The escalating consumption and disposal of electronic products have spurred a pressing demand for environmental conservation. Traditional disassembly factories encounter challenges when handling discarded products from various locations, including high costs and limited flexibility. This study addresses a multifactory disassembly process optimization problem, taking into account worker posture and the selection of disassembly line types. Subsequently, a mathematical model to maximize profit is built. The reinforcement learning algorithm, Categorical deep Q network (DQN), is utilized to find optimal solutions. Experimental results are compared with those from CPLEX to validate the precision and viability of the proposed model. Furthermore, we compare the proposed solution with various reinforcement learning algorithms, including DQN, proximal policy optimization, and Advantage Actor–Critic. The effectiveness of the proposed model and algorithm is verified by experiments on several cases with different complexity scales.

Federated Learning (FL) has gained prominence in fields where safeguarding data privacy is of utmost importance, presenting a valuable approach for the detection of credit card or financial fraud. The detection of fraud in credit card transactions, as well as other digitized financial activities, plays a pivotal role in upholding the integrity of financial transactions, safeguarding the assets of individuals and businesses, and contributing to a more secure and trustworthy financial environment. The field of machine learning and deep learning is continuously advancing, offering promising solutions for bolstering fraud prevention and minimizing financial losses. Nevertheless, in situations where data owners are hesitant to share their data due to privacy regulations, security concerns, or the sensitive nature of the information involved, a privacy-preserving machine learning technique like FL can significantly enhance fraud detection. FL achieves this by harnessing the collective strength of multiple institutions' data without compromising the privacy of individual users. The re-search presented in this paper leverages the advantages of FL for financial fraud detection. The proposed FL model outperformed a conventional neural network in terms of precision, accuracy, loss and recall when identifying instances of financial fraud.

The International Information System Security Certification Consortium, Inc (ISC)2Cybersecurity Workforce Study, 2021, underscored the fact that there is a glaring 2.72 million global deficit of cybersecurity talent. Despite the availability of various learning resources, starting or advancing a career in cybersecurity demands extensive time and effort to select the appropriate resources. Individuals need to identify their prior knowledge and interest and map them appropriately to future learning requirements. The task of choosing an appropriate cybersecurity job profile and acquiring the necessary knowledge, ability, and skills across the variety of online information, can be a difficult undertaking. In this work, we explored data from an online community, Reddit, and applied Natural Language Processing techniques to identify the commonly referred skills, roles, and certifications in cybersecurity. This information can then be leveraged to recommend a systematic way on how to navigate a career in cybersecurity.

Digital Contact-tracing through mobile applications require gathering of location and other personal information of an individual by the government or private organizations and became an essential solution for moderating the pandemic and slackening lockdown measures. However, the moral and legal boundaries for such privacy-sensitive information reconnaissance procedure and the ambiguity in the security measures of such technologies has gained controversial reputation.In this work, we performed static profiling of 10 different Android Contact-tracing applications, developed by the health departments of 10 different states within the United States and studied possible security threats posed by them. To the best of our knowledge, our work is the first to heuristically analyze the users’ attitude towards these applications to understand the user-perceived contribution of these apps towards their well-being. We collected user feedback for each of the apps and trained a logistic regression classifier on cleaned, pre-processed and vectorized texts to identify positive or negative outlook towards these apps. Using the confusion matrix, our predictive model showed up to 85% accuracy, 94% precision, 93% recall and 83% f1 score. in predicting the sentiments. The sentiment prediction shows, users in some states did find the apps to be helpful where some other states found them wasteful. Whereas, our static analysis shows none of the apps are malicious themselves but all of them request permission that can be abused to gain escalated privileges.

We present an exploratory study of responses from 75 security professionals and ethical hackers in order to understand how they abuse cloud platforms for attack purposes. The participants were recruited at the Black Hat and DEF CON conferences. We presented the participants' with various attack scenarios and asked them to explain the steps they would have carried out for launching the attack in each scenario. Participants' responses were studied to understand attackers' mental models, which would improve our understanding of necessary security controls and recommendations regarding precautionary actions to circumvent the exploitation of clouds for malicious activities. We observed that in 93.78% of the responses, participants are abusing cloud services to establish their attack environment and launch attacks.

Cloud computing offers users scalable platforms and low resource cost. At the same time, the off-site location of the resources of this service model makes it more vulnerable to certain types of adversarial actions. Cloud computing has not only gained major user base, but also, it has the features that attackers can leverage to remain anonymous and stealth. With convenient access to data and technology, cloud has turned into an attack platform among other utilization. This paper reports our study to show that cyber attackers heavily abuse the public cloud platforms to setup their attack environments and launch stealth attacks. The paper first reviews types of attacks launched through cloud environment. It then reports case studies through which the processes of launching cyber attacks using clouds are demonstrated. We simulated various attacks using a virtualized environment, similar to cloud platforms, to identify the possible countermeasures from a defender's perspective, and thus to provide implications for the cloud service providers.

This paper proposes to use Fast Fourier Transformation-based U-Net (a refined "fully convolutional networks") and perform image convolution in neural networks. Leveraging the Fast Fourier Transformation, it reduces the image convolution costs involved in the Convolutional Neural Networks (CNNs) and thus reduces the overall computational costs. The proposed model identifies the object information from the images. We apply the Fast Fourier transform algorithm on an image data set to obtain more accessible information about the image data, before segmenting them through the U-Net architecture. More specifically, we implement the FFT-based convolutional neural network to improve the training time of the network. The proposed approach was applied to publicly available Broad Bioimage Benchmark Collection (BBBC) dataset. Our model demonstrated improvement in training time during convolution from 600 - 700 ms/step to 400-500 ms/step. We evaluated the accuracy of our model using Intersection over Union (IoU) metric showing significant improvements.

Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, orev encredentials and pass words. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A phisher usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.

Billions of devices in the Internet of Things (IoT) are inter-connected over the internet and communicate with each other or end users. IoT devices communicate through messaging bots. These bots are important in IoT systems to automate and better manage the work flows. IoT devices are usually spread across many applications and are able to capture or generate substantial influx of big data. The integration of IoT with cloud computing to handle and manage big data, requires considerable security measures in order to prevent cyber attackers from adversarial use of such large amount of data. An attacker can simply utilize the messaging bots to perform malicious activities on a number of devices and thus bots pose serious cybersecurity hazards for IoT devices. Hence, it is important to detect the presence of malicious bots in the network. In this paper we propose an evidence theory-based approach for malicious bot detection. Evidence Theory, a.k.a. Dempster Shafer Theory (DST) is a probabilistic reasoning tool and has the unique ability to handle uncertainty, i.e. in the absence of evidence. It can be applied efficiently to identify a bot, especially when the bots have dynamic or polymorphic behavior. The key characteristic of DST is that the detection system may not need any prior information about the malicious signatures and profiles. In this work, we propose to analyze the network flow characteristics to extract key evidence for bot traces. We then quantify these pieces of evidence using apriori algorithm and apply DST to detect the presence of the bots.

With the continued improvement and innovation, technology has become an integral part of our daily lives. The rapid adoption of technology and its affordability has given rise to the Internet-of-Things (IoT). IoT is an interconnected network of devices that are able to communicate and share information seamlessly. IoT encompasses a gamut of heterogeneous devices ranging from a small sensor to large industrial machines. One such domain of IoT that has seen a significant growth in the recent few years is that of the wearable devices. While the privacy issues for medical devices has been well-researched and documented in the literature, the threats to privacy arising from the use of consumer wearable devices have received very little attention from the research community. This paper presents a survey of the literature to understand the various privacy challenges, mitigation strategies, and future research directions as a result of the widespread adoption of wearable devices.

Search engines are the major instruments on the Web. The determination of the liability of the results returned by a typical search engine is a daunting challenge mainly due to the presence of Web spams. New types of Web spams are continuously introduced every now and then, which makes it drastically challenging to decide about the accuracy of the results. The problem looks like a reasoning problem in the presence of uncertainty. This paper presents a methodology for predicting Web spam where the spamicity of hosts is formulated as a reasoning problem. The approach is based on evidence theory, a mathematical prediction model based on Dempster-Shafer Theory (DST). The key benefit of our approach for Web spam is DST's ability to deal with the uncertainty. When a new spam is introduced in the system, the system lacks a reasonable prior knowledge. This is where DST provides more liable solution to detect spams without any prior information. The paper presents detailed statistical evaluations of the proposed approach where an accuracy of 99.27% in detecting Web spams is reported.