A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning

Buffer overflow is one of the worst program vulnerabilities. Many preventive approaches are applied to mitigate buffer overflow (BOF) vulnerabilities. However, BOF vulnerabilities are still being discovered in programs on a daily basis which might be exploited to crash programs and execute unwanted code at runtime. Monitoring is a popular approach for detecting BOF attacks during program execution and can prevent the consequences of BOF vulnerability exploitations. However, there is no classification of the proposed approaches to understand their common characteristics, objectives, and limitations. In this paper, we classify the current BOF vulnerability monitoring approaches based on the following five characteristics: monitoring objective, program state utilization, implementation mechanism, environmental change, and attack response. The classification will enable researchers to differentiate among existing monitoring approaches. Moreover, it will provide a guideline to choose monitoring approaches suitable for their needs.

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

Buffer overflow (BOF) vulnerabilities when present in code can be exploited to violate security objectives such as availability, confidentiality and integrity. They make up substantial portion of input manipulation attacks due to their common presence and ease of exploitation. In this study, the authors propose a hybrid approach combining static and dynamic program analysis with machine learning to audit BOFs. Simple rules to generate test data is proposed to confirm some of the vulnerabilities through dynamic analysis. Confirmed cases can be fixed by developers without further verification. Statements whose vulnerability is not confirmed by dynamic analysis are predicted by mining static code attributes. In the authors’ evaluation using standard benchmarks, their best classifier achieved a recall over 93% and accuracy >94%. Dynamic analysis itself confirmed 34% of known vulnerabilities along with reporting six new bugs, thereby reducing by third, otherwise needed manual auditing effort.

Energy management of hybrid energy system sources based on machine learning classification algorithms

Buffer overflow is one of the most common types of software vulnerabilities. Various static analysis and dynamic testing techniques have been proposed to detect buffer overflow vulnerabilities. With automatic tool support, static buffer overflow detection technique has been widely used in academia and industry. However, it tends to report too many false positives fundamentally due to the lack of software execution information. Currently, static warnings can only be validated by manual inspection, which significantly limits the practicality of the static analysis. In this paper, we present BovInspector, a tool framework for automatic static buffer overflow warnings inspection and validated bugs repair. Given the program source code and static buffer overflow vulnerability warnings, BovInspector first performs warning reachability analysis. Then, BovInspector executes the source code symbolically under the guidance of reachable warnings. Each reachable warning is validated and classified by checking whether all the path conditions and the buffer overflow constraints can be satisfied simultaneously. For each validated true warning, BovInspector fix it with three predefined strategies. BovInspector is complementary to prior static buffer overflow discovery schemes. Experimental results on real open source programs show that BovInspector can automatically inspect on average of 74.9% of total warnings, and false warnings account for about 25% to 100% (on average of 59.9%) of the total inspected warnings. In addition, the automatically generated patches fix all target vulnerabilities. Further information regarding the implementation and experimental results of BovInspector is available at http://bovinspectortool.github.io/project/. And a short video for demonstrating the capabilities of BovInspector is now available at https://youtu.be/IMdcksROJDg.

In the process of weathering for a long time, due to the exchange of internal elements and environmental elements, the composition proportion of glass cultural relics changes, which affects the correct judgment of archeological workers on their categories. In this paper, by machine learning method, the random forest algorithm is used to find out the feature attributes with higher importance. Based on the results obtained by random forest, the decision tree algorithm is used to get the decision tree and the classification method of high-potassium glass and lead-barium glass. In addition, the clustering coefficients of high-potassium glass and lead-barium glass were calculated by clustering algorithm, and the number of subclasses of the two kinds of glass was calculated. Then the random forest and decision tree algorithms are used to get the classification methods of high-potassium glass subclass and lead-barium glass subclass.

While memory-safe and type-safe languages have been available for many years, the vast majority of software is still implemented in type-unsafe languages such as C/C++. Despite massive concerted efforts by software vendors such as Microsoft to eliminate buffer overflow vulnerabilities through automated and manual code review, they continue to be found and exploited. We present a novel approach that accepts the existence of overflow vulnerabilities and uses parallelism available through current and future multi-core architectures to detect vulnerabilities by monitoring the parallel execution of several slightly varying instances of the same application. During regular execution each instance performs equivalent computations. When an attacker attempts to inject an attack vector through a buffer overflow vulnerability, however, each instance reacts differently due to the variances we introduced into each instance. We describe our prototype implementation of such a parallelism-based buffer overflow detection system and demonstrate that it is capable of stopping buffer overflow vulnerabilities using actual exploit codes for the popular Apache Web server. The experimental results show that the runtime overhead of our parallel execution framework is less than 10% on average.

Buffer overflow vulnerabilities in the memory stack continue to pose serious threats to network and computer security. By exploiting these vulnerabilities, a malicious party can strategically overwrite the return address of a procedure call, obtain control of a system, and subsequently launch more virulent attacks. Software countermeasures for such intrusions entail modifications to applications, compilers, and operating systems. Despite the availability of these defenses, many systems remain vulnerable to buffer overflow attacks. We present a hardware-based solution that prevents buffer overflow attacks involving procedure return address corruption. We add a secure return address stack to the processor that provides built-in, dynamic protection against return address tampering without requiring any effort by users or application programmers. Also, the performance impact is negligible for most applications. Changes are not required of application source code, so both legacy and future software can enjoy the security benefits of this solution.

Mixture design optimization and machine learning-based prediction of Al-Mg alloy composite reinforced by Zn nanoparticles: A molecular dynamics study

With the continuous development of science and technology, the issue of learning copyright knowledge in music copyright education has become increasingly severe. In response to the problems highlighted above, this paper proposes a study on optimizing the effectiveness of copyright knowledge learning based on the random forest algorithm. By integrating music copyright education issues and three principles for constructing an evaluation system, this study establishes an evaluation indicator system for copyright knowledge learning effectiveness. Under the influence of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, the evaluation indicators are quantified. Based on this, the Random Forest algorithm is utilized to optimize the quantified data of each evaluation indicator, followed by an exploration of the optimization effects of the Random Forest algorithm. The optimization values of the four algorithms are 3.9365, 3.7213, 3.7323, and 3.7088, respectively. It can be concluded that the optimization effect of the RF (random forest) algorithm is higher than that of the DT (decision tree algorithm), LSTM (long short-term memory network), and LR (logistic regression), verifying the practical applicability and application value of the research scheme proposed in this paper, which has practical strategic significance for improving music copyright education.

The buffer overflow attack is the single most dominant and lethal form of security exploits as evidenced by recent worm outbreaks such as Code Red and SQL Slammer. In this paper, we propose a new architectural solution to detect and deter the buffer overflow exploit. The idea is that the buffer overflow attacks usually exhibit abnormal symptoms in the system. This kind of unusual behavior can be simply detected by checking the integrity of instruction and data references at runtime, avoiding the potential data or control corruptions made by such attacks. Both the hardware cost and the performance penalty of enforcing the integrity rules are negligible. By performing detailed execution-driven simulations on the programs selected from SPEC CPU2000 benchmark, we evaluate the effectiveness of the proposed safety guards. By randomly corrupting stack and other data sections of a process’s address space during simulation, we create various buffer overflow scenarios, including both stack and heap smashing. Experimental data shows that enforcing two safety guards not only reduces the number of system failures substantially but it also circumvents virtually all forms of malicious code execution made by stack smashing or function pointer corruptions.KeywordsReturn AddressMalicious CodeBuffer OverflowData CorruptionBranch TargetThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

--Heart disease is one of the notable causes of death in worldwide everyday divination of heart disease is a critical challenge in area of medical data analysis. Machine learning (ML) has been shown to be successful in helping in making decision and prediction from huge quantity of data produced by medical health care industry. Medical data mining is an important area of data mining and considered as one of the important research field due to the application in medical health care domain. In medical data mining classification and prediction of medical dataset process challenges. It is difficult to medical practice to predict the heart attack as it complex task. The health sector at present contain the information that are hidden and which are important in making decision. Data mining algorithms such as Naive Bayes algorithm, decision tree algorithm and random forest are applied in the research for heart disease prediction the result show the comparison between the three algorithm and selecting the best one among three, the Random forest algorithm will provide the more accuracy can compared to all the three algorithms.

Buffer overflow vulnerabilities are currently the most prevalent security vulnerability. The paper presents a method that combines static analysis with dynamic test to deal with the problem on buffer overflow vulnerabilities detecting. By using the method we can identify potential weakness locations. A buffer overflow vulnerabilities testing system was developed. The experiment results tested and verified that the new methodology is feasibility and availability.

Either in terms of software error or being an attack itself, buffer overflow attacks have been one of the most important security problems accountable for a common vulnerability of software security and cyber risks. Buffer overflow vulnerabilities influence the zone of remote network penetration vulnerabilities, where an anonymous user tries to gain partial or complete control of the host. Buffer overflow attacks liable for some of the biggest data breaches in recent years. One of the notable data breach examples include Morris worm or internet worm of November 2, 1988. Morris worm is an ancient computer worm distributed via the internet, was noted for infecting around 6000 major Unix machines. This paper mainly focuses on buffer overflow attack vulnerabilities and the preventive measures to safeguard once system from being attacked. Keywords – Buffer overflow attack, susceptibility, impediment

The reactor core design involves the search for and detailed calculation of a large number of schemes. Four different machine learning algorithms were used in this technical note: the C4.5 algorithm (an algorithm of decision trees), Support Vector Machine, Random Forest, and Multi-layer Perceptron, respectively. Uranium enrichment, the number of fuel rods containing burnable poison, and the concentration of burnable poison were taken as independent variables in the calculation. The k-eff unevenness coefficient, the radial power nonuniformity coefficient, the radial flux nonuniformity coefficient, and the core life were taken as the number of core parameters fulfilled (CPF). Machine learning models were constructed through learning the training data set, which consisted of a large number of assembly and core schemes whose nuclear design parameters were already known. Using the models, the CPF values for the unknown core data set (the test data set) were quickly predicted. The results show that the cross-validation accuracy of each algorithm was above 94% and that the C4.5 algorithm had the highest accuracy for the overall prediction of the CPF values. For the CPF value prediction of the test data set, the time for the training data set was within 10s, while the Random Forest algorithm has the highest prediction accuracy for CPF = 4 or CPF ≠ 4.