Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.

The Internet economy is based on free access to content in exchange of viewing advertisements that might lead to online purchases. Advertisements represent an important source of revenue to Advertising companies. Those companies employ every possible technique and trick to maximize clicks and visits to advertisers' websites. Modern websites exchange advertisement contents from ads' providers (such as Google AdSense), which means they do not control the contents of those advertisements. Although large providers such as Google and Yahoo! are supposed to be trustworthy, ad arbitration allows them to auction of those ad slots to other providers. Therefore, web administrators cannot guarantee the source of the ads on their delegated website areas. Those advertisements contain Javascript and may redirect to malicious websites which might lead to malicious code being executed or malware being installed. This paper proposes and implements a system for automatically detecting malicious advertisements. It employs three different online malware domain detections systems (VirusTotal, URLVoid, and TrendMicro) for malicious advertisements detection purposes and reports the number of detected malicious advertisements using each system. In addition, we study the efficiency of each system by calculating the confusion matrix and accuracy. We find that URLVoid is the best in terms of accuracy (73%) because it uses a combination of well known website scanners and domain blacklists.

The rapid increase in wired Internet speed and the constant growth in the number of attacks make network protection a challenge. Intrusion detection systems (IDSs) play a crucial role in discovering suspicious activities and also in preventing their harmful impact. Existing signature-based IDSs have significant overheads in terms of execution time and memory usage mainly due to the pattern matching operation. Therefore, there is a need to design an efficient system to reduce overhead. This research intends to accelerate the pattern matching operation through parallelizing a matching algorithm on a multi-core CPU. In this paper, we parallelize a bit-vector algorithm, Myers algorithm, on a multi-core CPU under the MapReduce framework. On average, we achieve four times speedup using our multi-core implementations when compared to the serial version. Additionally, we use two implementations of MapReduce to parallelize the Myers algorithm using Phoenix++ and MAPCG. Our MapReduce parallel implementations of the Myers algorithm are compared with an earlier message passing interface (MPI)-based parallel implementation of the algorithm. The results show 1.3 and 1.7 times improvement for Phoenix++ and MAPCG MapReduce implementations over MPI respectively.