Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Efficient network management and security in 5G enabled internet of things using deep learning algorithms

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model

AI-based intrusion detection systems for in-vehicle networks: a survey.

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions

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