325 research outputs found
Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods
Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques.
The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns.
The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other.
The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques.
The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy
IoT Threat Detection Testbed Using Generative Adversarial Networks
The Internet of Things(IoT) paradigm provides persistent sensing and data
collection capabilities and is becoming increasingly prevalent across many
market sectors. However, most IoT devices emphasize usability and function over
security, making them very vulnerable to malicious exploits. This concern is
evidenced by the increased use of compromised IoT devices in large scale bot
networks (botnets) to launch distributed denial of service(DDoS) attacks
against high value targets. Unsecured IoT systems can also provide entry points
to private networks, allowing adversaries relatively easy access to valuable
resources and services. Indeed, these evolving IoT threat vectors (ranging from
brute force attacks to remote code execution exploits) are posing key
challenges. Moreover, many traditional security mechanisms are not amenable for
deployment on smaller resource-constrained IoT platforms. As a result,
researchers have been developing a range of methods for IoT security, with many
strategies using advanced machine learning(ML) techniques. Along these lines,
this paper presents a novel generative adversarial network(GAN) solution to
detect threats from malicious IoT devices both inside and outside a network.
This model is trained using both benign IoT traffic and global darknet data and
further evaluated in a testbed with real IoT devices and malware threats.Comment: 8 pages, 5 figure
Effective Anomaly Detection Using Deep Learning in IoT Systems
Anomaly detection in network traffic is a hot and ongoing research theme especially when concerning IoT devices, which are quickly spreading throughout various situations of people's life and, at the same time, prone to be attacked through different weak points. In this paper, we tackle the emerging anomaly detection problem in IoT, by integrating five different datasets of abnormal IoT traffic and evaluating them with a deep learning approach capable of identifying both normal and malicious IoT traffic as well as different types of anomalies. The large integrated dataset is aimed at providing a realistic and still missing benchmark for IoT normal and abnormal traffic, with data coming from different IoT scenarios. Moreover, the deep learning approach has been enriched through a proper hyperparameter optimization phase, a feature reduction phase by using an autoencoder neural network, and a study of the robustness of the best considered deep neural networks in situations affected by Gaussian noise over some of the considered features. The obtained results demonstrate the effectiveness of the created IoT dataset for anomaly detection using deep learning techniques, also in a noisy scenario
Deep Learning Algorithms Used in Intrusion Detection Systems -- A Review
The increase in network attacks has necessitated the development of robust
and efficient intrusion detection systems (IDS) capable of identifying
malicious activities in real-time. In the last five years, deep learning
algorithms have emerged as powerful tools in this domain, offering enhanced
detection capabilities compared to traditional methods. This review paper
studies recent advancements in the application of deep learning techniques,
including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN),
Deep Belief Networks (DBN), Deep Neural Networks (DNN), Long Short-Term Memory
(LSTM), autoencoders (AE), Multi-Layer Perceptrons (MLP), Self-Normalizing
Networks (SNN) and hybrid models, within network intrusion detection systems.
we delve into the unique architectures, training models, and classification
methodologies tailored for network traffic analysis and anomaly detection.
Furthermore, we analyze the strengths and limitations of each deep learning
approach in terms of detection accuracy, computational efficiency, scalability,
and adaptability to evolving threats. Additionally, this paper highlights
prominent datasets and benchmarking frameworks commonly utilized for evaluating
the performance of deep learning-based IDS. This review will provide
researchers and industry practitioners with valuable insights into the
state-of-the-art deep learning algorithms for enhancing the security framework
of network environments through intrusion detection
Management And Security Of Multi-Cloud Applications
Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy
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
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