The massive modern technical revolution in electronics, cognitive computing, and sensing has provided critical infrastructure for the development of today’s Internet of Things (IoT) for a wide range of applications. However, because endpoint devices’ computing, storage, and communication capabilities are limited, IoT infrastructures are exposed to a wide range of cyber-attacks. As such, Darknet or blackholes (sinkholes) attacks are significant, and recent attack vectors that are launched against several IoT communication services. Since Darknet address space evolved as a reserved internet address space that is not contemplated to be used by legitimate hosts globally, any communication traffic is speculated to be unsolicited and distinctively deemed a probe, backscatter, or misconfiguration. Thus, in this paper, we develop, investigate, and evaluate the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks. Mainly, we make use of six supervised machine-learning techniques, including bagging decision tree ensembles (BAG-DT), AdaBoost decision tree ensembles (ADA-DT), RUSBoosted decision tree ensembles (RUS-DT), optimizable decision tree (O-DT), optimizable k-nearest neighbor (O-KNN), and optimizable discriminant (O-DSC). We evaluate the implemented DTDS models on a recent and comprehensive dataset, known as the CIC-Darknet-2020 dataset, composed of contemporary actual IoT communication traffic involving four different classes that combine VPN and Tor traffic in a single dataset covering a wide range of captured cyber-attacks and hidden services provided by the Darknet. Our empirical performance analysis demonstrates that bagging ensemble techniques (BAG-DT) offer better accuracy and lower error rates than other implemented supervised learning techniques, scoring a 99.50% of classification accuracy with a low inferencing overhead of 9.09 µ second. Finally, we also contrast our BAG-DT-DTDS with other existing DTDS models and demonstrate that our best results are improved by (1.9~27%) over the former state-of-the-art models.

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Machine-Learning-Based Darknet Traffic Detection System for IoT Applications

Due to the prompt expansion and development of intelligent systems and autonomous, energy-aware sensing devices, the Internet of Things (IoT) has remarkably grown and obstructed nearly all applications in our daily life. However, constraints in computation, storage, and communication capabilities of IoT devices has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for a lightweight and anomaly-based detection system that can build profiles for normal and malicious activities over IoT networks. In this paper, we propose an ensemble learning model for botnet attack detection in IoT networks called ELBA-IoT that profiles behavior features of IoT networks and uses ensemble learning to identify anomalous network traffic from compromised IoT devices. In addition, our IoT-based botnet detection approach characterizes the evaluation of three different machine learning techniques that belong to decision tree techniques (AdaBoosted, RUSBoosted, and bagged). To evaluate ELBA-IoT, we used the N-BaIoT-2021 dataset, which comprises records of both normal IoT network traffic and botnet attack traffic of infected IoT devices. The experimental results demonstrate that our proposed ELBA-IoT can detect the botnet attacks launched from the compromised IoT devices with high detection accuracy (99.6%) and low inference overhead (40 µ-seconds). We also contrast ELBA-IoT results with other state-of-the-art results and demonstrate that ELBA-IoT is superior.

/pdf/elba-iot-an-ensemble-learning-model-for-botnet-attack-t6gj6kfg.pdf

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Cybersecurity is important today because of the increasing growth of the Internet of Things (IoT), which has resulted in a variety of attacks on computer systems and networks. As the number of various IoT devices and services grows, cyber security will become an increasingly difficult issue to manage. Malicious traffic identification using deep learning techniques has emerged as a key component of network-based intrusion detection systems (IDS). Deep learning methods have been a research focus in network intrusion detection. A recurrent neural network is useful in a wide range of applications. This paper proposes a novel deep learning model for detecting anomalies in IoT networks using recurrent neural networks. The proposed model is implemented in IoT networks utilizing LSTM, BiLSTM, and GRU-based approaches for anomaly detection. A convolutional neural network can analyze input features without losing important information, making them particularly well suited for feature learning. In addition, we propose a hybrid deep learning model based on convolutional and recurrent neural networks. Finally, employing LSTM, BiLSTM, and GRU-based techniques, we propose a lightweight deep learning model for binary classification. The proposed deep learning models are validated using NSLKDD, BoT-IoT, IoT-NI, MQTT, MQTTset, IoT-23, and IoT-DS2 datasets. Our proposed binary and multiclass classification model achieved high accuracy, precision, recall, and F1 score compared to current deep learning implementations.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09777970.pdf

Design and Development of RNN-based Anomaly Detection Model for IoT Networks

Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead.

/pdf/pdf-malware-detection-based-on-optimizable-decision-trees-3th8ng0s.pdf

PDF Malware Detection Based on Optimizable Decision Trees

Smartness and IoT along with machine learning (ML) lead the research directions nowadays. Smart city, smart campus, smart home, smart vehicle, etc; or if we call it “Smart x” will change how the world entities interact among themselves. This paper provides an ML benchmarking as well as a taxonomy that divides its models into linear and non-linear ones based on the problem type (classification or regression), the targeted security issue, the kind of IoT network, and the used evaluation measure. On the other hand, security algorithms enhanced with ML play a significant role to govern the new era of communication. This paper also provides a case study to apply the ML methods to IoT smart campus (SC) as a model to reach a secured IoT system for data collection and manipulation with guided research directions. 

Machine Learning Benchmarking for Secured IoT Smart Systems

The Internet of Things (IoT) is an emerging technology that is considered a key enabler for next‐generation smart cities, industries, security services and economies. IoT networks allow connected devices to communicate with each other automatically without human intervention which empowers innovative solutions for pressing challenges and limitations of current technologies required to materialize smart environments. Due to the concrete involvement of IoT networks in critical infrastructures and cyber‐physical systems, defending them against cyber‐attacks has led to extensive research efforts to propose effective countermeasures against such attacks. In this work, we present Boost‐Defence: a framework to secure IoT networks from a large vector of cyber‐attacks at different IoT layers. We employ the AdaBoost machine learning technique combined with Decision Trees and extensive data engineering techniques to construct a robust classifier for detecting and classifying several cyber‐attacks in IoT networks. We evaluate our system on the TON_IoT_2020 datasets, a collection of datasets compiled specifically for 3‐layered IoT systems comprising: physical, network and application layers. We contrast the performance of our system against existing state‐of‐the‐art solutions. Our experimental analysis demonstrates the capability of our framework in providing superior classification accuracy and lower types 1 and 2 errors for constructing more resilient IoT infrastructures.

Boost‐Defence for resilient IoT networks: A head‐to‐toe approach

High-performance intrusion detection system for networked UAVs via deep learning

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cryptography. While promising, these techniques have received limited use due to their extreme overheads of running on general-purpose machines. In this paper, we present a novel vector Instruction Set Architecture (ISA) and microarchitecture for accelerating the ring-based computations of RLWE. The ISA, named B512, is developed to meet the needs of ring processing workloads while balancing high-performance and general-purpose programming support. Having an ISA rather than fixed hardware facilitates continued software improvement post-fabrication and the ability to support the evolving workloads. We then propose the ring processing unit (RPU), a high-performance, modular implementation of B512. The RPU has native large word modular arithmetic support, capabilities for very wide parallel processing, and a large capacity highbandwidth scratchpad to meet the needs of ring processing. We address the challenges of programming the RPU using a newly developed SPIRAL backend. A configurable simulator is built to characterize design tradeoffs and quantify performance. The best performing design was implemented in RTL and used to validate simulator performance. In addition to our characterization, we show that a RPU using 20.5mm2 of GF12nm can provide a speedup of 1485× over a CPU running a 64k, 128-bit NTT, a core RLWE workload.

RPU: The Ring Processing Unit

Demystifying Bootstrapping in Fully Homomorphic Encryption

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to extract the sensitive information. To address these vulnerabilities Fully Homomorphic Encryption (FHE) keeps the data encrypted during computation and secures the results, even in these untrusted environments. However, FHE requires a significant amount of computation to perform equivalent unencrypted operations. To be useful, FHE must significantly close the computation gap (within 10x) to make encrypted processing practical. To accomplish this ambitious goal the TREBUCHET project is leading research and development in FHE processing hardware to accelerate deep computations on encrypted data, as part of the DARPA MTO Data Privacy for Virtual Environments (DPRIVE) program. We accelerate the major secure standardized FHE schemes (BGV, BFV, CKKS, FHEW, etc.) at>=128-bit security while integrating with the open-source PALISADE and OpenFHE libraries currently used in the DoD and in industry. We utilize a novel tile-based chip design with highly parallel ALUs optimized for vectorized 128b modulo arithmetic. The TREBUCHET coprocessor design provides a highly modular, flexible, and extensible FHE accelerator for easy reconfiguration, deployment, integration and application on other hardware form factors, such as System-on-Chip or alternate chip areas.

Ahmad Al Badawi

Papers

Boost‐Defence for resilient IoT networks: A head‐to‐toe approach

High-performance intrusion detection system for networked UAVs via deep learning

RPU: The Ring Processing Unit

Demystifying Bootstrapping in Fully Homomorphic Encryption

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation