Cloud computing has been the core infrastructure for providing services to the offloaded workloads from IoT devices. However, for time-sensitive tasks, reducing end-to-end delay is a major concern. With advancements in the IoT industry, the computation requirements of incoming tasks at the cloud are escalating, resulting in compromised quality of service. Fog computing emerged to alleviate such issues. However, the resources at the fog layer are limited and require efficient usage. The Whale Optimization Algorithm is a promising meta-heuristic algorithm extensively used to solve various optimization problems. However, being an exploitation-driven technique, its exploration potential is limited, resulting in reduced solution diversity, local optima, and poor convergence. To address these issues, this study proposes a dynamic opposition learning approach to enhance the Whale Optimization Algorithm to offload independent tasks. Opposition-Based Learning (OBL) has been extensively used to improve the exploration capability of the Whale Optimization Algorithm. However, it is computationally expensive and requires efficient utilization of appropriate OBL strategies to fully realize its advantages. Therefore, our proposed algorithm employs three OBL strategies at different stages to minimize end-to-end delay and improve load balancing during task offloading. First, basic OBL and quasi-OBL are employed during population initialization. Then, the proposed dynamic partial-opposition method enhances search space exploration using an information-based triggering mechanism that tracks the status of each agent. The results illustrate significant performance improvements by the proposed algorithm compared to SACO, PSOGA, IPSO, and oppoCWOA using the NASA Ames iPSC and HPC2N workload datasets.

Ethereum Smart Contract (SC) is a sophisticated technology that enhances the scope of BlockChain automation. However, SCs’ vulnerable programs have marred BlockChain’s nascent technology’s brighter aspects. Two critical hacks, the DAO attack, caused by reentrancy vulnerability, and the Parity attack, caused by unprotected selfdestruct and frozen Ether vulnerabilities, are famous for their historical cryptocurrency frauds. DAO attack robbed a sixty million dollar amount of cryptocurrency from the victim’s account. But the Parity attack created a new trend in software vulnerabilities by freezing a thirty million dollar amount of Ether. In fact, the Parity attack wiped out the library SC. Subsequently, all the SCs, depending upon the library functions (e.g., the Parity SC’s Ether transfer), became paralyzed, which locked the investors’ funds. This paper enhances our dynamic analysis tool, TechyTech, to detect the Locked Ether (i.e., Frozen Ether) vulnerability. Furthermore, TechyTech adopts a transfer-based approach and uses case studies to compare TechyTech’s performance with Remix.

Research in Ethereum BlockChain has resulted in the growth of several tools for vulnerability detection. As a typical example, the Vandal tool detects selfdestruct vulnerability.Even though Vandal is a static analysis tool, its approach is also employed by several dynamic analysis tools. There is a need for a different approach for dynamic analysis tools to detect vulnerabilities such as selfdestruct so that dynamic analysis tools can maintain their individuality. This paper uses dynamic analysis to detect the selfdestruct (or self-destructive) vulnerability. Our work balances the developmental pace of the static and dynamic analysis approaches. The novelty of the work presented in this paper is that we use an Ether transfer-based approach and name it as "terminating transfer" to detect the selfdestruct vulnerability using the tool developed by us called "TechyTech".

Various decentralized applications have deployed millions of smart contracts (SCs) on the Blockchain networks. SCs enable programmable transactions involving the transfer of monetary assets between peers on a Blockchain network without any need to a central authority. However, similar to any software program, SCs may contain security issues. Software se-curity engineers and researchers have already uncovered several Ethereum BlockChain and SC vulnerabilities. Still, researchers continuously discover many more security flaws in deployed SCs. Indeed, the popularity of SCs attracts adversaries to launch new attack vectors. Thus, efficient vulnerability detection is necessary. This paper lists broad known vulnerabilities in SCs and classifies them based on the multi-class categories such as Suicidal, Prodigal, Greedy, and Normal SCs. The paper adopts artificial recurrent neural network architecture such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) used in deep learning to identify and then classify vulnerable Scs.

The Internet of Things (IoT) has already changed our daily lives by integrating smart devices together towards delivering high quality services to its clients. These devices when integrated together form a network through which massive amount of data can be produced, transferred, and shared. A critical concern is the security and integrity of such a complex platform to ensure the sustainability and reliability of these IoT-based systems. Blockchain is an emerging technology that has demonstrated its unique features and capabilities for different problems and application domains including IoT-based systems. This survey paper reviews the adaptation of Blockchain in the context of IoT to represent how this technology is capable of addressing the integration and security problems of devices connected to IoT systems. The innovation of this survey is that we present a survey based upon the integration approaches and security issues of IoT data and discuss the role of Blockchain in connection with these issues.