Since its inception, the Internet of Things (IoT) has witnessed mushroom growth as a breakthrough technology. In a nutshell, IoT is the integration of devices and data such that processes are automated and centralized to a certain extent. IoT is revolutionizing the way business is done and is transforming society as a whole. As this technology advances further, the need to exploit detection and weakness awareness increases to prevent unauthorized access to critical resources and business functions, thereby rendering the system unavailable. Denial of Service (DoS) and Distributed DoS attacks are all too common. In this paper, we propose a Protocol Based Deep Intrusion Detection (PB-DID) architecture, in which we created a data-set of packets from IoT traffic by comparing features from the UNSWNB15 and Bot-IoT data-sets based on flow and Transmission Control Protocol (TCP). We classify non-anomalous, DoS, and DDoS traffic uniquely by taking care of the problems like imbalanced and over-fitting. We have achieved a classification accuracy of 96.3% by using deep learning (DL) technique.

Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.

A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person.

Malware applications (Apps) targeting mobile devices are widespread, and compromise the sensitive and private information stored on the devices. This is due to the asymmetry between informative permissions and irrelevant and redundant permissions for benign Apps. It also depends on the characteristics of the Android platform, such as adopting an open-source policy, supporting unofficial App stores, and the great tolerance for App verification; therefore the Android platform is destined to face such malicious intrusions. In this paper, we propose a permissions-based malware detection system (PerDRaML) that determines the App’s maliciousness based on the usage of suspicious permissions. The system uses a multi-level based methodology; we first extract and identify the significant features such as permissions, smali sizes, and permission rates from a manually collected dataset of 10,000 applications. Further, we employ various machine learning models to categorize the Apps into their malicious or benign categories. Through extensive experimentations, the proposed method successfully identifies the 5× most significant features to predict malicious Apps. The proposed method outperformed the existing techniques by achieving high accuracies of malware detection i.e., 89.7% with Support Vector Machine, 89.96% with Random Forest, 86.25% with Rotation Forest, and 89.52% with Naïve Bayes models. Moreover, the proposed method optimized up to ~77% of the feature set as compared to the recent approaches, while improving the evaluation metrics such as precision, sensitivity, accuracy, and F-measure. The experimental results show that the proposed system provides a high level of symmetry between irrelevant permissions and malware Apps. Further, the proposed system is promising and may provide a low-cost alternative for Android malware detection for malicious or repackaged Apps.

Human Activity Recognition is a key component of ambient assisted living and one of the most active areas of research in the Internet of Things. The use of wearable and embedded sensors in human activity recognition overcomes the limitations of conventional approaches relying on machine vision and environmental sensors. We offer a novel, lightweight Convolutional Neural Network – Bidirectional Gated Recurrent Unit (CNN-BiGRU) model that classifies human activities using the inertial sensor data collected with body-mounted smartwatches and smartphones. Unlike the traditional approaches, the presented model is trained on the magnitude of the 3D acceleration (mag _a ), which significantly minimizes the input space 1D. The deep learner has been validated using two different publicly available datasets from the Wireless Sensor Data Mining (WISDM) lab and different evaluation parameters such as Recall/Sensitivity, Precision, accuracy, and F1-scores are computed. A comparison with the existing studies reveals that our proposed learner surpasses the existing methodologies. Using magnitude of 3D acceleration (mag _a ^w , 1D input signal), we have achieved 97.29% accuracy for all 6 activities of WISDM 2011 dataset and 98.81% accuracy for 3D acceleration (a _x ^w ,a _y ^w ,a _z ^w ). The Precision, Recall and F1-score remained at 97% for the 1D case and 99% for the 3D case. When evaluated on the data of all 18 smartwatch-based activities in WISDM 2019 dataset, we have achieved 97.5% accuracy with the magnitude of 3D acceleration (mag _a ^w ) and 98.4% accuracy for 6D acceleration and angular velocities (a _x ^w ,a _y ^w ,a _z ^w ,ω _x ^w ,ω _y ^w ,ω _z ^w ). The Precision, Recall and F1-score remained at 98% in both cases.