used for NIDPS clusters. These schemes have two major drawbacks: (1) the loss of correlation information caused by the traffic distribution because correlated flows are not sent to the same NIDPS and (2) the unbalanced loads of the NIDPSs. The first drawback severely affects the ability to detect intrusions that require analysis of correlated flows. The second drawback greatly increases the chance of overloading an NIDPS even when loads of the others are low. In this thesis, we address these two drawbacks. In particular, we propose two novel traffic distribution systems: the Correlation-Based Load Balancer and the Correlation-Based Load Manager as two different solutions to the NIDPS traffic distribution problem. On the one hand, the Load Balancer and the Load Manager both consider the current loads of the NIDPSs while distributing traffic to provide fine-grained load balancing and dynamic load distribution, respectively. On the other hand, both systems take into account traffic correlation in their distributions, thereby significantly reducing the loss of correlation information during their distribution of traffic. We have implemented prototypes of both systems and evaluated them using extensive simulations and real traffic traces. Overall, the evaluation results show that both systems have low overhead in terms of the delays introduced to the packets. More importantly, compared to the naive hash-based distribution, the Load Balancer significantly improves the anomaly-based detection accuracy of DDoS attacks and port scans -- the two major attacks that require the analysis of correlated flows -- meanwhile, the Load Manager successfully maintains the anomaly-based detection accuracy of these two major attacks of the NIDPSs.

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.