Cyber-surveillance and connected devices can be misused to monitor, harass, isolate, and otherwise, harm individuals. In particular, these devices gather high volumes of personal data such as account details with shared passwords, person’s behavior and preferences, movements by GPS, and audio-video recordings which can be maneuvered. It is therefore imperative to define approaches that help mitigate the Internet of things (IoT)-based real-time abuse in a pro-active, reactive, or predictive manner. The key objective of this research is to outline and categorize such approaches. Further, to comprehend predictive analytics as a potential solution to mitigate technology abuse, we propose an anomaly detection methodology (MF_EW_Bagging) to classify normal and abnormal use pattern categories in an Intrusion Detection System (IDS) for IoT system. A hybrid feature selection technique based on an ensemble of multiple filter–based techniques and a wrapper algorithm is firstly used as search method for finding an optimal feature subset. Further, ensemble learning technique, namely bagging, is used for final classification into normal and abnormal use pattern categories. The use of ensemble feature selection removes biasness of individual feature selection method during ensemble and identifies the optimal subset with non-redundant and relevant features. The proposed methodology is evaluated on publicly available real-time IDS dataset. The research persuades the need of designing robust and lightweight IDS for IoT-based smart environments which understand the cyber-security risks in a proactive predictive manner as it the best way to defend networks and systems with the growing IoT complexity.

网络监视和连接的设备可能被滥用以监视，骚扰，隔离和伤害他人。特别是，这些设备收集大量的个人数据，例如带有共享密码的帐户详细信息，人的行为和偏好，GPS的移动以及可以操纵的音频视频记录。因此，必须定义一种方法，以主动，被动或预测的方式帮助缓解基于物联网（IoT）的实时滥用。这项研究的主要目的是概述和分类这些方法。此外，为了将预测分析理解为减轻技术滥用的潜在解决方案，我们提出了一种异常检测方法（MF _EW_Bagging）以在IoT系统的入侵检测系统（IDS）中对正常和异常使用模式类别进行分类。根据一个A混合特征选择技术合奏的多个过滤器的基于技术和一个封装器算法首先用作用于找到最优特征子集的搜索方法。此外，集成学习技术，即装袋，用于最终分类为正常使用模式和异常使用模式类别。集成特征选择的使用消除了集成过程中单个特征选择方法的偏差，并标识了具有非冗余和相关特征的最优子集。在公开可用的实时IDS数据集上评估了所提出的方法。这项研究表明，需要为基于IoT的智能环境设计健壮且轻巧的IDS，该IDS以积极主动的预测方式了解网络安全风险，因为它是防御日益复杂的IoT的网络和系统的最佳方法。