In the modern transportation system, In-vehicle communication systems are managed by controllers know as controller area networks (CAN). The CAN facilitates the interaction of 20 to 100 Electronic Control Units (ECU) which coordinate, monitor and control loads of internal vehicle components such as engine system, brake system and telematics system through the exchange of information among them. CAN operates by broadcasting packets to its bus. This means that all nodes and ECUs attached to the bus can receive the packets, without an authentication mechanism for identifying the legitimacy/source of packets. This makes it vulnerable to attacks. An Intrusion Detection System (IDS) can be used to detect attacks on CAN. Machine learning for the IDS, in particular, would be useful for creating models to detect non-linear attack patterns. However, car manufacturers and owners are might not be willing to just share the sensitive information required for training the models. In this paper, we propose a Blockchain-based Federated Forest Software-Defined Networking (SDN)-enabled Intrusion detection system (BFF-IDS) for an In-vehicle network to address the problem of sharing the sensitive CAN data. Due to the limited scalability of blockchain, InterPlanetary File System (IPFS) was used to host the models, while a hash of the model and a pointer to its location was stored and shared via the blockchain. The SDN provides the dynamic routing of packets and model exchanges from IPFS through the blockchain. In the detection model system, a Federated Learning (FL) method creates a radom forest model in a distributed manner by aggregating partially trained models that were trained by individuals with their data kept confidential during the process. Using Fourier transform, we decomposed the CAN IDs cycle from CAN bus traffic in the frequency domain for better generalization in multiclass detection of attacks. Multiple statistical and entropy features were extracted to handle the high complexity and non-linearity in CAN bus traffic. With this proposed system, manufacturers and car owners may be willing to contribute to the training of the models, as their sensitive data is protected due to the use of FL. By storing hashes of the models on a blockchain, the risk of adversaries poisoning the models is reduced and a single point of failure is avoided. The evaluation was conducted by performing experiments in a testbed. We found that the proposed system has efficient use of memory and CPU resources, and that the detection rate of closely related attacks was high.

Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of the model in terms of trainable parameters while still maintaining high accuracy. One way to achieve this is to select the minimum possible number of features. In this paper, we propose a long short-term memory (LSTM) network for the classification of epileptic EEG signals. Discrete wavelet transform (DWT) is employed to remove noise and extract 20 eigenvalue features. The optimal features were then identified using correlation and P value analysis. The proposed method significantly reduces the number of trainable LSTM parameters required to attain high accuracy. Finally, our model outperforms other proposed frameworks, including popular classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT).

No abstract

The amount of feed dispense to match fish appetite plays a significant role in increasing fish cultivation. However, measuring the quantity of fish feed intake remains a critical challenge. To addressed this problem, this paper proposed an intelligent fish feeding regime system using fish behavioral vibration analysis and artificial neural networks. The model was developed using acceleration and angular velocity data obtained through a data logger that incorporated a triaxial accelerometer, magnetometer, and gyroscope for predicting fish behavioral activities. To improve the system accuracy, we developed a novel 8-directional Chain Code generator algorithm that extracts the vectors representing escape, swimming, and feeding activities. The set of sequence vectors extracted was further processed using Discrete Fourier Transform, and then the Fourier Descriptors of the individual activity representations were computed. These Fourier Descriptors are fed into an artificial neural network, the results of which are evaluated and compared with the Fourier Descriptors obtained directly from the acceleration and angular velocity data. The results show that the developed model using Fourier Descriptors obtained from Chain Code has an accuracy of 100%. In comparison, the developed classifier using Fourier Descriptors obtained directly from the fish movements acceleration, and angular velocity has an accuracy of 35.60%. These results showed that the proposed system could be used in dispensing feeds successfully without human intervention based on the fish requirements. INDEX TERMS Accelerometer, artificial neural network, aquaculture, chain code, fish, fish activities, fish feeding system, Fourier descriptor, IoT devices.

No abstract

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchainbased Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models are still vulnerable to evasion, poisoning and exploratory attacks by adversarial examples. The BFF-IDS offers partial defence against poisoning but has no measure for evasion attacks, the most common attack/threat faced by ML models. Besides, the impact of adversarial examples transferability in CAN IDS has largely remained untested. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We also investigated the statistical adversarial detector's effectiveness and resilience in detecting the attacks and subsequent countermeasures by augmenting the model with detected samples. Our investigation results established that BFF-IDS is very vulnerable to adversarial examples attacks. The statistical adversarial detector and the subsequent BFF-IDS augmentation (BFF-IDS(AUG)) provide an effective mechanism against the adversarial examples. Consequently, integrating the statistical adversarial detector and the subsequent BFF-IDS augmentation with the detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks. INDEX TERMSAdversarial examples, artificial intelligent (AI), blockchain, controller area network (CAN), federated learning, intrusion detection system (IDS).

No abstract

Nowadays, demand for low power, small, mobile and flexible computing machines that interconnects are growing rapidly. This study highlights internet of things (IoT) model regarding sensor node discovery and IPV6 framework using 6LoWPAN. Contiki network simulator (cooja) was used to examine the performance of the proposed network. The simulator was chosen because it provides good graphical user interface environment and allow rapid simulation setup found to be best in simulating network involving 6LoWPAN. Three experiments were carry out with the network topology designed to have 3, 7 and 5 motes respectively. The parameters considered in the simulation were throughput and packet loss which were examined using packet generation rate of 1 to 50 packet/sec with a constant delay. GET requests was sent to the humidity and temperature sensor motes running CoAP servers, and the corresponding throughput were observed in each case per experiment, it was observed that there was a 10 packet per second increase before it finally dropped This was because of the packet loss due to the increase in traffic. GET request was sent to motes to obtain the packet loss and the packet that were not acknowledged determined the packet loss. In this study, the performance of the proposed model in terms of throughput and packet loss was studied and the expected results will aid in planning 6LoWPAN network, A transition flow diagram was evolved for this work to represent packet routing process.