12,271 research outputs found
The Resilience of Deep Learning Intrusion Detection Systems for Automotive Networks
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
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
Rising connectivity in vehicles is enabling new capabilities like connected
autonomous driving and advanced driver assistance systems (ADAS) for improving
the safety and reliability of next-generation vehicles. This increased access
to in-vehicle functions compromises critical capabilities that use legacy
invehicle networks like Controller Area Network (CAN), which has no inherent
security or authentication mechanism. Intrusion detection and mitigation
approaches, particularly using machine learning models, have shown promising
results in detecting multiple attack vectors in CAN through their ability to
generalise to new vectors. However, most deployments require dedicated
computing units like GPUs to perform line-rate detection, consuming much higher
power. In this paper, we present a lightweight multi-attack quantised machine
learning model that is deployed using Xilinx's Deep Learning Processing Unit IP
on a Zynq Ultrascale+ (XCZU3EG) FPGA, which is trained and validated using the
public CAN Intrusion Detection dataset. The quantised model detects denial of
service and fuzzing attacks with an accuracy of above 99 % and a false positive
rate of 0.07%, which are comparable to the state-of-the-art techniques in the
literature. The Intrusion Detection System (IDS) execution consumes just 2.0 W
with software tasks running on the ECU and achieves a 25 % reduction in
per-message processing latency over the state-of-the-art implementations. This
deployment allows the ECU function to coexist with the IDS with minimal changes
to the tasks, making it ideal for real-time IDS in in-vehicle systems.Comment: 5 pages, 2 figures, 6 table
A cognitive based Intrusion detection system
Intrusion detection is one of the primary mechanisms to provide computer
networks with security. With an increase in attacks and growing dependence on
various fields such as medicine, commercial, and engineering to give services
over a network, securing networks have become a significant issue. The purpose
of Intrusion Detection Systems (IDS) is to make models which can recognize
regular communications from abnormal ones and take necessary actions. Among
different methods in this field, Artificial Neural Networks (ANNs) have been
widely used. However, ANN-based IDS, has two main disadvantages: 1- Low
detection precision. 2- Weak detection stability. To overcome these issues,
this paper proposes a new approach based on Deep Neural Network (DNN. The
general mechanism of our model is as follows: first, some of the data in
dataset is properly ranked, afterwards, dataset is normalized with Min-Max
normalizer to fit in the limited domain. Then dimensionality reduction is
applied to decrease the amount of both useless dimensions and computational
cost. After the preprocessing part, Mean-Shift clustering algorithm is the used
to create different subsets and reduce the complexity of dataset. Based on each
subset, two models are trained by Support Vector Machine (SVM) and deep
learning method. Between two models for each subset, the model with a higher
accuracy is chosen. This idea is inspired from philosophy of divide and
conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to
reduce the error from the previous step, an ANN model is trained to gain and
use the results in order to be able to predict the attacks. We can reach to
95.4 percent of accuracy. Possessing a simple structure and less number of
tunable parameters, the proposed model still has a grand generalization with a
high level of accuracy in compared to other methods such as SVM, Bayes network,
and STL.Comment: 18 pages, 6 figure
RSU based Joint Congestion-Intrusion Detection System in Vanets Using Deep Learning Technique
Vehicular Ad hoc Network (VANET) is a technology that makes it possible to provide many practical services in intelligent transportation systems, but it is also susceptible to several intrusion threats. Through the identification of unusual network behavior, intrusion detection systems (ID Ss) can reduce security vulnerabilities. However, rather than detecting anomalous network behaviors throughout the whole VANET, current IDS systems are only able to do so for local sub-networks. Hence there is a need for a Joint Congestion and Intrusion Detection System (JCIDS). We designed an JCICS model that can collect network data cooperatively from vehicles and Roadside Units (RSUs).This paper, proposes a new deep learning model to improve the performance of JCIDS by using k-means and a posterior detection based on coresets to improve the detection accuracy and eliminate the redundant messages. The efficacy of the current Recurrent Neural Network (RNN) and Honey badger Algorithm (HBA)on the fundamental AODV protocol is combined with the advantages of the JCIDS is suggested in this protocol. First, formation of clusters using vehicle’s mobility parameters like, velocity and distance to enhance route stability. Moreover, a vehicle will be chosen as Cluster Head with highest route stability. Second, the efficient intrusion detection is achieved with the consumption using RNN method. In the RNN, the optimal weighting factor is selected with the help of HBA. The RNN is performing efficient prediction with the assistance of HBA. The finest path for data dissemination is selected by choosing link lifetime, hop count and residual energy along the path.As a result, multimedia data streaming is improved network life time, in terms of reduced packet loss ratio and energy consumption as compared to existing DNN and SVM scheme for different node density and speed
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