Artificial intelligence and computer vision advancements have revolutionized computer-aided diagnosis (CAD) systems, enabling more accurate breast cancer (BrCan) detection using histopathology images. This study proposes a classification framework that integrates Vision Transformers (ViT), Graph Attention Networks (GAT), and Calibrated Random Forest (CRF) to enhance diagnostic accuracy. ViT effectively captures rich visual representations and helps to form a graph-like structure, while GAT models the structural relationships within histopathology images, providing a more comprehensive understanding of tissue morphology. Extensive experiments were conducted with various model combinations, demonstrating that the ViT + GAT + CRF architecture achieved the highest performance. The experiment is carried out on the BreakHis dataset, and the model acquires an accuracy of 97.33%. These results highlight the effectiveness of incorporating both visual and structural features to improve diagnostic reliability. Our proposed framework represents a significant advancement in digital histopathology-based (BrCan) diagnosis and holds promise for broader applications in medical imaging.

Fuzzing is a popular and highly effective technique for software testing especially vulnerability detection. Fuzzing includes the random mutation of well-formed program inputs using dynamic program analysis. Though fuzzing is an active area of research, less systematic efforts have been investigated to understand as well as to generate powerful input seeds for a fuzzer. Reactive systems are used in different applications such as web services, decision support systems, and logical controllers. These systems are quite complex and bigger, hence the validation process becomes tedious. In this work, we propose a static seed generator that helps to accelerate the performance of existing fuzzers. In this paper, we validate the reactive systems using our approach by detecting vulnerability. To evaluate the performance of our developed seeder, we experimented with 100 Rigorous Ex-amination of Reactive Systems (RERS) C-programs. Experimental results show that our approach SSG-AFL is superior as compared to the AFL with random seeds. SSG-AFL shows 59.75% winning programs after running all four phases as compared to Random-AFL.

In today’s world, all things are connected and influencing the existing applications. The E-Health domain extensively adopts IoT and presents new healthcare services and medical facilities. However, the major hurdle is improving security and preserving the patients’ privacy. Many security/privacy-preserving models and protocols are proposed but prone to adversarial attacks. Our objective is to improve their security and ensure lightweight complexity for the low-powered and limited memory device. In this paper, we improve security using four critical steps. The effectivity of random number generation is improved, which defines the current security level in cryptography. A new technique is proposed utilising timestamp for handling a replay attack. We ensure strong forward security using the Elliptic Curve Discrete Logarithm Problem (ECDLP), making it challenging for an adversary to decode the security parameters. Finally, it is ensured that the hash function’s bits maintain the entropy of the key involved in the security model. Thus, the proposed model preserves privacy as well as improves the security of the E-Health model.

In this technical world, the detection of malware variants is getting cumbersome day by day.Newer variants of malware make it even tougher to detect them.The enormous amount of diversified malware enforced us to stumble on new techniques like machine learning.In this work, we propose an incremental malware detection model for meta-feature API and system call sequence.We represent the host behaviour using a sequence of API calls and system calls.For the creation of sequential system calls, we use NITRSCT (NITR System call Tracer) and for sequential API calls, we generate a list of anomaly scores for each API call sequence using Numenta Hierarchical Temporal Memory (N-HTM).We have converted the API call sequence into six meta-features that narrates its influence.We do the feature selection using a correlation matrix with a heatmap to select the best meta-features.An incremental malware detection model is proposed to decide the label of the binary executable under study.We classify malware samples into their respective types and demonstrated via a case study that, our proposed model can reduce the effort required in STS-Tool(Socio-Technical Security Tool) approach and Abuse case.Theoretical analysis and real-life experiments show that our model is efficient and achieves 95.2% accuracy.The detection speed of our proposed model is 0.03s.We resolve the issue of limited precision and recall while detecting malware.User's requirement is also met by fixing the trade-off between accuracy and speed.

Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyberworld. Attackers take advantage of the dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that are needed for creating the dataset. We compute Pearson's r between every feature for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

Summary This paper presents a context‐sensitive dynamic slicing technique for the concurrent and aspectized programs. To effectively represent the concurrent aspect‐oriented programs, we propose an intermediate graph called the multithreaded aspect‐oriented dependence graph (MAODG). The MAODG is a dynamic graph generated from the execution trace of a given program with respect to a particular set of values given as an input. Interference dependencies between the statements are shown by a distinguished edge called the interference dependence edge in the MAODG. Based on this intermediate representation, we propose a precise and accurate dynamic slicing algorithm for the concurrent aspect‐oriented programs implemented using AspectJ. The proposed dynamic slicing algorithm is implemented in a slicing tool developed using the ASM framework. Several open source programs are studied and evaluated using the proposed technique along with some existing techniques. The experimentation shows that our proposed slicing algorithm generates slices of the same or smaller size, as compared with the existing algorithms. Furthermore, we found that the slice computation time is significantly less in our proposed algorithm, as compared with the existing algorithms.

Distributed concolic testing (DCT) for complex programs takes a remarkable computational time. Also, the achieved modified condition/decision coverage (MC/DC) for such programs is often inadequate. We propose an improved DCT approach that reduces the computational time and simultaneously enhanced the MC/DC. We have named our approach SMCDCT (scalable MC/DC percentage calculator using DCT). Our experimental study on forty-five C programs indicates 6.62% of average increase in MC/DC coverage. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Software testers always face the dilemma of whether to retest the software with all the test cases or select a few of them on the basis of their fault detection ability. This paper introduces a novel approach to minimizing the test suite as an integer linear programming problem with optimal results. The minimization method uses the cohesion values of the program parts affected by the changes made to the program. The hypothesis is that the program parts with low cohesion values are more prone to errors. This assumption is validated on the mutation fault detection ability of the test cases. The experimental study carried out on 30 programs evaluates the effectiveness and usefulness of the proposed framework. The experimental results show that the minimized test suite can efficiently reveal the errors and ensure acceptable software quality. Copyright © 2017 John Wiley & Sons, Ltd.

Modified Condition / Decision Coverage (MC/DC) is the second strongest coverage criterion in white-box testing.According to DO178C/RTCA criterion it is mandatory to achieve Level A certification for MC/DC.Concolic testing is the combination of Concrete and Symbolic execution.It is a systematic technique that performs symbolic execution but uses randomlygenerated test inputs to initialize the search and to allow the tool to execute programs when symbolic execution fails.In this paper, we extend concolic testing by computing MC/DC using the automatically generated test cases.On the other hand Feedback-Directed Random Test Generation builds inputs incrementally by randomly selecting a method call to apply and find arguments from among previously-constructed inputs.As soon as the input is built, it is executed and checked against a set of contracts and filters.In our proposed work, we combine feedback-directed test cases generation with concolic testing to form Java-Hybrid Concolic Testing (Java-HCT).Java-HCT generates more number of test cases since it combines the features of both Feedback-Directed Random Test and Concolic Testing.Hence, through Java-HCT, we achieve high MC/DC.Combinations of approaches represent different tradeoffs of completeness and scalability.We develop Java-HCT using RANDOOP, jCUTE, and COPECA.Combination of RANDOOP and jCUTE creates more test cases.COPECA is used to measure MC/DC% using the generated test cases.Experimental study shows that Java-HCT produces better MC/DC% than individual testing techniques(feedback-directed random testing and concolic testing).We have improved MC/DC by ×1.62 and by ×1.26 for feedback-directed random testing and concolic testing respectively.

Program slicing has numerous applications in software engineering activities like debugging, testing, maintenance, model checking etc. The main objective of this paper is to automate the generation of System Dependency Graphs (SDG) for aspect-oriented programs to efficiently compute accurate slices. The construction of SDG is automated by analysing the byte code of aspect-oriented programs that incorporates the representation of aspect-oriented features. After constructing the SDG, we propose a slicing algorithm that uses the intermediate graph and computes slices for a given AOP. To implement our proposed slicing technique, we have developed a prototype tool that takes an AOP as input and compute its slices using our proposed slicing algorithm. To evaluate our proposed technique, we have considered some case studies by taking open source projects. The comparative study of our proposed slicing algorithm with some existing algorithms show that our approach is an efficient and scalable approach of slicing for different applications with respect to aspect-oriented programs.

This paper presents a context-sensitive dynamic slicing technique for concurrent AOPs having multiple threads. To effectively represent the concurrent AOP, we propose an intermediate graph called Multithreaded Aspect-Oriented Dependence Graph (MAODG). Based on this intermediate representation, we design a precise and accurate dynamic slicing algorithm for concurrent AOPs. This algorithm takes the MAODG of the concurrent AOP and a slicing criterion as input and computes the dynamic slice for the given concurrent AOP.

We propose a new dynamic slicing technique for object-oriented programs that is more efficient than the related algorithms. We use an extended system dependence graph (ESDG) as the intermediate program representation. Our dynamic slicing algorithm is based on marking and unmarking the edges in the ESDG as and when dependencies arise and cease during runtime.