The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.

Software-Defined Networking (SDN) is an emerging concept that intends to replace traditional networks by breaking vertical integration. It does so by separating the control logic of network from the underlying switches and routers, suggesting logical centralization of network control, and allowing to program the network. Although SDN promises more flexible network management, there are numerous security threats accompanied with its deployment. This paper aims at studying SDN accompanied with OpenFlow protocol from the perspective of intrusion and Distributed Denial of Service (DDoS) attacks and suggest machine learning based techniques for mitigation of such attacks.

Frequent “crashes” of the stock market reported during the year 1994 suggest that the Karachi bourse is rapidly converting into a volatile market. This cannot be viewed as a positive sign for this developing market of South Asia. Though heavy fluctuations in stock prices are not an unusual phenomena and it has been observed at almost all big and small exchanges of the world. Focusing on the reasons for such fluctuations is instructive and likely to have important policy implications. Proponents of the efficient market hypothesis argue that changes in stock prices are mainly dependent on the arrival of information regarding the expected returns from the stock. However, Fama (1965), French (1980), and French and Rolls (1986) observed that volatility is to some extent caused by trading itself. Portfolio insurance schemes also have the potential to increase volatility. Brady Commission’s Report provides useful insights into the effect of portfolio insurance schemes. It is interesting to note that many analysts consider the so-called “crashes” of Karachi stock market as a deliberate move to bring down prices. An attempt is made in this study to examine the effect of trading on the volatility of stock prices at Karachi Stock Exchange (KSE). Findings of the study will help understand the mechanism of the rise and fall of stock prices at the Karachi bourse.