CONTROLLING IP SPOOFING THROUGH INTER DOMAIN PACKET FILTERS

IP Spoofing is a serious threat to the legitimate use of the Internet. By employing IP spoofing, attackers can overload the destination network thus preventing it from providing service to legitimate user. In this paper, we propose an inter domain packet filter (IDPF) architecture that can minimize the level of IP spoofing on the Internet. A key feature of our scheme is that it does not require global routing information.  IDPFs are constructed from the information implicit in Border Gateway Protocol (BGP) route updates and are deployed in network border routers.  We establish the conditions under which the IDPF framework correctly works in that it does not discard packets with valid source addresses. We show that, even with partial deployment on the Internet, IDPFs can proactively limit the spoofing capability of attackers. In addition, they can help localize the origin of an attack packet to a small number of candidate networks

Securing IoT with Trusted Authority Validation in Homomorphic Encryption Technique with ABE

Existing security system includes levels of encryption. IoT access is very important aspect. Failure of IoT security can cause more risks of physical and logical damage. IoT contain both functionalities including physical or computational process. In proposed approach, levels of encryption are enhanced by increasing levels of security. User can access IoT through central trusted authority only. Instead of actual data like user credentials or I/O functionality of Internet of things, encrypted data is delivered. Trusted authorities are been involved in secured IoT access structure by considering their credentials. Trusted authority is selected randomly, based on randomized selection algorithm. Based on secured logic, decryption key will be delivered to the IoT through separate channel by trusted authority. Session management has been added by considering initial and waiting time after which all encryption or decryption data will be expired. Homomorphism is applied in encryption process where proposed logic is applied on considered data after which again RSA algorithm is applied. Overall, proposed logical approach, homomorphism, session management, secured access structure and trusted authority involvement improves the security level in IoT access process

Under the Spotlight: Web Tracking in Indian Partisan News Websites

India is experiencing intense political partisanship and sectarian divisions. The paper performs, to the best of our knowledge, the first comprehensive analysis on the Indian online news media with respect to tracking and partisanship. We build a dataset of 103 online, mostly mainstream news websites. With the help of two experts, alongside data from the Media Ownership Monitor of the Reporters without Borders, we label these websites according to their partisanship (Left, Right, or Centre). We study and compare user tracking on these sites with different metrics: numbers of cookies, cookie synchronizations, device fingerprinting, and invisible pixel-based tracking. We find that Left and Centre websites serve more cookies than Right-leaning websites. However, through cookie synchronization, more user IDs are synchronized in Left websites than Right or Centre. Canvas fingerprinting is used similarly by Left and Right, and less by Centre. Invisible pixel-based tracking is 50% more intense in Centre-leaning websites than Right, and 25% more than Left. Desktop versions of news websites deliver more cookies than their mobile counterparts. A handful of third-parties are tracking users in most websites in this study. This paper, by demonstrating intense web tracking, has implications for research on overall privacy of users visiting partisan news websites in India

Nano Medicine in Healing Chronic Wounds: Opportunities and Challenges

Chronic wounds pose a continual healthcare challenge, demanding innovative interventions to improve healing outcomes. This comprehensive review navigates the transformative landscape of nanotechnology in chronic wound healing, covering mechanisms, clinical applications, challenges, and future directions. The introduction establishes the need for advanced therapeutic strategies, providing an overview of chronic wounds and the evolving landscape of therapeutic approaches. The exploration of nanoparticle types and their mechanisms in wound healing encompasses lipid-based, polymeric, and inorganic variants, each contributing uniquely to drug solubility, controlled release, and tailored interactions within the wound microenvironment. Clinical applications and formulations exemplify real-world efficacy, demonstrating nanotechnology\u27s success in promoting wound healing. Opportunities in nano-medicine for chronic wounds focus on targeted drug delivery precision and overcoming cellular barriers through enhanced cellular uptake. Acknowledging challenges, including biocompatibility concerns and regulatory hurdles, the review emphasizes the need for rigorous evaluation and streamlined regulatory pathways. Future directions delve into emerging nanotechnologies and potential breakthroughs, highlighting advancements in design, fabrication, and integration with artificial intelligence and personalized medicine

Building Scalable Video Understanding Benchmarks through Sports

Existing benchmarks for evaluating long video understanding falls short on two critical aspects, either lacking in scale or quality of annotations. These limitations arise from the difficulty in collecting dense annotations for long videos, which often require manually labeling each frame. In this work, we introduce an automated Annotation and Video Stream Alignment Pipeline (abbreviated ASAP). We demonstrate the generality of ASAP by aligning unlabeled videos of four different sports with corresponding freely available dense web annotations (i.e. commentary). We then leverage ASAP scalability to create LCric, a large-scale long video understanding benchmark, with over 1000 hours of densely annotated long Cricket videos (with an average sample length of ~50 mins) collected at virtually zero annotation cost. We benchmark and analyze state-of-the-art video understanding models on LCric through a large set of compositional multi-choice and regression queries. We establish a human baseline that indicates significant room for new research to explore. Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed. The dataset along with the code for ASAP and baselines can be accessed here: https://asap-benchmark.github.io/

Thread Detection and Response Generation using Transformers with Prompt Optimisation

Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models.Comment: 6 pages, 4 figures, submitted to 2024 IEEE International Conference on Signal Processing and Communications (SPCOM

F1/10: An Open-Source Autonomous Cyber-Physical Platform

In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of safety and performance on real vehicles is costly and hazardous. The use of such vehicles is also outside the reach of most researchers and students. In this paper, we present F1/10: an open-source, affordable, and high-performance 1/10 scale autonomous vehicle testbed. The F1/10 testbed carries a full suite of sensors, perception, planning, control, and networking software stacks that are similar to full scale solutions. We demonstrate key examples of the research enabled by the F1/10 testbed, and how the platform can be used to augment research and education in autonomous systems, making autonomy more accessible