In view of the current situation in which a large number of maintenance tasks impose a heavy burden on the hands, this project proposes the development of a kind of maintenance power-assisted gloves that will provide auxiliary grip strength through the exoskeleton structure. In this paper, the design of the hand exoskeleton structure of the gloves is introduced in detail, and the current maintenance field needs are taken as the design goal. The exoskeleton structure was designed and improved from the perspective of bionics, and the movement of the exoskeleton structure was analyzed through theoretical study and analysis as well as simulation. Finally, experiments were performed to verify the power-assisted performance of the exoskeleton structure.
Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. Experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard’s detection rate for mining pages is 96%, but MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot.
As an approach to feature estimation, exponential fitting has attracted research interests in mathematical modeling. Semantic networks are used for numerous applications in computers, physics, and biology. However, such applications may have fitting troubles with various mathematical tools. Therefore, we present a novel method of fitting 2n data points of a signal to a sum of n exponential functions. The experiments proved that the proposed method operated well for linear and nonlinear functions, as its algorithm was straightforward, practical, and easy to determine. At the same time, the computational intricacy was considerably low, which has specific worth in use.
In the recent development of the online cryptocurrency mining platform, Coinhive, numerous websites have employed "Cryptojacking." They may need the unauthorized use of CPU resources to mine cryptocurrency and replace advertising income. Web cryptojacking technologies are the most recent attack in information security. Security teams have suggested blocking Cryptojacking scripts by using a blacklist as a strategy. However, the updating procedure of the static blacklist has not been able to promptly safeguard consumers because of the sharp rise in "Cryptojacking kidnapping". Therefore, we propose a Cryptojacking identification technique based on analyzing the user's computer resources to combat the assault technology known as "Cryptojacking kidnapping." Machine learning techniques are used to monitor changes in computer resources such as CPU changes. The experiment results indicate that this method is more accurate than the blacklist system and, in contrast to the blacklist system, manually updates the blacklist regularly. The misuse of online Cryptojacking programs and the unlawful hijacking of users' machines for Cryptojacking are becoming worse. In the future, information security undoubtedly addresses the issue of how to prevent Cryptojacking and abduction. The result of this study helps to save individuals from unintentionally becoming miners.
In the late 20th century, computer viruses emerged as powerful malware that resides permanently in target hosts. For a virus to function, it must load into memory from persistent storage, such as a file on a hard drive. Due to the significant destructive potential of viruses, numerous defense measures have been developed to protect computer systems. Among these, antivirus software is one of the most recognized and widely used. Typically, antivirus solutions rely on static analysis (signature-based) technologies to detect infections in files stored on permanent storage devices, such as hard drives or USB (Universal Serial Bus) flash drives. However, a new breed of malware, fileless malware, has been designed to evade detection and enhance durability. Fileless malware resides solely in the memory of the target hosts, circumventing traditional antivirus software, which cannot access or analyze processes executed directly from memory. This study proposes the Check-on-Execution (CoE) kernel-based approach to detect fileless malware on Linux systems. CoE intervenes by suspending code execution before a program executes code from a process’s writable and executable memory area. To prevent the execution of fileless malware, CoE extracts the code from memory, packages it with an ELF (Executable and Linkable Format) header to create an ELF file, and uses VirusTotal for analysis. Experimental results demonstrate that CoE significantly enhances a Linux system’s ability to defend against fileless malware. Additionally, CoE effectively protects against shell code injection attacks, including buffer and memory overflows, and can handle packed malware. However, it is important to note that this study focuses exclusively on fileless malware, and further research is needed to address other types of malware.
In recent years, the hijacking vulnerabilities of Android components have been widely discussed, and hijacked Android components have been used to disclose personal information or private data to attackers. Such attacks redirect the Android component's original workflow to malicious code, or even execute malware. We propose an Activity Hijacking Attacks (AHA) scheme that examines the original activity workflow to keep track of every activity in the Android framework. This enables AHA to detect a malicious app that attempts to open an activity in the foreground or whose layout is similar to a login page, i.e., with a text field for account names and passwords. When such activities are detected, our scheme notifies users to be cautious of keying in their credentials for the new activity. Experimental results show that using AHA can prevent attacks designed to steal personal information. Furthermore, our proposed scheme can be easily patched into the existing Android system and has a negligible overhead.
To investigate the effect of music on emotion, the physiological responses to music were analyzed with respect to heart rate variability (HRV) using an electrocardiogram (ECG) sensor.For the experiment, music was categorized by its bits per minute (BPM) and played while monitoring the HRV of the participants.The obtained data were analyzed to obtain parameters in the frequency domain analysis.The indicators of the autonomic nervous system (ANS) changed with the BPM of music and represented the emotional state of the participants.In particular, total power (TP), high-and low-frequency powers, and high-and low-frequency power ratios (HFR and LFR, respectively) changed when listening to fast (120-140 BPM), intermediate (60-80 BPM), and slow (less than 40 BPM) music.Fast music enhanced the autonomic sympathetic nervous system, thus increasing LFR and TP, but HFR decreased owing to the reduced activity of the parasympathetic nerve.Intermediate music inhibited the activity of the autonomic sympathetic nerve, leading to decreased LFR and increased HRF owing to the enhanced activity of the parasympathetic nerve.Slow music did not change the activity of the autonomic sympathetic nerve.These results suggest that music can be used to lessen or prevent stress and enhance work performance.
'/%3e%3cuse xlink:href='%23a' transform='rotate(60)'/%3e%3ccircle r='17' fill='%23000095'/%3e%3ccircle r='15' fill='%23fff'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)