Hybrid email spam detection model with negative selection algorithm and differential evolution

Idris, Ismaila; Selamat, Ali; Omatu, Sigeru

doi:10.1016/j.engappai.2013.12.001

Cited by 71 publications

(28 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Its highly distributed, adaptive, and self-organizing nature, together with its learning, memory, feature extraction, and pattern recognition features offer rich metaphors for its artificial counterpart [9]. AIS has been applied in many research areas; because of the natural similarity of responding to nonself, AIS can solve many computer security problems [10,11], such as malware detection [12], intrusion detection [13], and spam detection [14]. Four major AIS algorithms have been constantly developed and gained popularity: (1) negative selection algorithm (NSA); (2) artificial immune network; (3) clonal selection algorithm; and (4) the danger theory and dendritic cell algorithm.…”

Section: Artificial Immunementioning

confidence: 99%

A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

Zhang

2018

Security and Communication Networks

View full text Add to dashboard Cite

Shellcodes are machine language codes injected into target programs in the form of network packets or malformed files. Shellcodes can trigger buffer overflow vulnerability and execute malicious instructions. Signature matching technology used by antivirus software or intrusion detection system has low detection rate for unknown or polymorphic shellcodes; to solve such problem, an immune-inspired shellcode detection algorithm was proposed, named ISDA. Static analysis and dynamic analysis were both applied. The shellcodes were disassembled to assembly instructions during static analysis and, for dynamic analysis, the API function sequences of shellcodes were obtained by simulation execution to get the behavioral features of polymorphic shellcodes. The extracted features of shellcodes were encoded to antigens based on -gram model. Immature detectors become mature after immune tolerance based on negative selection algorithm. To improve nonself space coverage rate, the immune detectors were encoded to hyperellipsoids. To generate better antibody offspring, the detectors were optimized through clonal selection algorithm with genetic mutation. Finally, shellcode samples were collected and tested, and result shows that the proposed method has higher detection accuracy for both nonencoded and polymorphic shellcodes.

show abstract

Section: Artificial Immunementioning

confidence: 99%

A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

Zhang

2018

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Despite legal laws, the SMS spam problem is increasing day by day. To solve this issue, an increasing number of methods, which can be classified into blacklisting, statistical methods that are based on the frequency of occurrence of words or characters and machine learning methods, have been proposed . Although the source of an SMS spam may be easily searched or detected and their content are generally about advertisement, making money, adult products, or losing weight, an efficient solution has not been proposed yet .…”

Section: Introductionmentioning

confidence: 99%

A novel feature extraction approach in SMS spam filtering for mobile communication: one‐dimensional ternary patterns

Kaya

Ertuğrul

2016

Security Comm Networks

View full text Add to dashboard Cite

The importance and utilization of mobile communication are increasing day by day, and the short message service (SMS) is one of them. Although SMS is a widely used communication way, it brings together a major problem, which is SMS spam messages. SMS spams do not only use vain in the mobile communication traffic but also disturb users. Based on this fact, blacklisting methods, statistical methods which are built on the frequency of occurrence of words or characters, and machine learning methods have been employed. Because punishments and legal laws are not enough to solve this problem and the Group Special Mobile number of SMS spam can easily be changed, a content-based approach must be proposed. Content-based methods showed high success in spam e-mail filtering, but it is hard in the SMS spam filtering because SMS messages are extremely short and generally contains many abbreviations. In this study, an image processing method, local ternary pattern was improved to extract features from SMS messages in the feature extraction stage. In the proposed one-dimensional ternary patterns, firstly, text message was converted to their UTF-8 values. Later, each character (its UTF-8 value) in the message was compared with its neighbors. Two different feature sets were extracted from the results of these comparisons. Finally, some machine learning methods were employed to classify these features. In order to validate the proposed approach, three different SMS corpora were used. The achieved accuracies and other employee performance measures showed that the proposed approach, one-dimensional ternary patterns, can be effectively employed in SMS spam filtering.

show abstract

“…boosting, ensembles and hybrid classifiers) [19][20][21]. One approach has not been fully explored in e-mail spam filtering, which is to combine classifiers.…”

Section: Spam Classifiersmentioning

confidence: 99%

“…number of tokens ending with {.net, .com, .jo, etc.}. X 18 Count of emoticons X 19 Count of images/image links X 20 Count of HTML tags X 21 Count of lines…”

Section: Readability Featuresmentioning

confidence: 99%

Voting-based Classification for E-mail Spam Detection

et al. 2016

View full text Add to dashboard Cite

Abstract. The problem of spam e-mail has gained a tremendous amount of attention. Although entities tend to use e-mail spam filter applications to filter out received spam e-mails, marketing companies still tend to send unsolicited emails in bulk and users still receive a reasonable amount of spam e-mail despite those filtering applications. This work proposes a new method for classifying emails into spam and non-spam. First, several e-mail content features are extracted and then those features are used for classifying each e-mail individually. The classification results of three different classifiers (i.e. Decision Trees, Random Forests and k-Nearest Neighbor) are combined in various voting schemes (i.e. majority vote, average probability, product of probabilities, minimum probability and maximum probability) for making the final decision. To validate our method, two different spam e-mail collections were used.

show abstract

Hybrid email spam detection model with negative selection algorithm and differential evolution

Cited by 71 publications

References 30 publications

A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

A novel feature extraction approach in SMS spam filtering for mobile communication: one‐dimensional ternary patterns

Voting-based Classification for E-mail Spam Detection

Contact Info

Product

Resources

About