Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era

Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud

Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions.Comment: Copy of the paper accepted at 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS

Deep learning : enhancing the security of software-defined networks

Software-defined networking (SDN) is a communication paradigm that promotes network flexibility and programmability by separating the control plane from the data plane. SDN consolidates the logic of network devices into a single entity known as the controller. SDN raises significant security challenges related to its architecture and associated characteristics such as programmability and centralisation. Notably, security flaws pose a risk to controller integrity, confidentiality and availability. The SDN model introduces separation of the forwarding and control planes. It detaches the control logic from switching and routing devices, forming a central plane or network controller that facilitates communications between applications and devices. The architecture enhances network resilience, simplifies management procedures and supports network policy enforcement. However, it is vulnerable to new attack vectors that can target the controller. Current security solutions rely on traditional measures such as firewalls or intrusion detection systems (IDS). An IDS can use two different approaches: signature-based or anomaly-based detection. The signature-based approach is incapable of detecting zero-day attacks, while anomaly-based detection has high false-positive and false-negative alarm rates. Inaccuracies related to false-positive attacks may have significant consequences, specifically from threats that target the controller. Thus, improving the accuracy of the IDS will enhance controller security and, subsequently, SDN security. A centralised network entity that controls the entire network is a primary target for intruders. The controller is located at a central point between the applications and the data plane and has two interfaces for plane communications, known as northbound and southbound, respectively. Communications between the controller, the application and data planes are prone to various types of attacks, such as eavesdropping and tampering. The controller software is vulnerable to attacks such as buffer and stack overflow, which enable remote code execution that can result in attackers taking control of the entire network. Additionally, traditional network attacks are more destructive. This thesis introduces a threat detection approach aimed at improving the accuracy and efficiency of the IDS, which is essential for controller security. To evaluate the effectiveness of the proposed framework, an empirical study of SDN controller security was conducted to identify, formalise and quantify security concerns related to SDN architecture. The study explored the threats related to SDN architecture, specifically threats originating from the existence of the control plane. The framework comprises two stages, involving the use of deep learning (DL) algorithms and clustering algorithms, respectively. DL algorithms were used to reduce the dimensionality of inputs, which were forwarded to clustering algorithms in the second stage. Features were compressed to a single value, simplifying and improving the performance of the clustering algorithm. Rather than using the output of the neural network, the framework presented a unique technique for dimensionality reduction that used a single value—reconstruction error—for the entire input record. The use of a DL algorithm in the pre-training stage contributed to solving the problem of dimensionality related to k-means clustering. Using unsupervised algorithms facilitated the discovery of new attacks. Further, this study compares generative energy-based models (restricted Boltzmann machines) with non-probabilistic models (autoencoders). The study implements TensorFlow in four scenarios. Simulation results were statistically analysed using a confusion matrix, which was evaluated and compared with similar related works. The proposed framework, which was adapted from existing similar approaches, resulted in promising outcomes and may provide a robust prospect for deployment in modern threat detection systems in SDN. The framework was implemented using TensorFlow and was benchmarked to the KDD99 dataset. Simulation results showed that the use of the DL algorithm to reduce dimensionality significantly improved detection accuracy and reduced false-positive and false-negative alarm rates. Extensive simulation studies on benchmark tasks demonstrated that the proposed framework consistently outperforms all competing approaches. This improvement is a further step towards the development of a reliable IDS to enhance the security of SDN controllers

Fog computing security: a review of current applications and security solutions

Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems