We present a detailed study on structure of Organic LEDs (OLEDs) that promise flexibility and enhanced performance. Ordinary LEDs fail when it comes to need of ultra-smart size, thin, flexible smart screens and high efficiency light sources. With electroluminescent layer made of organic compounds, OLEDs promise all such features. We did a comprehensive analysis to find what structural features distinguish OLEDs from semiconductor LEDs. We found that it is the special six layered structure with organic emissive layer and delocalized charges due to weak pi bonds that enable OLEDs to perform better. We dis-cuss a few limitations related to production and life of these LEDs and suggest possible solutions to overcome these challenges. A rigorous, in-depth analysis of this structure is imperative to further comprehend the working of this device in order to make future devices cheaper and more efficient
{"title":"Structural Analysis of Enhanced Performance Organic Light Emitting Diodes (OLEDs)","authors":"","doi":"10.47277/ijcncs/8(9)2","DOIUrl":"https://doi.org/10.47277/ijcncs/8(9)2","url":null,"abstract":"We present a detailed study on structure of Organic LEDs (OLEDs) that promise flexibility and enhanced performance. Ordinary LEDs fail when it comes to need of ultra-smart size, thin, flexible smart screens and high efficiency light sources. With electroluminescent layer made of organic compounds, OLEDs promise all such features. We did a comprehensive analysis to find what structural features distinguish OLEDs from semiconductor LEDs. We found that it is the special six layered structure with organic emissive layer and delocalized charges due to weak pi bonds that enable OLEDs to perform better. We dis-cuss a few limitations related to production and life of these LEDs and suggest possible solutions to overcome these challenges. A rigorous, in-depth analysis of this structure is imperative to further comprehend the working of this device in order to make future devices cheaper and more efficient","PeriodicalId":265348,"journal":{"name":"International Journal of Computer Networks and Communications Security","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116682875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The quality of human life is improving day by day and IOT plays a very important role in this improvement. Everything related to internet have some security concerns. This paper aims to improve the security in IOT environments. In any of the IOT networks the unknown and knows flaws can be a backdoor for any adversary. The increase use of such environment results in the increase of zero day cyber-attacks. This paper aims to focus on different models of DL in order to predict the attacks in IOT environments. The main aim of this research is to provide a very best solution for the detection of threats in order to improve the infrastructures of IOT. In this paper different experiments has been conducted and its results has been discussed in order to provide an effective solution
{"title":"Threat Detection using Machine/Deep Learning in IOT Environments","authors":"","doi":"10.47277/ijcncs/8(8)2","DOIUrl":"https://doi.org/10.47277/ijcncs/8(8)2","url":null,"abstract":"The quality of human life is improving day by day and IOT plays a very important role in this improvement. Everything related to internet have some security concerns. This paper aims to improve the security in IOT environments. In any of the IOT networks the unknown and knows flaws can be a backdoor for any adversary. The increase use of such environment results in the increase of zero day cyber-attacks. This paper aims to focus on different models of DL in order to predict the attacks in IOT environments. The main aim of this research is to provide a very best solution for the detection of threats in order to improve the infrastructures of IOT. In this paper different experiments has been conducted and its results has been discussed in order to provide an effective solution","PeriodicalId":265348,"journal":{"name":"International Journal of Computer Networks and Communications Security","volume":"25 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120914552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Javeed, M. Khan, Ijaz Ahmad, Tahir Iqbal, Umar Mohammed Badamasi, C. Ndubuisi, Aliyu Umar
The capacity and occurrence of new cyber-attacks have shattered in recent years. Such measures have very complicated workflows and comprise multiple illegal actors and organizations. Threat hunting demonstrates the process of proactively searching through networks for threats based on zero-day attacks by repeating the hunting process again and again. Unlike threat intelligence, it uses different automated security tools to collect logs in order to provide a pattern for making new intelligence-based tools by following those logs. According to our research findings about “threat hunting tools” there’s a major flaw that the designed tools are limited to the collection of logs. It works completely on logs for generating new patterns avoiding system’s main memory. Codes written directly to memory fail this process to provide proactive hunting. To overcome this major challenge, we are proposing two distinct methods, either by generating malicious code alerts or by binding memory forensics processes with threat hunting tools to make active hunting possible
{"title":"An Efficient Approach of Threat Hunting Using Memory Forensics","authors":"D. Javeed, M. Khan, Ijaz Ahmad, Tahir Iqbal, Umar Mohammed Badamasi, C. Ndubuisi, Aliyu Umar","doi":"10.47277/ijcncs/8(5)1","DOIUrl":"https://doi.org/10.47277/ijcncs/8(5)1","url":null,"abstract":"The capacity and occurrence of new cyber-attacks have shattered in recent years. Such measures have very complicated workflows and comprise multiple illegal actors and organizations. Threat hunting demonstrates the process of proactively searching through networks for threats based on zero-day attacks by repeating the hunting process again and again. Unlike threat intelligence, it uses different automated security tools to collect logs in order to provide a pattern for making new intelligence-based tools by following those logs. According to our research findings about “threat hunting tools” there’s a major flaw that the designed tools are limited to the collection of logs. It works completely on logs for generating new patterns avoiding system’s main memory. Codes written directly to memory fail this process to provide proactive hunting. To overcome this major challenge, we are proposing two distinct methods, either by generating malicious code alerts or by binding memory forensics processes with threat hunting tools to make active hunting possible","PeriodicalId":265348,"journal":{"name":"International Journal of Computer Networks and Communications Security","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117063712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：