Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

A Survey on Performance Evolution Of Various Encryption Algorithms

Security is becoming much more important in data storage and transmission. Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks. This security mechanism uses some algorithms to scramble data into unreadable text which can be only being decoded or decrypted by party those possesses the associated key. These algorithms consume a significant amount of computing resources such as CPU time, memory and computation time. In this paper we are studying the performance evaluation of the various encryption algorithms and also we are analyzing the best encryption algorithm from the widely used algorithms. Keywords: Cryptography, Encryption Algorithms, CPU Time, Computation Time

Operating Systems for Ethical Hackers - A Platform Comparison of Kali Linux and Parrot OS

Many operating systems are used for ethical hacking, which has emerged over the years. These operating systems have multiple tools and features to encounter malicious attacks performed by hackers. This study aims to discuss the benefits of various operating systems used for ethical hacking and to present a platform comparison study of two well-known Debian-derived Linux distributions used for ethical hacking, namely Kali Linux and Parrot OS. These tools and features assist ethical hackers in determining which operating system is best for penetration testing. In this paper, we will explore what penetration testing is, why we use this testing technique and how to secure the computer and the network from cyber-attacks using different ethical hacking operating systems. The paper deals with a qualitative analysis of the tools and features to deeply analyze some of their metrics which have been common in these operating systems. This paper will help ethical hackers to nail down the operating systems that are most suitable for them.

Feedback deer hunting optimization algorithm for intrusion detection in cloud based deep residual network

Cloud computing is the distributed computing paradigm continually exposed to different attacks and threats of various origins. The data stored in the cloud framework is easier for external and internal intruders, as access to the cloud framework is done through internet services. Various intrusion detection (ID) methods are developed to detect network intruders in the cloud, but these methods are not primarily effective in generating accurate detection results. Hence, an effective intrusion detection system (IDS) is designed to solve the security issues that unfavorably influence the sustainable development of the cloud and enhance the protection of the cloud from malicious attacks. The IDS is modeled using the proposed Feedback Deer Hunting Optimization (FDHO)-based Deep Residual network to detect network intrusions. However, the proposed FDHO algorithm is designed by integrating Feedback Artificial Tree (FAT) with Deer Hunting Optimization (DHOA), respectively. Moreover, the detection of malicious attacks is carried out using a Deep Residual network that significantly increases the training speed, reduces the computational complexity, and generates effective detection results. The performance of the proposed method is comparatively analyzed with the existing techniques, such as Stacked Contractive Auto-Encoder and Support Vector Machine (SCAE+SVM), Artificial Neural Network with ant bee colony optimization algorithm+fuzzy clustering (ANN+ABC+fuzzy clustering), Improved dynamic immune algorithm (IDIA), and Normalized K-means (NK) clustering algorithm with RNN named, (NK-RNN), FAT-based Deep Residual network, and DHOA-based Deep Residual network using the BoT-IoT dataset and KDD cup-99 dataset. The proposed method achieved outstanding performance by considering the metrics, like specificity, accuracy, and sensitivity, with the values of 0.9526, 0.9498, and 0.9214 using the BoT-IoT dataset.

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.