Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

A new era of data access and management has begun with the use of cloud computing in the healthcare industry.Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity Operations.We use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional method.This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

Modified Elliptic Curve Cryptography Multi-Signature Scheme to Enhance Security in Cryptocurrency

Internet of Things(IoT)is an emerging technology that moves the world in the direction of smart things.But,IoT security is the complex problem due to its centralized architecture,and limited capacity.So,blockchain technology has great attention due to its features of decentralized architecture,transparency,immutable records and cryptography hash functions when combining with IoT.Cryptography hash algorithms are very important in blockchain technology for secure transmission.It converts the variable size inputs to a fixed size hash output which is unchangeable.Existing cryptography hash algorithms with digital signature have issues of single node accessibility and accessed up to 128 bytes of key size only.As well as,if the attacker tries to hack the key,it cancels the transaction.This paper presents the Modified Elliptic Curve Cryptography Multi Signature Scheme(MECC-MSS)for multiple node accessibility by finding nearest path for secure transaction.In this work,the input key size can be extended up to 512 bytes to enhance the security.The performance of the proposed algorithm is analyzed with other cryptography hash algorithms like Secure Hashing Algorithms(SHAs)such as SHA224,SHA256,SHA384,SHA512,SHA3-224,SHA3-256,SHA3-384,SHA3-512 and Message Digest5 by one-way analysis of variance test in terms of accuracy and time complexity.Results show that the MECC-MSS achieves 90.85%of accuracy and time complexity of 1.4 nano seconds with significance less than 0.05.From the statistical analysis,it is observed that the proposed algorithm is significantly better than other cryptography hash algorithms and also having less time complexity.

A network security situation prediction model based on wavelet neural network with optimized parameters

The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network （WNN） with optimized parameters by the Improved Niche Genetic Algorithm （INGA）. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm （GA） so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network （GA-WNN）. Genetic Algorithm-Back Propagation Neural Network （GA-BPNN） and WNN.

Secure Localization Algorithm Based on Node Cooperative for Sensor Networks

As to the safety threats faced by sensor networks （SN）, nodes limitations of computation, memory and communication, a secure location algorithm （node cooperative secure localization, NCSL） is presented in this paper. The algorithm takes the improvements of SN location information security as its design targets, utilizing nodes＇ cooperation to build virtual antennae array to communicate and localize, and gains arraying antenna advantage for SN without extra hardware cost, such as reducing multi-path effects, increasing receivers＇ signal to noise ratio and system capa- bility, reducing transmitting power, and so on. Simulations show that the algorithm based on virtual antennae array has good localization ability with a at high accuracy in direction-of-arrival （DOA） estimation, and makes SN capable to resist common malicious attacks, especially wormhole attack, by using the judgment rules for malicious attacks.

Forensics: Collection of Sound Digital Evidence

This summary paper will discuss the concept of forensic evidence and evidence collection methods. Emphasis will be placed on the techniques used to collect forensically sound digital evidence for the purpose of introduction to digital forensics. This discussion will thereafter result in identifying and categorizing the different types of digital forensics evidence and a clear procedure for how to collect forensically sound digital evidence. This paper will further discuss the creation of awareness and promote the idea that competent practice of computer forensics collection is important for admissibility in court.

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public net-work infrastructures that allow clients to access computational resources.Virtual Machines(VMs)are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious con-nections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence,the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position.Allocation of SECURITY Resources accepts a safety game in a simple format as input andfinds the best coverage vector for the opponent using a Stackelberg Equilibrium(SSE)technique.A Mixed Integer Linear Programming(MILP)framework is used in the algorithm.The VM challenge is reduced by afirewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The pro-posed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally,the experimental results indicate that the pro-posed security method is efficient.It consumes minimum execution time,better false positive rate,accuracy,and memory usage than the conventional approach.

基于FPGA集群的Office口令恢复优化实现

口令恢复是口令找回和电子取证的关键技术,而加密的Office文档被广泛使用,实现Office加密文档的有效恢复对信息安全具有重要的意义。口令恢复是计算密集型任务,需要硬件加速来实现恢复过程,传统的CPU和GPU受限于处理器结构,大大限制了口令验证速度的进一步提升。基于此,文中提出了基于FPGA集群的口令恢复系统。通过详细分析Office加密机制,给出了各版本Office的口令恢复流程。其次,在FPGA上以流水线结构优化了核心Hash算法,以LUT(Look Up Table)合并运算优化改进了AES(Advanced Encryption Standard)算法,以高速并行实现了口令生成算法。同时,以多算子并行设计了FPGA整体架构,实现了Office口令的快速恢复。最后,采用FPGA加速卡搭建集群,配合动态口令切分策略,充分发掘了FPGA低功耗高性能的计算特性。实验结果表明,无论在计算速度还是能效比上,优化后的FPGA加速卡都是GPU的2倍以上,具有明显的优势,非常适合大规模部署于云端,以缩短恢复时间找回口令。

A Survey of Bitmap Index Compression Algorithms for Big Data

With the growing popularity of Internet applications and the widespread use of mobile Internet, Internet traffic has maintained rapid growth over the past two decades. Internet Traffic Archival Systems（ITAS） for packets or flow records have become more and more widely used in network monitoring, network troubleshooting, and user behavior and experience analysis. Among the three key technologies in ITAS, we focus on bitmap index compression algorithm and give a detailed survey in this paper. The current state-of-the-art bitmap index encoding schemes include： BBC, WAH, PLWAH, EWAH, PWAH, CONCISE, COMPAX, VLC, DF-WAH, and VAL-WAH. Based on differences in segmentation, chunking, merge compress, and Near Identical（NI） features, we provide a thorough categorization of the state-of-the-art bitmap index compression algorithms. We also propose some new bitmap index encoding algorithms, such as SECOMPAX, ICX, MASC, and PLWAH＋, and present the state diagrams for their encoding algorithms. We then evaluate their CPU and GPU implementations with a real Internet trace from CAIDA. Finally, we summarize and discuss the future direction of bitmap index compression algorithms. Beyond the application in network security and network forensic, bitmap index compression with faster bitwise-logical operations and reduced search space is widely used in analysis in genome data, geographical information system, graph databases, image retrieval, Internet of things, etc. It is expected that bitmap index compression will thrive and be prosperous again in Big Data era since 1980s.