A comprehensive study of DDoS attacks over IoT network and their countermeasures

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

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HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

In the contemporary digital landscape, the escalating threat landscape of cyber attacks, particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern for network security. This research introduces an innovative approach to DDoS detection leveraging a deep neural network (DNN) architecture rooted in deep learning (DL) principles. The proposed model exhibits a scalable and adaptable framework, enabling meticulous analysis of network traffic data to discern intricate patterns indicative of DDoS attacks. To validate the efficacy of our methodology, rigorous evaluations were conducted using authentic real-world traffic data. The results unequivocally establish the superiority of our DNN-based approach over traditional DDoS detection techniques. This research holds significant promise for bolstering network security, particularly within the dynamic landscape of software-defined network (SDN) environments. The study's findings contribute to the continual refinement and eventual deployment of advanced measures in fortifying digital infrastructure against the evolving threat landscape. Performance metrics, including detection accuracy and loss rates, further emphasize the effectiveness of our approach across different datasets. With detection accuracy rates of 99.98%, 100%, and 99.99% for the InSDN, CICIDS2018, and Kaggle DDoS datasets, respectively, coupled with low loss rates, our DNN-based model demonstrates robust capabilities in mitigating contemporary DDoS threats. This study not only presents a novel DDoS detection approach within SDN infrastructures but also offers insights into practical implications and challenges associated with deploying DNNs in real-world SDN environments. Network security professionals can benefit from the nuanced perspectives provided, contributing to the ongoing discourse on fortifying digital networks against evolving cyber threats. 

DDoS attack detection and mitigation using deep neural network in SDN environment

The Internet connection is becoming ubiquitous in embedded systems, making them potential victims of intrusion. Although gaining popularity in recent years, deep learning based intrusion detection systems tend to produce worse results than those using traditional machine learning algorithms. On the contrary, we propose an end-to-end methodology allowing a neural network to outperform traditional machine learning algorithms. We demonstrate high performance score on CIC-IDS2017 data set, showing an accuracy greater than 99% and a false positive rate lower than 0.5%. Our results are compared to traditional machine learning algorithms and previous studies. Then, we show that our approach can be successfully applied to CSE-CIC-IDS2018 data set, confirming that neural network can reach better scores than other machine learning algorithms. Our performance is compared to previous work on this data set. We further deployed our solution on a system-on-chip for automotive, allowing to characterize real-time performance aspect on an embedded system, both for feature extraction and inference. Finally, a discussion opens up on problems related to some attacks that are particularly difficult to detect with flow-based techniques and weaknesses found in the data sets.

Multi-layer perceptron for network intrusion detection

The explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forward a large amount of traffic to damage one or all target’s resources simultaneously and prevents authenticated users from accessing network services. The paper aims to select the least number of relevant DDoS attack detection features by designing an intelligent wrapper feature selection model that utilizes a binary-particle swarm optimization algorithm with a decision tree classifier. In this paper, the Binary-particle swarm optimization algorithm is used to resolve discrete optimization problems such as feature selection and decision tree classifier as a performance evaluator to evaluate the wrapper model’s accuracy using the selected features from the network traffic flows. The model’s intelligence is indicated by selecting 19 convenient features out of 76 features of the dataset. The experiments were accomplished on a large DDoS dataset. The optimal selected features were evaluated with different machine learning algorithms by performance measurement metrics regarding the accuracy, Recall, Precision, and F1-score to detect DDoS attacks. The proposed model showed a high accuracy rate by decision tree classifier 99.52%, random forest 96.94%, and multi-layer perceptron 90.06 %. Also, the paper compares the outcome of the proposed model with previous feature selection models in terms of performance measurement metrics. This outcome will be useful for improving DDoS attack detection systems based on machine learning algorithms. It is also probably applied to other research topics such as DDoS attack detection in the cloud environment and DDoS attack mitigation systems.

https://ijain.org/index.php/IJAIN/article/download/553/ijain_v7i1_p37-48

Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection

Analog crossbar arrays have recently attracted significant attention due to their usefulness for deep neural net (DNN) computations with ultra-low power consumption. However, recent studies have shown that DNNs implemented with such crossbar arrays suffer from as high as 30% degradation in performance due to the effects of manufacturing process variability effects resulting in degradation of their functional safety. One way to test these DNNs is to apply an exhaustive set of test images to each device to ascertain its performance. This is expensive and time-consuming. We propose an alternative test scheme in which a small subset of test images is applied to each DNN and the classification accuracy of the DNN is predicted directly from observation of the final layer outputs of the network. This saves test cost while allowing binning of DNNs for performance. Experimental results for a variety of test cases are presented and show test efficiency improvements of 10.3X over testing with the exhaustive test image set.

Efficient Low Cost Alternative Testing of Analog Crossbar Arrays for Deep Neural Networks

Spiking Neural Networks (SNNs) can be implemented with power-efficient digital as well as analog circuitry. However, in Resistive RAM (RRAM) based SNN accelerators, synapse weights programmed into the crossbar can differ from their ideal values due to defects and programming errors, degrading inference accuracy. In addition, circuit nonidealities within analog spiking neurons that alter the neuron spiking rate (modeled by variations in neuron firing threshold) can degrade SNN inference accuracy when the value of inference time steps (ITSteps) of SNN is set to a critical minimum that maximizes network throughput. We first develop a recursive linearized check to detect synapse weight errors with high sensitivity. This triggers a correction methodology which sets out-of-range synapse values to zero. For correcting the effects of firing threshold variations, we develop a test methodology that calibrates the extent of such variations. This is then used to proportionally increase inference time steps during inference for chips with higher variation. Experiments on a variety of SNNs prove the viability of the proposed resilience methods.

A Resilience Framework for Synapse Weight Errors and Firing Threshold Perturbations in RRAM Spiking Neural Networks

Modern deep neural networks (DNNs) are deployed across a wide range of applications, from medical robotics to autonomous driving, where safety and reliability are key concerns. The complexity, speed, and low-power operation of the underlying hardware makes them vulnerable to soft errors that corrupt the results of computations and memory accesses. Existing approaches to error resilience are either expensive in terms of overhead, require DNN retraining or applicable to only specific hardware domains. In contrast, we present a novel error resilience approach that does not require DNN retraining and scales across computation as well as weight parameter errors. In the proposed methodology, the statistics of gradients of neuron output values relative to adjacent neurons in an ordering of neurons allow tight theoretically grounded thresholding of neuron outputs to diagnose erroneous neuron outputs. These are then set to zero (suppressed) for error resilience. A low-overhead error diagnosis module is used for this purpose and is designed using gradient statistics collected across the training dataset of the DNN. Our approach is compared against state of the art error resilience techniques and validated on multiple datasets, networks and error scenarios as well a hardware test case.

Error Resilience in Deep Neural Networks Using Neuron Gradient Statistics

Transformer networks have achieved remarkable success in Natural Language Processing (NLP) and Computer Vision applications. However, the underlying large volumes of Transformer computations demand high reliability and resilience to soft errors in processor hardware. The objective of this research is to develop efficient techniques for design of error resilient Transformer architectures. To enable this, we first perform a soft error vulnerability analysis of every fully connected layers in Transformer computations. Based on this study, error detection and suppression modules are selectively introduced into datapaths to restore Transformer performance under anticipated error rate conditions. Memory access errors and neuron output errors are detected using checksums of linear Transformer computations. Correction consists of determining output neurons with out-of-range values and suppressing the same to zero. For a Transformer with nominal BLEU score of 52.7, such vulnerability guided selective error suppression can recover language translation performance from a BLEU score of 0 to 50.774 with as much as 0.001 probability of activation error, incurring negligible memory and computation overheads.

Error Resilient Transformers: A Novel Soft Error Vulnerability Guided Approach to Error Checking and Suppression

The reliability of emerging neuromorphic compute fabrics is of great concern due to their widespread use in critical data-intensive applications. Ensuring such reliability is difficult due to the intensity of underlying computations (billions of parameters), errors induced by low power operation and the complex relationship between errors in computations and their effect on network performance accuracy. We study the problem of designing error-resilient neuromorphic systems where errors can stem from: (a) soft errors in computation of matrix-vector multiplications and neuron activations, (b) malicious trojan and adversarial security attacks and (c) effects of manufacturing process variations on analog crossbar arrays that can affect DNN accuracy. The core principle of error detection relies on embedded predictive neuron checks using invariants derived from the statis-tics of nominal neuron activation patterns of hidden layers of a neural network. Algorithmic encodings of hidden neuron function are also used to derive invariants for checking. A key contribution is designing checks that are robust to the inherent nonlinearity of neuron computations with minimal impact on error detection coverage. Once errors are detected, they are corrected using probabilistic methods due to the difficulties involved in exact error diagnosis in such complex systems. The technique is scalable across soft errors as well as a range of security attacks. The effects of manufacturing process variations are handled through the use of compact tests from which DNN performance can be assessed using learning techniques. Experimental results on a variety of neuromorphic test systems: DNNs, spiking networks and hyperdimensional computing are presented.

Error Resilient Neuromorphic Systems Using Embedded Predictive Neuron Checks

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