Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses

Towards Effective Detection of Botnet Attacks using BoT-IoT Dataset

In the world of cybersecurity, intrusion detection systems (IDS) have leveraged the power of artificial intelligence for the efficient detection of attacks. This is done by applying supervised machine learning (ML) techniques on labeled datasets. A growing body of literature has been devoted to the use of BoT-IoT dataset for IDS based ML frameworks. A few number of related works have recognized the need for a balanced dataset and applied techniques to alleviate the issue of imbalance. However, a significant amount of related research works failed to treat the imbalance in the BoT-IoT dataset. A lack of unanimity was observed in the literature towards the definition of taxonomy for balancing techniques. The study presented here seeks to explore the degree to which the imbalance of the dataset has been treated and to determine the taxonomy of techniques used. In this thesis, a comparison analysis is performed by using a small subset of an entire dataset to determine the threshold sample limit at which the model achieves the highest accuracy. In addition to this analysis, a study was conducted to determine the extent to which each feature of the dataset has an impact on the threshold performance. The study is implemented on the BoT-IoT dataset using three supervised ML classifiers: K-nearest Neighbor, Random Forest, and Logistic Regression. The four principal findings of this thesis are: existing taxonomies are not understood and imbalance of the dataset is not treated; high performance across all metrics is achieved on a highly imbalanced dataset; model is able to achieve the threshold performance using a small subset of samples; certain features had varying impact on the threshold value using different techniques

Implementation of ISO Frameworks to Risk Management in IPv6 Security

The Internet of Things is a technology wave sweeping across various industries and sectors. It promises to improve productivity and efficiency by providing new services and data to users. However, the full potential of this technology is still not realized due to the transition to IPv6 as a backbone. Despite the security assurances that IPv6 provides, privacy and concerns about the Internet of Things remain. This is why it is important that organizations thoroughly understand the protocol and its migration to ensure that they are equipped to take advantage of its many benefits. Due to the lack of available IPv4 addresses, organizations are in an uncertain situation when it comes to implementing IoT technologies. The other aim is to fill in the gaps left by the ISO to identify and classify the risks that are not yet apparent. The thesis seeks to establish and implement the use of ISO to manage risks. It will also help to align security efforts with organizational goals. The proposed solution is evaluated through a survey that is designed to gather feedback from various levels of security and risk management professionals. The suggested modifications are also included in the study. A survey on the implementation of ISO frameworks to risk management in IPv6 was conducted and with results as shown in the random sampling technique that was used for conducting the research a total of 75 questionnaires were shared online, 50 respondents returned responses online through emails and social media platforms. The result of the analysis shows that system admin has the highest pooling 26% of all the overall participants, followed by network admin with 20%, then cybersecurity specialists with 16%. 14% of the respondents were network architects while senior management and risk management professionals were 4% and 2% respectively. The majority of the respondents agreed that risk treatment enhances the risk management performance of the IPv6 network resulting from the proper selection and implementation of correct risk prevention strategies

Attack2vec: Leveraging temporal word embeddings to understand the evolution of cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses.Accepted manuscrip