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Cyber Threats and Policies for Industrial Control Systems

Modern Industrial Control Systems (ICS) are very important in our life as we use information and communication technology (ICT) to manage, monitor and improve ICS usage. This continually exposes it to new threats due to the vulnerabilities and architectural weaknesses introduced by the extensive use of ICT. Different types of ICSs have common attacks in which these attacks are very sophisticated

Artificial Intelligence

A novel image steganography technique based on quantum substitution boxes

Substitution boxes play an essential role in designing secure cryptosystems. With the evolution of quantum technologies, current data security mechanisms may be broken due to their construction based on mathematical computation. Quantum walks, a universal quantum computational model, play an essential role in designing quantum algorithms. We utilize the benefits of quantum walks to present a novel

Artificial Intelligence

User Privacy in Legacy Mobile Network Protocols

Current security issues in mobile networks have great impact on user privacy. With the focus directed to signaling protocols security, network security and air interface encryption, critical configurations and design flaws that would impact user privacy can be overlooked. The leakage of IMSI on the broadcast channels during network paging is a privacy issue worth considering. In this paper, we

Artificial Intelligence

On the effect of uplink power control on temporal retransmission diversity

Using stochastic geometry, this letter studies the retransmission performance in uplink cellular networks with fractional path-loss inversion power control (FPC). We first show that the signal-to-interference-ratio (SIR) is correlated across time, which imposes temporal diversity loss in the retransmission performance. In particular, FPC with lower path-loss compensation factor decreases inter

Artificial Intelligence

Enriched environmental conditions modify the gut microbiome composition and fecal markers of inflammation in parkinson’s disease

Recent findings suggest an implication of the gut microbiome in Parkinson’s disease (PD) patients. PD onset and progression has also been linked with various environmental factors such as physical activity, exposure to pesticides, head injury, nicotine, and dietary factors. In this study, we used a mouse model, overexpressing the complete human SNCA gene (SNCA-TG mice) modeling familial and

Artificial Intelligence

A Hybrid Machine Learning Approach for the Phenotypic Classification of Metagenomic Colon Cancer Reads Based on Kmer Frequency and Biomarker Profiling

Human Microbiome plays a critical role in health and the environment. Colorectal cancer (CRC) is the most common cause of death in many countries, and hence early diagnosis of CRC may help in increasing the survival rate. Tracking changes in the microbiome structure of human gut opens new gates towards the detection and prediction of the risk of CRC. Recently, machine learning became a powerful

Artificial Intelligence

NONYM!ZER: Mitigation Framework for Browser Fingerprinting

Not only recent compelled cookies regulations have radically restrained their threats but also increased people awareness has played a fundamental part. This has placed huge pressure on enterprises to find alternatives to bridge this gap and satisfy business demands. Since then fingerprinting has gained enormous popularity. In this paper, we introduce 'nonym!zer' as a mitigation framework for

Software and Communications

Security Perspective in RAMI 4.0

Cloud Computing, Internet of Things (IoT) are the main technologies contributing to the adoption of the fourth revolution in manufacturing, Industry 4.0 also known as smart manufacturing or digital manufacturing. Smart manufacturing facilitates and accelerates the process of manufacturing with the connection of all the systems related to the manufacturing process starting with the Enterprise

Artificial Intelligence
Software and Communications

Deep convolutional encoder-decoders with aggregated multi-resolution skip connections for skin lesion segmentation

The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these

Artificial Intelligence

Deep Ensemble Learning for Skin Lesion Classification from Dermoscopic Images

Skin cancer is one of the leading causes of death globally. Early diagnosis of skin lesion significantly increases the prevalence of recovery. Automatic classification of the skin lesion is a challenging task to provide clinicians with the ability to differentiate between different kind of lesion categories and recommend the suitable treatment. Recently, Deep Convolutional Neural Networks have

Artificial Intelligence