304 research outputs found

    Embedded response technology and service cloud platform for vehicle information tracking

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    Based on the Indonesia national police crime database, it is reported that vehicle theft cases have increased during the Covid-19 pandemic. The database reported an increasing trend of vehicle theft, 4,065 cases from January 2019 to January 2020 in the province and regency region. Therefore, to help police officers work and minimize the criminal cases of vehicle theft, an effective strategy is needed to reduce these threats. This study proposes implementing SMS and QRcode technology embedded in the vehicle for validation information. Cloud computing capabilities can offer real-time network access to technology resources that can be physically located anywhere geographically based on business needs. This technology can rapidly search and show detailed information regarding the specific vehicle, including the vehicle owner, the vehicle registration number, and the validation of the driver's license. To implement and examine the effectiveness of the proposed technology, this study was conducted an experimental study in a real-world setting from January 2021 until April 2021 in Makassar city, Indonesia. This study concluded that the proposed technology could successfully be implemented and effectively show detailed information regarding the specific vehicle based on the experimental results. This study concluded the potential use of the proposed technology in the real world as an alternative solution to minimize the criminal cases of vehicle theft. It can be used as an alternative solution to reduce the increase in criminal cases of inter-island private vehicle theft syndicates

    Commercial Anti-Smishing Tools and Their Comparative Effectiveness Against Modern Threats

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    Smishing, also known as SMS phishing, is a type of fraudulent communication in which an attacker disguises SMS communications to deceive a target into providing their sensitive data. Smishing attacks use a variety of tactics; however, they have a similar goal of stealing money or personally identifying information (PII) from a victim. In response to these attacks, a wide variety of anti-smishing tools have been developed to block or filter these communications. Despite this, the number of phishing attacks continue to rise. In this paper, we developed a test bed for measuring the effectiveness of popular anti-smishing tools against fresh smishing attacks. To collect fresh smishing data, we introduce Smishtank.com, a collaborative online resource for reporting and collecting smishing data sets. The SMS messages were validated by a security expert and an in-depth qualitative analysis was performed on the collected messages to provide further insights. To compare tool effectiveness, we experimented with 20 smishing and benign messages across 3 key segments of the SMS messaging delivery ecosystem. Our results revealed significant room for improvement in all 3 areas against our smishing set. Most anti-phishing apps and bulk messaging services didn't filter smishing messages beyond the carrier blocking. The 2 apps that blocked the most smish also blocked 85-100\% of benign messages. Finally, while carriers did not block any benign messages, they were only able to reach a 25-35\% blocking rate for smishing messages. Our work provides insights into the performance of anti-smishing tools and the roles they play in the message blocking process. This paper would enable the research community and industry to be better informed on the current state of anti-smishing technology on the SMS platform

    A Review on mobile SMS Spam filtering techniques

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    Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement

    SMS Security by Elliptic Curve and Chaotic Encryption Algorithms

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    Short message services (SMS) represent one of the components of the global communications network and are one of the important developments in communication technologies and communications technology. SMS messages without a password are stored in the SMS server. For the purpose of review and dispute resolution. The security of SMS content cannot be protected because it is transmitted in plain text and is accessible to network operators and employees. Therefore, the end-to-end key is based on encryption and decryption technology can provide SMS security. The security protocols used for SMS security on contemporary mobile devices were examined in this study. SMS security system encryption time affects how well mobile devices work. This shows that security technologies take longer to generate keys and encrypt keys as the key size increases. Due to the limited processing power of mobile devices, large-scale algorithms such as DES, AES, RC4, and Blowfish are not suitable for SMS encryption. SMS may be encrypted using the elliptic curve technique because it provides great security with a smaller key on devices with limited resources, such as mobile phones. And chaotic theory, encryption is simple, fast and secure data encryption. As a result, a combination of elliptic curve algorithm and chaotic encryption algorithm is proposed to achieve a high level of security. In this paper, several tests have been done to compare the algorithms in terms of throughput, power consumption, SMS size, encoding time, and decoding time. The results indicate that the proposed method is better than the comparison method.

    Canary in Twitter Mine: Collecting Phishing Reports from Experts and Non-experts

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    The rise in phishing attacks via e-mail and short message service (SMS) has not slowed down at all. The first thing we need to do to combat the ever-increasing number of phishing attacks is to collect and characterize more phishing cases that reach end users. Without understanding these characteristics, anti-phishing countermeasures cannot evolve. In this study, we propose an approach using Twitter as a new observation point to immediately collect and characterize phishing cases via e-mail and SMS that evade countermeasures and reach users. Specifically, we propose CrowdCanary, a system capable of structurally and accurately extracting phishing information (e.g., URLs and domains) from tweets about phishing by users who have actually discovered or encountered it. In our three months of live operation, CrowdCanary identified 35,432 phishing URLs out of 38,935 phishing reports. We confirmed that 31,960 (90.2%) of these phishing URLs were later detected by the anti-virus engine, demonstrating that CrowdCanary is superior to existing systems in both accuracy and volume of threat extraction. We also analyzed users who shared phishing threats by utilizing the extracted phishing URLs and categorized them into two distinct groups - namely, experts and non-experts. As a result, we found that CrowdCanary could collect information that is specifically included in non-expert reports, such as information shared only by the company brand name in the tweet, information about phishing attacks that we find only in the image of the tweet, and information about the landing page before the redirect

    Enhancing shopping experiences in smart retailing

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    The retailing market has undergone a paradigm-shift in the last decades, departing from its traditional form of shopping in brick-and-mortar stores towards online shopping and the establishment of shopping malls. As a result, “small” independent retailers operating in urban environments have suffered a substantial reduction of their turnover. This situation could be presumably reversed if retailers were to establish business “alliances” targeting economies of scale and engage themselves in providing innovative digital services. The SMARTBUY ecosystem realizes the concept of a “distributed shopping mall”, which allows retailers to join forces and unite in a large commercial coalition that generates added value for both retailers and customers. Along this line, the SMARTBUY ecosystem offers several novel features: (i) inventory management of centralized products and services, (ii) geo-located marketing of products and services, (iii) location-based search for products offered by neighboring retailers, and (iv) personalized recommendations for purchasing products derived by an innovative recommendation system. SMARTBUY materializes a blended retailing paradigm which combines the benefits of online shopping with the attractiveness of traditional shopping in brick-and-mortar stores. This article provides an overview of the main architectural components and functional aspects of the SMARTBUY ecosystem. Then, it reports the main findings derived from a 12 months-long pilot execution of SMARTBUY across four European cities and discusses the key technology acceptance factors when deploying alike business alliances
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