Intrusion Detection Systems for Flying Ad-hoc Networks

Unmanned Aerial Vehicles (UAVs) are becoming more dependent on mission success than ever. Due to their increase in demand, addressing security vulnerabilities to both UAVs and the Flying Ad-hoc Networks (FANET) they form is more important than ever. As the network traffic is communicated through open airwaves, this network of UAVs relies on monitoring applications known as Intrusion Detection Systems (IDS) to detect and mitigate attacks. This paper will survey current IDS systems that include machine learning techniques when combating various vulnerabilities and attacks from bad actors. This paper will be concluded with research challenges and future research directions in finding an effective IDS system that can handle cyber-attacks while meeting performance requirements.Comment: 5 Pages, 1 figure, 1 table, 41 Reference

An energy scaled and expanded vector-based forwarding scheme for industrial underwater acoustic sensor networks with sink mobility

Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay

Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the data increase the complexity of the classification models. The dimensionality reduction and data balancing approaches are considered very important for lowering costs and increasing the accuracy of the model. In PaRSEL, three dimensionality reduction techniques, Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Factor Analysis (FA), are used to reduce the dimensionality and select the most relevant features for the diagnosis of heart disease. Furthermore, eight balancing techniques, Proximity Weighted Random Affine Shadowsampling (ProWRAS), Localized Randomized Affine Shadowsampling (LoRAS), Random Over Sampling (ROS), Adaptive Synthetic (ADASYN), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMOTE), Majority Weighted Minority Oversampling Technique (MWMOTE) and Random Walk Oversampling (RWOS), are used to deal with the imbalanced nature of the dataset. The performance of PaRSEL is compared with the other standalone classifiers using different performance measures like accuracy, F1-score, precision, recall and AUC-ROC score. Our proposed model achieves 97% accuracy, 80% F1-score, precision is greater than 90%, 67% recall, and 98% AUC-ROC score. This shows that PaRSEL outperforms other standalone classifiers in terms of heart disease prediction. Additionally, we deploy SHapley Additive exPlanations (SHAP) on our proposed model. It helps to understand the internal working of the model. It illustrates how much influence a classifier has on the final prediction outcome

Mitigating Anomalous Electricity Consumption in Smart Cities Using an AI-Based Stacked-Generalization Technique

Energy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non-technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the main issue in maintaining grid stability and cause revenue loss to the utility. The automatic metering infrastructure (AMI) system has reduced grid instability but it has opened up new ways for NTLs in the form of different cyber-physical theft attacks (CPTA). Machine learning (ML) techniques can be used to detect and minimize CPTA. However, they have certain limitations and cannot capture the energy consumption patterns (ECPs) of all the users, which decreases the performance of ML techniques in detecting malicious users. In this paper, we propose a novel ML-based stacked generalization method for the cyber-physical theft issue in the smart grid. The original data obtained from the grid is preprocessed to improve model training and processing. This includes NaN-imputation, normalization, outliers\u27 capping, support vector machine-synthetic minority oversampling technique (SVM-SMOTE) balancing, and principal component analysis (PCA) based data reduction techniques. The pre-processed dataset is provided to the ML models light gradient boosting (LGB), extra trees (ET), extreme gradient boosting (XGBoost), and random forest (RF), to accurately capture all consumers\u27 overall ECP. The predictions from these base models are fed to a meta-classifier multi-layer perceptron (MLP). The MLP combines the learning capability of all the base models and gives an improved final prediction. The proposed structure is implemented and verified on the publicly available real-time large dataset of the State Grid Corporation of China (SGCC). The proposed model outperformed the individual base classifiers and the existing research in terms of CPTA detection with false positive rate (FPR), false negative rate (FNR), F1-score, and accuracy values of 0.72%, 2.05%, 97.6%, and 97.69%, respectively

Interest Broadcast Suppression Scheme for Named Data Wireless Sensor Networks

Named data networking (NDN) is one of the future networking architectures that communicates content using names, instead of the node addresses. It uses a very simple pull-based communication mechanism to retrieve content by sending an Interest message, and the node that has the required content or producer node replies with the data message. In wireless networks, the interest is flooded in the network to find the data provider node. The directional diffusion method is used to pull further content from the provider node. Due to broadcast nature and without node addresses, interest flooding causes network congestion and wastes network resources, especially bandwidth and battery power. These resources have prime importance in the case of wireless sensor networks (WSNs) because all WSN nodes operate on battery and have limited bandwidth. In this paper, we propose an interest broadcast suppression scheme that considers interest holding time using the distance between forwarder and receiver of the interest, energy, angle, and distance from the beeline between consumer and the spatial region, to avoid broadcasting of unnecessary copies of Interest. The simulation results show that the proposed scheme mitigates the interest broadcast and conserves battery power of the wireless nodes compared with the state-of-the-art scheme in the domain. © 2019 IEEE.1

CODIE: Controlled Data and Interest Evaluation in Vehicular Named Data Networks

[EN] Recently, named data networking (NDN) has been proposed as a promising architecture for future Internet technologies. NDN is an extension to the content-centric network (CCN) and is expected to support various applications in vehicular communications [ vehicular NDN (VNDN)]. VNDN basically relies on naming the content rather than using end-to-end device names. In VNDN, a vehicle broadcasts an "Interest" packet for the required "content," regardless of end-to-end connectivity with servers or other vehicles and known as a "consumer." In response, a vehicle with the content replies to the Interest packet with a "Data" packet and named as a "provider." However, the simple VNDN architecture faces several challenges such as consumer/provider mobility and Interest/Data packet(s) forwarding. In VNDN, for the most part, the Data packet is sent along the reverse path of the related Interest packet. However, there is no extensive simulated reference available in the literature to support this argument. In this paper, therefore, we first analyze the propagation behavior of Interest and Data packets in the vehicular ad hoc network (VANET) environment through extensive simulations. Second, we propose the "CODIE" scheme to control the Data flooding/broadcast storm in the naive VNDN. The main idea is to allow the consumer vehicle to start hop counter in Interest packet. Upon receiving this Interest by any potential provider, a data dissemination limit (DDL) value stores the number of hops and a data packet needs to travel back. Simulation results show that CODIE forwards fewer copies of data packets processed (CDPP) while achieving similar interest satisfaction rate (ISR), as compared with the naive VNDN. In addition, we also found that CODIE also minimizes the overall interest satisfaction delay (ISD), respectively.This work was supported by the Ministry of Science, ICT and Future Planning, South Korea, under Grant IITP-2015-H8601-15-1002 of the Convergence Information Technology Research Center supervised by the Institute for Information and Communications Technology Promotion. The review of this paper was coordinated by Editors of CVS. (Corresponding author: Dongkyun Kim.)Ahmed, SH.; Bouk, SH.; Yaqub, MA.; Kim, D.; Song, H.; Lloret, J. (2016). CODIE: Controlled Data and Interest Evaluation in Vehicular Named Data Networks. IEEE Transactions on Vehicular Technology. 65(6):3954-3963. https://doi.org/10.1109/TVT.2016.2558650S3954396365

Research on Efficient Data Forwarding in Vehicular Networks

[No abstract available]1

Improved Resource Allocation in 5G MTC Networks

Effective resource allocation has always been one of the serious challenges in wireless communication. A considerable number of machine type communication (MTC) devices in 5G with variable quality of service (QoS) aggravates this challenge even further. Existing Resource allocation schemes in MTC are usually considering signal to noise ratio (SNR), which provides preference to MTC devices based on distance rather than their QoS requirements. This paper proposes a resource allocation scheme with dynamic priorities for MTC devices with multiple radio access technologies (RATs). The proposed resource allocation scheme has two main parts namely medium access and resource allocation. The medium access leverages the broadcast nature of wireless signal and MTC devices' wait time to assign priorities using capillary band in a secure and integral way. At resource allocation, SNR, total induced transmission delay, and transmission-Awaiting MTC devices are used to assign resources in the cellular band. The rumination of two-staged dynamic priorities in the proposed scheduling scheme brings significant performance improvements in outage and success probabilities. Compared to SNR-based schemes, the proposed mechanism performs well by expressively improving the outage and success probability by 20% and 30%, respectively.1

A novel superframe structure and optimal time slot allocation algorithm for IEEE 802.15.4–based Internet of things

IEEE 802.15.4 standard is specifically designed for a low-rate and low-processing Internet of things (IoT) applications and offers guaranteed time slots. A beacon-enabled IEEE 802.15.4 consists of a superframe structure that comprises of the contention access period and contention-free period. During contention-free period, nodes transfer their data using guaranteed time slots without any collision. The coordinator node receives data transmission requests in one cycle and allocates guaranteed time slots to the nodes in the next cycle. This allocation process may cause large delay that may not be acceptable for few applications. In this work, a novel superframe structure is proposed that significantly reduces guaranteed time slots allocation delay for the nodes with data requests. The proposed superframe structure comprises of two contention access periods and one contention-free period, where contention-free period precedes both contention access periods with reduced slot size. In addition, the knapsack algorithm is modified for better guaranteed time slots allocation by allowing more guaranteed time slots requesting nodes to send their data as compared to the IEEE 802.15.4 standard. The simulation and analytical results show that the proposed superframe structure reduces the network delay by up to 80%, increases contention-free period utilization up to 50%, and allocates guaranteed time slots up to 16 nodes in a single superframe duration. © The Author(s) 2020.1